From 1ea69e5cefa4a66209cac743cecf126092b3b6a3 Mon Sep 17 00:00:00 2001 From: rafael Date: Thu, 15 Jun 2023 16:44:04 +0200 Subject: [PATCH 01/24] update dask --- dask/01-dask-intro.ipynb | 4 +- ...cityblock-distance-matrix-scipy.dask.ipynb | 4 +- ...cityblock-distance-matrix-scipy.dask.ipynb | 4 +- dask/03-dask-arrays.ipynb | 4 +- dask/04-dask-arrays-from-file.ipynb | 46 ++++++------------- ...ibuted.ipynb => 05-dask-distributed.ipynb} | 29 ++++++------ dask/06-dask-processes-vs-threads.ipynb | 4 +- 7 files changed, 36 insertions(+), 59 deletions(-) rename dask/{05-dask_distributed.ipynb => 05-dask-distributed.ipynb} (90%) diff --git a/dask/01-dask-intro.ipynb b/dask/01-dask-intro.ipynb index ec82127..5ce0766 100644 --- a/dask/01-dask-intro.ipynb +++ b/dask/01-dask-intro.ipynb @@ -208,9 +208,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/dask/02-exercise-cityblock-distance-matrix-scipy.dask.ipynb b/dask/02-exercise-cityblock-distance-matrix-scipy.dask.ipynb index 3ad5d09..d1dce66 100644 --- a/dask/02-exercise-cityblock-distance-matrix-scipy.dask.ipynb +++ b/dask/02-exercise-cityblock-distance-matrix-scipy.dask.ipynb @@ -202,9 +202,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/dask/02-solution-cityblock-distance-matrix-scipy.dask.ipynb b/dask/02-solution-cityblock-distance-matrix-scipy.dask.ipynb index 7b7d01a..aae8472 100644 --- a/dask/02-solution-cityblock-distance-matrix-scipy.dask.ipynb +++ b/dask/02-solution-cityblock-distance-matrix-scipy.dask.ipynb @@ -214,9 +214,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/dask/03-dask-arrays.ipynb b/dask/03-dask-arrays.ipynb index 185fb7d..983a76f 100644 --- a/dask/03-dask-arrays.ipynb +++ b/dask/03-dask-arrays.ipynb @@ -165,9 +165,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/dask/04-dask-arrays-from-file.ipynb b/dask/04-dask-arrays-from-file.ipynb index f963991..041c73f 100644 --- a/dask/04-dask-arrays-from-file.ipynb +++ b/dask/04-dask-arrays-from-file.ipynb @@ -36,36 +36,16 @@ "metadata": {}, "outputs": [], "source": [ - "filenames = ['2014-01-01.hdf5', \n", - " '2014-01-02.hdf5',\n", - " '2014-01-03.hdf5',\n", - " '2014-01-04.hdf5',\n", - " '2014-01-05.hdf5',\n", - " '2014-01-06.hdf5',\n", - " '2014-01-07.hdf5',\n", - " '2014-01-08.hdf5',\n", - " '2014-01-09.hdf5',\n", - " '2014-01-10.hdf5',\n", - " '2014-01-11.hdf5',\n", - " '2014-01-12.hdf5',\n", - " '2014-01-13.hdf5',\n", - " '2014-01-14.hdf5',\n", - " '2014-01-15.hdf5',\n", - " '2014-01-16.hdf5',\n", - " '2014-01-17.hdf5',\n", - " '2014-01-18.hdf5',\n", - " '2014-01-19.hdf5',\n", - " '2014-01-20.hdf5',\n", - " '2014-01-21.hdf5',\n", - " '2014-01-22.hdf5',\n", - " '2014-01-23.hdf5',\n", - " '2014-01-24.hdf5',\n", - " '2014-01-25.hdf5',\n", - " '2014-01-26.hdf5',\n", - " '2014-01-27.hdf5',\n", - " '2014-01-28.hdf5',\n", - " '2014-01-29.hdf5',\n", - " '2014-01-30.hdf5',\n", + "filenames = ['2014-01-01.hdf5', '2014-01-02.hdf5', '2014-01-03.hdf5',\n", + " '2014-01-04.hdf5', '2014-01-05.hdf5', '2014-01-06.hdf5',\n", + " '2014-01-07.hdf5', '2014-01-08.hdf5', '2014-01-09.hdf5',\n", + " '2014-01-10.hdf5', '2014-01-11.hdf5', '2014-01-12.hdf5',\n", + " '2014-01-13.hdf5', '2014-01-14.hdf5', '2014-01-15.hdf5',\n", + " '2014-01-16.hdf5', '2014-01-17.hdf5', '2014-01-18.hdf5',\n", + " '2014-01-19.hdf5', '2014-01-20.hdf5', '2014-01-21.hdf5',\n", + " '2014-01-22.hdf5', '2014-01-23.hdf5', '2014-01-24.hdf5',\n", + " '2014-01-25.hdf5', '2014-01-26.hdf5', '2014-01-27.hdf5',\n", + " '2014-01-28.hdf5', '2014-01-29.hdf5', '2014-01-30.hdf5',\n", " '2014-01-31.hdf5']" ] }, @@ -82,7 +62,7 @@ "metadata": {}, "outputs": [], "source": [ - "datadir = os.path.join('/scratch/snx3000/class401/weather-big')\n", + "datadir = os.path.join('/scratch/snx3000/class471/weather-big')\n", "dsets = [\n", " h5py.File(os.path.join(datadir, filename), mode='r')['/t2m']\n", " for filename in filenames\n", @@ -234,9 +214,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/dask/05-dask_distributed.ipynb b/dask/05-dask-distributed.ipynb similarity index 90% rename from dask/05-dask_distributed.ipynb rename to dask/05-dask-distributed.ipynb index 56b22f6..33ed452 100644 --- a/dask/05-dask_distributed.ipynb +++ b/dask/05-dask-distributed.ipynb @@ -34,19 +34,6 @@ "%ipcluster start -n 12 --dask" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce47165d-38b4-40ce-9a3e-3307dfa4ff88", - "metadata": {}, - "outputs": [], - "source": [ - "# connect to cluster\n", - "from dask.distributed import Client\n", - "client = Client(scheduler_file='/tmp/tmpxlmqg20v')\n", - "client" - ] - }, { "cell_type": "code", "execution_count": null, @@ -54,7 +41,7 @@ "metadata": {}, "outputs": [], "source": [ - "N = 500_000\n", + "N = 50_000\n", "x = da.random.random((N, N)) #, chunks=(N / 500, 1000))\n", "x" ] @@ -93,6 +80,16 @@ "socket.gethostname()" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "d90bcd04-333d-453f-a30e-68b7bf10873b", + "metadata": {}, + "outputs": [], + "source": [ + "client.close()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -106,9 +103,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/dask/06-dask-processes-vs-threads.ipynb b/dask/06-dask-processes-vs-threads.ipynb index c06451c..c065440 100644 --- a/dask/06-dask-processes-vs-threads.ipynb +++ b/dask/06-dask-processes-vs-threads.ipynb @@ -99,9 +99,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { From 522281f262415486694c94c2f4a0140cf266baf6 Mon Sep 17 00:00:00 2001 From: rafael Date: Thu, 15 Jun 2023 16:52:55 +0200 Subject: [PATCH 02/24] update locks --- intro/locks-example-nolock.ipynb | 12 ++++++------ intro/locks-example.ipynb | 12 ++++++------ 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/intro/locks-example-nolock.ipynb b/intro/locks-example-nolock.ipynb index 1313c6a..01ba1c3 100644 --- a/intro/locks-example-nolock.ipynb +++ b/intro/locks-example-nolock.ipynb @@ -17,18 +17,18 @@ "outputs": [], "source": [ "def function1():\n", - " c = '\\033[91m' # red\n", + " color = '\\033[91m' # red\n", " for i in range(10):\n", " for word in ['Hello', 'from', 'the', 'first', 'function\\n']:\n", " time.sleep(1e-5)\n", - " print(f' {c}{word}', end='')\n", + " print(f' {color}{word}', end='')\n", "\n", "def function2():\n", - " c = '\\033[92m' # green\n", + " color = '\\033[92m' # green\n", " for i in range(10):\n", " for word in ['Hello', 'from', 'the', 'second', 'function\\n']:\n", " time.sleep(1e-5)\n", - " print(f' {c}{word}', end='')" + " print(f' {color}{word}', end='')" ] }, { @@ -50,9 +50,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/intro/locks-example.ipynb b/intro/locks-example.ipynb index 593c56d..61098a2 100644 --- a/intro/locks-example.ipynb +++ b/intro/locks-example.ipynb @@ -26,22 +26,22 @@ "outputs": [], "source": [ "def function1():\n", - " c = '\\033[91m' # red\n", + " color = '\\033[91m' # red\n", " for i in range(10):\n", " lock.acquire ()\n", " for word in ['Hello', 'from', 'the', 'first', 'function\\n']:\n", " time.sleep(1e-5)\n", - " print(f' {c}{word}', end='')\n", + " print(f' {color}{word}', end='')\n", " \n", " lock.release ()\n", "\n", "def function2():\n", - " c = '\\033[92m' # green\n", + " color = '\\033[92m' # green\n", " for i in range(10):\n", " lock.acquire ()\n", " for word in ['Hello', 'from', 'the', 'second', 'function\\n']:\n", " time.sleep(1e-5)\n", - " print(f' {c}{word}', end='')\n", + " print(f' {color}{word}', end='')\n", " \n", " lock.release ()" ] @@ -65,9 +65,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { From 889f963d626a9a5201f5109c60909e8d687022bb Mon Sep 17 00:00:00 2001 From: rafael Date: Sun, 18 Jun 2023 12:03:49 +0200 Subject: [PATCH 03/24] update locks --- intro/locks-example-nolock.ipynb | 31 ++++++++++++++++++------------ intro/locks-example.ipynb | 33 ++++++++++++++++++-------------- 2 files changed, 38 insertions(+), 26 deletions(-) diff --git a/intro/locks-example-nolock.ipynb b/intro/locks-example-nolock.ipynb index 01ba1c3..13ef9af 100644 --- a/intro/locks-example-nolock.ipynb +++ b/intro/locks-example-nolock.ipynb @@ -16,19 +16,26 @@ "metadata": {}, "outputs": [], "source": [ - "def function1():\n", - " color = '\\033[91m' # red\n", + "def print_word_by_word(string, color=''):\n", + " for word in string.split(' '):\n", + " time.sleep(1e-5)\n", + " print(f' {color}{word}', end='')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def function_1():\n", " for i in range(10):\n", - " for word in ['Hello', 'from', 'the', 'first', 'function\\n']:\n", - " time.sleep(1e-5)\n", - " print(f' {color}{word}', end='')\n", + " print_word_by_word('Hello from the first function\\n', color='\\033[91m')\n", + "\n", "\n", - "def function2():\n", - " color = '\\033[92m' # green\n", + "def function_2():\n", " for i in range(10):\n", - " for word in ['Hello', 'from', 'the', 'second', 'function\\n']:\n", - " time.sleep(1e-5)\n", - " print(f' {color}{word}', end='')" + " print_word_by_word('Hello from the second function\\n', color='\\033[92m')" ] }, { @@ -37,8 +44,8 @@ "metadata": {}, "outputs": [], "source": [ - "thread_1 = threading.Thread(target=function1)\n", - "thread_2 = threading.Thread(target=function2)\n", + "thread_1 = threading.Thread(target=function_1)\n", + "thread_2 = threading.Thread(target=function_2)\n", "\n", "thread_1.start()\n", "thread_2.start()\n", diff --git a/intro/locks-example.ipynb b/intro/locks-example.ipynb index 61098a2..c76eb5e 100644 --- a/intro/locks-example.ipynb +++ b/intro/locks-example.ipynb @@ -10,6 +10,18 @@ "import threading" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def print_word_by_word(string, color=''):\n", + " for word in string.split(' '):\n", + " time.sleep(1e-5)\n", + " print(f' {color}{word}', end='')" + ] + }, { "cell_type": "code", "execution_count": null, @@ -25,24 +37,17 @@ "metadata": {}, "outputs": [], "source": [ - "def function1():\n", - " color = '\\033[91m' # red\n", + "def function_1():\n", " for i in range(10):\n", " lock.acquire ()\n", - " for word in ['Hello', 'from', 'the', 'first', 'function\\n']:\n", - " time.sleep(1e-5)\n", - " print(f' {color}{word}', end='')\n", - " \n", + " print_word_by_word('Hello from the first function\\n', color='\\033[91m')\n", " lock.release ()\n", "\n", - "def function2():\n", - " color = '\\033[92m' # green\n", + "\n", + "def function_2():\n", " for i in range(10):\n", " lock.acquire ()\n", - " for word in ['Hello', 'from', 'the', 'second', 'function\\n']:\n", - " time.sleep(1e-5)\n", - " print(f' {color}{word}', end='')\n", - " \n", + " print_word_by_word('Hello from the second function\\n', color='\\033[92m')\n", " lock.release ()" ] }, @@ -52,8 +57,8 @@ "metadata": {}, "outputs": [], "source": [ - "thread_1 = threading.Thread(target=function1)\n", - "thread_2 = threading.Thread(target=function2)\n", + "thread_1 = threading.Thread(target=function_1)\n", + "thread_2 = threading.Thread(target=function_2)\n", "\n", "thread_1.start()\n", "thread_2.start()\n", From 91048bbc2e11f237972f49126e9d99e1559774a4 Mon Sep 17 00:00:00 2001 From: rafael Date: Sun, 18 Jun 2023 12:26:49 +0200 Subject: [PATCH 04/24] update ipcmagic --- ipcmagic/01-osu_bw_mpi4py-cupy.ipynb | 37 ++++++++++++++++++++++------ 1 file changed, 29 insertions(+), 8 deletions(-) diff --git a/ipcmagic/01-osu_bw_mpi4py-cupy.ipynb b/ipcmagic/01-osu_bw_mpi4py-cupy.ipynb index 9f4ea90..5a329c1 100644 --- a/ipcmagic/01-osu_bw_mpi4py-cupy.ipynb +++ b/ipcmagic/01-osu_bw_mpi4py-cupy.ipynb @@ -7,9 +7,31 @@ "tags": [] }, "source": [ - "# OSU G2G Bandwidth benchmark with MPI4Py\n", - "In this example we use IPCMagic to run a test from the [OSU Bandwidth benchmark](http://mvapich.cse.ohio-state.edu/benchmarks/) with MPI4Py from a Jupyter notebook.\n", - "Using [this example](https://mpi4py.readthedocs.io/en/stable/tutorial.html#cuda-aware-mpi-python-gpu-arrays), we adapt the [osu_bw.py](https://github.com/mpi4py/mpi4py/blob/d0228f0397403ff73d8f41d90d97b411efda6128/demo/osu_bw.py) script from the MPI4Py repository so it uses an array allocated on the GPU." + "## OSU G2G Bandwidth Benchmark with MPI4Py\n", + "In this example we use [IPCMagic](https://github.com/eth-cscs/ipcluster_magic/tree/master) to run a test from the [OSU Bandwidth benchmark](http://mvapich.cse.ohio-state.edu/benchmarks/) with MPI4Py from a Jupyter notebook.\n", + "Using [this example](https://mpi4py.readthedocs.io/en/stable/tutorial.html#cuda-aware-mpi-python-gpu-arrays), we adapted the [osu_bw.py](https://github.com/mpi4py/mpi4py/blob/d0228f0397403ff73d8f41d90d97b411efda6128/demo/osu_bw.py) script from the MPI4Py repository so it uses an array allocated on the GPU.\n", + "\n", + "* From a shell in Piz Daint this can be run using this Slurm job script:\n", + " \n", + "```\n", + "#!/bin/bash -l\n", + "\n", + "#SBATCH --job-name=osubw\n", + "#SBATCH --time=00:05:00\n", + "#SBATCH --nodes=2\n", + "#SBATCH --ntasks-per-core=1\n", + "#SBATCH --ntasks-per-node=1\n", + "#SBATCH --cpus-per-task=12\n", + "#SBATCH --partition=normal\n", + "#SBATCH --constraint=gpu\n", + "#SBATCH --account=\n", + "\n", + "# source python environment with cupy and mpi4py\n", + "\n", + "export MPICH_RDMA_ENABLED_CUDA=1\n", + "\n", + "srun python osu_bw_cupy.py\n", + "```" ] }, { @@ -30,7 +52,7 @@ "metadata": {}, "outputs": [], "source": [ - "os.environ['MPICH_RDMA_ENABLED_CUDA'] = '1'" + "os.environ['MPICH_RDMA_ENABLED_CUDA'] = '1' # Enable direct communication between GPUs" ] }, { @@ -60,8 +82,7 @@ "metadata": {}, "outputs": [], "source": [ - "# IPyParallel shows a progress bar if cells take time executing.\n", - "# That can be disabled with by passing `--progress-after -1` to `%%px`.\n", + "# Disable IPyParallel's progress bar\n", "%pxconfig --progress-after -1" ] }, @@ -113,9 +134,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { From a90abc23df3129875e189b55e80b1c2c6701bbce Mon Sep 17 00:00:00 2001 From: rafael Date: Sun, 18 Jun 2023 12:27:28 +0200 Subject: [PATCH 05/24] update ipcmagic --- ipcmagic/02-distributed-SGD.ipynb | 289 ------------------------------ 1 file changed, 289 deletions(-) delete mode 100644 ipcmagic/02-distributed-SGD.ipynb diff --git a/ipcmagic/02-distributed-SGD.ipynb b/ipcmagic/02-distributed-SGD.ipynb deleted file mode 100644 index 0a00d64..0000000 --- a/ipcmagic/02-distributed-SGD.ipynb +++ /dev/null @@ -1,289 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simple Stochastic Gradient Descent in Two Nodes with TensorFlow, Horovod and IPCMagic.\n", - "\n", - "Here we visualize the minimization of the loss with the SGD algorithm.\n", - "For this, we consider a linear model with only two weights (the slope and the offset).\n", - "\n", - "With this example we show how use [IPCMagic](https://github.com/eth-cscs/ipcluster_magic) to run TensorFlow in two nodes from a Jupyter notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipcmagic\n", - "from ipcmagic import utilities " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%ipcluster --version" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%ipcluster start -n 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# IPyParallel shows a progress bar if cells that take some time executing.\n", - "# That can be disabled with by passing `--progress-after -1` to `%%px`\n", - "%pxconfig --progress-after -1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "import socket\n", - "socket.gethostname()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "import numpy as np\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", - "import horovod.tensorflow.keras as hvd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "hvd.init()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px --target 0\n", - "tf.version.VERSION" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "# Create a linear function with noise as our data\n", - "nsamples = 1000\n", - "ref_slope = 2.0\n", - "ref_offset = 0.0\n", - "noise = np.random.random((nsamples, 1)) - 0.5 # -0.5 to center the noise\n", - "x_train = np.random.random((nsamples, 1)) - 0.5 # -0.5 to center x around 0\n", - "y_train = ref_slope * x_train + ref_offset + noise" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "dataset = tf.data.Dataset.from_tensor_slices((x_train.astype(np.float32),\n", - " y_train.astype(np.float32)))\n", - "dataset = dataset.shuffle(1000)\n", - "dataset = dataset.batch(100)\n", - "dataset = dataset.repeat(150)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "model = tf.keras.models.Sequential([\n", - " tf.keras.layers.Dense(1, input_shape=(1,), activation='linear'),\n", - "])\n", - "\n", - "opt = tf.keras.optimizers.SGD(lr=0.5)\n", - "opt = hvd.DistributedOptimizer(opt)\n", - "\n", - "model.compile(optimizer=opt,\n", - " loss='mse')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "class TrainHistory(tf.keras.callbacks.Callback):\n", - " def on_train_begin(self, logs={}):\n", - " self.vars = []\n", - " self.loss = []\n", - "\n", - " def on_batch_end(self, batch, logs={}):\n", - " self.vars.append([v.numpy()[0].flatten() for v in self.model.variables])\n", - " self.loss.append(logs.get('loss'))\n", - " \n", - "history = TrainHistory()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "initial_sync = hvd.callbacks.BroadcastGlobalVariablesCallback(0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "fit = model.fit(dataset, callbacks=[initial_sync, history])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px --target 0,1\n", - "slope_hist = np.array(history.vars)[:, 0]\n", - "offset_hist = np.array(history.vars)[:, 1]\n", - "loss_hist = np.array(history.loss)\n", - "\n", - "matplotlib.rcParams['figure.figsize'] = (10, 4)\n", - "\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(loss_hist[10:], 'r.-')\n", - "plt.xlabel('Training steps')\n", - "plt.ylabel('Loss')\n", - "plt.grid()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.plot(x_train, y_train, '.')\n", - "plt.plot(x_train, slope_hist[-1] * x_train + offset_hist[-1], 'r-')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.grid()\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "matplotlib.rcParams['figure.figsize'] = (6, 4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px --target 0,1\n", - "def loss_function_field(m, n, xref, yref):\n", - " '''Utility function for ploting the loss'''\n", - " return np.mean(np.square(yref - m * xref - n ))\n", - "\n", - "_m = np.arange(-0.0, 4.01, 0.1)\n", - "_n = np.arange(-0.5, 0.51, 0.1)\n", - "M, N = np.meshgrid(_m, _n)\n", - "\n", - "Z = np.zeros(M.shape)\n", - "for i in range(M.shape[0]):\n", - " for j in range(M.shape[1]):\n", - " Z[i, j] = loss_function_field(M[i, j], N[i, j],\n", - " x_train, y_train)\n", - "\n", - "matplotlib.rcParams['figure.figsize'] = (10, 7)\n", - "\n", - "cp = plt.contour(M, N, Z, 15, vmin=Z.min(), vmax=Z.max(), alpha=0.99, colors='k', linestyles='--')\n", - "plt.contourf(M, N, Z, vmin=Z.min(), vmax=Z.max(), alpha=0.8, cmap=plt.cm.RdYlBu_r)\n", - "plt.clabel(cp, cp.levels[:6])\n", - "plt.colorbar()\n", - "m = slope_hist[-1]\n", - "n = offset_hist[-1]\n", - "plt.plot(slope_hist, offset_hist, '.-', lw=2, c='k')\n", - "plt.plot([ref_slope], [ref_offset], 'rx', ms=10)\n", - "plt.xlim([_m.min(), _m.max()])\n", - "plt.ylim([_n.min(), _n.max()])\n", - "plt.xlabel('Slope')\n", - "plt.ylabel('Offset')\n", - "plt.show()\n", - "\n", - "matplotlib.rcParams['figure.figsize'] = (6, 4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%ipcluster stop" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "hpcpython2022-tf", - "language": "python", - "name": "hpcpython2022-tf" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 29201d235e3bc9339a4f02e570d59bf741d39f03 Mon Sep 17 00:00:00 2001 From: rafael Date: Sun, 18 Jun 2023 12:53:24 +0200 Subject: [PATCH 06/24] delete numexpr --- numexpr/01_exercise-edm-numpexpr.ipynb | 240 ------------------------- numexpr/01_solution-numpexpr.ipynb | 140 --------------- 2 files changed, 380 deletions(-) delete mode 100644 numexpr/01_exercise-edm-numpexpr.ipynb delete mode 100644 numexpr/01_solution-numpexpr.ipynb diff --git a/numexpr/01_exercise-edm-numpexpr.ipynb b/numexpr/01_exercise-edm-numpexpr.ipynb deleted file mode 100644 index 7526d6b..0000000 --- a/numexpr/01_exercise-edm-numpexpr.ipynb +++ /dev/null @@ -1,240 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Playing with `numpexpr`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import os\n", - "# os.environ['NUMEXPR_NUM_THREADS'] = '12'\n", - "import numpy as np\n", - "import numexpr as ne" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.random.random((5000, 50))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check out the difference in time of the next two numpy equivalent expressions. The main difference comes from the implementation of the array's `__paw__` method which tensds to be slower thatn the equivalent multiplications." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%timeit ((x + 1.) * x + 1.) * x + 1." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%timeit x**3 + x**2 + x + 1." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's do the same using `numexpr`. Check out the use of multiple threading with the `top` command!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%timeit ne.evaluate('((x + 1.) * x + 1.) * x + 1.')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%timeit ne.evaluate('x**3 + x**2 + x + 1.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's do other expressions. Notice that we replace `np.sin` for `sin` inside the expression." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%timeit np.sin(x) + np.cos(x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%timeit ne.evaluate('sin(x) + cos(x)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some operators can be used as well" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%timeit np.sin(x) + np.cos(x) > x" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%timeit ne.evaluate('sin(x) + cos(x) > x')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see the configuration of `numexpr` with the following " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ne.show_config()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `euclidean_trick` with numexpr\n", - "\n", - "Question Modify the `euclidean_trick_numexpr` function on the next cell to use `numexpr`. Time it and compare that the result is the same as `euclidean_trick`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def euclidean_trick_numexpr(x, y):\n", - " \"\"\"Euclidean square distance matrix.\n", - " \n", - " Inputs:\n", - " x: (N, m) numpy array\n", - " y: (N, m) numpy array\n", - " \n", - " Ouput:\n", - " (N, N) Euclidean square distance matrix:\n", - " r_ij = x_ij^2 - y_ij^2\n", - " \"\"\"\n", - " x2 = np.einsum('ij,ij->i', x, x)[:, np.newaxis]\n", - " y2 = np.einsum('ij,ij->i', y, y)[np.newaxis, :]\n", - "\n", - " xy = np.dot(x, y.T)\n", - "\n", - " return np.abs(x2 + y2 - 2. * xy)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def euclidean_trick(x, y):\n", - " \"\"\"Euclidean square distance matrix.\n", - " \n", - " Inputs:\n", - " x: (N, m) numpy array\n", - " y: (N, m) numpy array\n", - " \n", - " Ouput:\n", - " (N, N) Euclidean square distance matrix:\n", - " r_ij = x_ij^2 - y_ij^2\n", - " \"\"\"\n", - " x2 = np.einsum('ij,ij->i', x, x)[:, np.newaxis]\n", - " y2 = np.einsum('ij,ij->i', y, y)[np.newaxis, :]\n", - "\n", - " xy = np.dot(x, y.T)\n", - "\n", - " return np.abs(x2 + y2 - 2. * xy)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nsamples = 6000\n", - "nfeat = 50\n", - "\n", - "x = 10. * np.random.random([nsamples, nfeat])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "miniconda-pythonhpc", - "language": "python", - "name": "miniconda-pythonhpc" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/numexpr/01_solution-numpexpr.ipynb b/numexpr/01_solution-numpexpr.ipynb deleted file mode 100644 index 32c0b83..0000000 --- a/numexpr/01_solution-numpexpr.ipynb +++ /dev/null @@ -1,140 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `euclidean_trick` with numexpr\n", - "\n", - "Modify the `euclidean_trick_numexpr` function on the 3rd cell to use numexpr. Time it and compare that the result is the same as `euclidean_trick`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import numexpr as ne" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def euclidean_trick(x, y):\n", - " \"\"\"Euclidean square distance matrix.\n", - " \n", - " Inputs:\n", - " x: (N, m) numpy array\n", - " y: (N, m) numpy array\n", - " \n", - " Ouput:\n", - " (N, N) Euclidean square distance matrix:\n", - " r_ij = x_ij^2 - y_ij^2\n", - " \"\"\"\n", - " x2 = np.einsum('ij,ij->i', x, x)[:, np.newaxis]\n", - " y2 = np.einsum('ij,ij->i', y, y)[np.newaxis, :]\n", - "\n", - " xy = np.dot(x, y.T)\n", - "\n", - " return np.abs(x2 + y2 - 2. * xy)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The most relevant part that can be replaced with `numexpr` is `np.abs(x2 + y2 - 2. * xy)`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def euclidean_trick_numexpr(x, y):\n", - " \"\"\"Euclidean square distance matrix.\n", - " \n", - " Inputs:\n", - " x: (N, m) numpy array\n", - " y: (N, m) numpy array\n", - " \n", - " Ouput:\n", - " (N, N) Euclidean square distance matrix:\n", - " r_ij = x_ij^2 - y_ij^2\n", - " \"\"\"\n", - " x2 = np.einsum('ij,ij->i', x, x)[:, np.newaxis]\n", - " y2 = np.einsum('ij,ij->i', y, y)[np.newaxis, :]\n", - "\n", - " xy = np.dot(x, y.T)\n", - "\n", - " return ne.evaluate('abs(x2 + y2 - 2. * xy)')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ">> Although not really necessary, it may seem a good idea to replace the `einsum`s by numexpr's reductions. However, that may harm the performance: According to [issue#73](https://github.com/pydata/numexpr/issues/73) in numexpr's repository, it seems there is a long-time known problem with numexper `sum` reduction. For our case it wouldn't change too much the results since the array is not to large in the axis whe we are reducing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nsamples = 6000\n", - "nfeat = 50\n", - "\n", - "x = 10. * np.random.random([nsamples, nfeat])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%timeit euclidean_trick(x, x)\n", - "%timeit euclidean_trick_numexpr(x, x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(np.abs(euclidean_trick(x, x) - euclidean_trick_numexpr(x, x)).max())\n", - "print(np.abs(euclidean_trick(x, x) - euclidean_trick_numexpr_redu(x, x)).max())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 933144e0b91f7014aace28890ce3b6fa40889cf6 Mon Sep 17 00:00:00 2001 From: rafael Date: Sun, 18 Jun 2023 13:06:03 +0200 Subject: [PATCH 07/24] update numpy --- numpy/01-numpy-array-internals.ipynb | 6 +++--- numpy/02-broadcasting.ipynb | 4 ++-- numpy/03-euclidean-distance-matrix-numpy.ipynb | 6 +++--- numpy/README.md | 2 +- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/numpy/01-numpy-array-internals.ipynb b/numpy/01-numpy-array-internals.ipynb index 28461d7..fda59ce 100644 --- a/numpy/01-numpy-array-internals.ipynb +++ b/numpy/01-numpy-array-internals.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Understanding the `numpy.ndarray` internals" + "## Understanding the `numpy.ndarray` internals" ] }, { @@ -164,9 +164,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/numpy/02-broadcasting.ipynb b/numpy/02-broadcasting.ipynb index 6f0a204..53f094d 100644 --- a/numpy/02-broadcasting.ipynb +++ b/numpy/02-broadcasting.ipynb @@ -252,9 +252,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/numpy/03-euclidean-distance-matrix-numpy.ipynb b/numpy/03-euclidean-distance-matrix-numpy.ipynb index 3acc40d..bc70476 100644 --- a/numpy/03-euclidean-distance-matrix-numpy.ipynb +++ b/numpy/03-euclidean-distance-matrix-numpy.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Computing the Euclidean Distance Matrix with NumPy\n", + "## Computing the Euclidean Distance Matrix with NumPy\n", "\n", "In this notebook we implement two functions to compute the Euclidean distance matrix. We use a simple algebra trick that makes possible to write the function in a completely vectorized way in terms of optimized NumPy functions." ] @@ -325,9 +325,9 @@ ], "metadata": { "kernelspec": { - "display_name": "hpcpython2022", + "display_name": "hpcpy2023", "language": "python", - "name": "hpcpython2022" + "name": "hpcpy2023" }, "language_info": { "codemirror_mode": { diff --git a/numpy/README.md b/numpy/README.md index 842b085..411ec77 100644 --- a/numpy/README.md +++ b/numpy/README.md @@ -1 +1 @@ -Introduction to NumPy +Introduction to NumPy \ No newline at end of file From 4c8a52f9f31ebfc8c4d255378a0e469866ba9db8 Mon Sep 17 00:00:00 2001 From: rafael Date: Sun, 18 Jun 2023 13:17:42 +0200 Subject: [PATCH 08/24] update locks --- {intro => locks}/locks-example-nolock.ipynb | 0 {intro => locks}/locks-example.ipynb | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename {intro => locks}/locks-example-nolock.ipynb (100%) rename {intro => locks}/locks-example.ipynb (100%) diff --git a/intro/locks-example-nolock.ipynb b/locks/locks-example-nolock.ipynb similarity index 100% rename from intro/locks-example-nolock.ipynb rename to locks/locks-example-nolock.ipynb diff --git a/intro/locks-example.ipynb b/locks/locks-example.ipynb similarity index 100% rename from intro/locks-example.ipynb rename to locks/locks-example.ipynb From b061d90fd09c570b6e94da64f76f7342aa6fe273 Mon Sep 17 00:00:00 2001 From: rafael Date: Sun, 18 Jun 2023 13:28:32 +0200 Subject: [PATCH 09/24] update dask --- dask/05-dask-distributed.ipynb | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/dask/05-dask-distributed.ipynb b/dask/05-dask-distributed.ipynb index 33ed452..0ccb3b5 100644 --- a/dask/05-dask-distributed.ipynb +++ b/dask/05-dask-distributed.ipynb @@ -7,10 +7,13 @@ "tags": [] }, "source": [ - "# Running a `dask.distribute` example with multiple nodes\n", + "# Running a [`Dask.distributed`](https://distributed.dask.org/en/stable/) example with multiple nodes\n", "\n", - "Here we pass the `--dask` option to `%ipcluster start` to create a multi-node dask cluster.\n", - "Then, using `dask.array` we compute the sum of some of the elements of a 1.82TB-large array of random numbers (`N = 500_000`)." + "Here we use a distributed multi-node dask cluster to compute the sum of some of the elements of a 1.82TB-large `dask.array` of random numbers.\n", + "\n", + "The distributed cluster can be created with [IPCMagic](https://github.com/eth-cscs/ipcluster_magic/tree/master) by passing the `--dask` option to `%ipcluster start`. When the cluster has been created, a new cell will appear in the notebook where a `distributed.Client` is defined so everything that's run here is submitted to the cluster.\n", + "\n", + "Here it's not necessary to decorate the cells with `%%px`. After defining the `distributed.Client`, dask will run everything in the cluster." ] }, { @@ -41,7 +44,7 @@ "metadata": {}, "outputs": [], "source": [ - "N = 50_000\n", + "N = 500_000\n", "x = da.random.random((N, N)) #, chunks=(N / 500, 1000))\n", "x" ] @@ -54,7 +57,7 @@ "outputs": [], "source": [ "%%time\n", - "result = x[::2, ::2].mean().compute()" + "result = x[::500, ::500].mean().compute()" ] }, { From 444619c01c35d5b97ec75aab9d53272272182191 Mon Sep 17 00:00:00 2001 From: rafael Date: Sun, 18 Jun 2023 14:02:00 +0200 Subject: [PATCH 10/24] delete ipyparallel_mpi4py --- .../00-mpi4py-ipyparallel.ipynb | 851 ------------------ ipyparallel_mpi4py/03-broadcast.png | Bin 9875 -> 0 bytes ipyparallel_mpi4py/03-gather.png | Bin 9740 -> 0 bytes ipyparallel_mpi4py/03-multireduce.png | Bin 15238 -> 0 bytes ipyparallel_mpi4py/03-reduce.png | Bin 14150 -> 0 bytes ipyparallel_mpi4py/03-scatter.png | Bin 10136 -> 0 bytes ipyparallel_mpi4py/ipyparallel.png | Bin 18561 -> 0 bytes .../send_recv_for_loop_solution.py | 15 - 8 files changed, 866 deletions(-) delete mode 100644 ipyparallel_mpi4py/00-mpi4py-ipyparallel.ipynb delete mode 100644 ipyparallel_mpi4py/03-broadcast.png delete mode 100644 ipyparallel_mpi4py/03-gather.png delete mode 100644 ipyparallel_mpi4py/03-multireduce.png delete mode 100644 ipyparallel_mpi4py/03-reduce.png delete mode 100644 ipyparallel_mpi4py/03-scatter.png delete mode 100644 ipyparallel_mpi4py/ipyparallel.png delete mode 100644 ipyparallel_mpi4py/send_recv_for_loop_solution.py diff --git a/ipyparallel_mpi4py/00-mpi4py-ipyparallel.ipynb b/ipyparallel_mpi4py/00-mpi4py-ipyparallel.ipynb deleted file mode 100644 index ac92a14..0000000 --- a/ipyparallel_mpi4py/00-mpi4py-ipyparallel.ipynb +++ /dev/null @@ -1,851 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# The Message Passing Interface MPI" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Introduction to MPI\n", - "The Message Passing Interface (MPI) is:\n", - "\n", - "- Particularly useful for - but not limited to - distributed memory machines.\n", - "- The _de facto_ standard parallel programming interface.\n", - "\n", - "Many implementations exist - MPICH, OpenMPI, ...\n", - "\n", - "Interfaces in: \n", - "- C/C++ \n", - "- Fortran and ... \n", - "- Python wrappers (mpi4py)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## What is message passing?\n", - "The program is launched as separate processes (tasks) each with their own address space.\n", - "- To achieve parallelism we could have each process work on different data \n", - "\n", - "Information must be **explicitly moved** from process to process:\n", - "- A task can access the data of another process through passing messages (a copy of the data is passed from one process to another)\n", - "\n", - "Two main classes of message passing:\n", - "- **Point-to-point** operations, involving only two processes\n", - "- **Collective** operations, involving a group of processes" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## mpi4py\n", - "\n", - "mpi4py supports both point-to-point and collective communications for \"generic\" Python objects as well as \"buffer-type\" objects\n", - "- for generic Python objects, use the all-lowercase methods, e.g. send, recv, isend, irecv, bcast, scatter, gather, reduce\n", - "- for buffer-type objects (e.g. numpy arrays) use the uppercase versions, e.g. Send, ...\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Hello World!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "%%writefile hello_world_mpi.py\n", - "from mpi4py import MPI\n", - "\n", - "comm = MPI.COMM_WORLD\n", - "size = comm.Get_size()\n", - "rank = comm.Get_rank()\n", - "\n", - "print('Hello from process {} out of {}'.format(rank, size))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Communicators\n", - "A communicator is a group of processes that can talk to each other. \n", - "\n", - "- the size of the communicator is obtained by the `Get_size` method\n", - "\n", - "Within that group each process is assigned a unique rank (assigned by the system when we launch the program).\n", - "\n", - "- The rank of each process is retrieved via the `Get_rank` method \n", - "\n", - "In the above code, we defined set \"comm\" to the default communicator `MPI.COMM_WORLD`. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Executing an MPI code\n", - "\n", - "There is nothing in the MPI standard that specifies how an MPI code should be executed. \n", - "\n", - "Typically, it will be launched with `mpirun` or `mpiexec` followed by python\n", - "e.g. `mpirun -n 4 python3 hello_world_python.py`\n", - "\n", - "On Piz Daint we launch with `srun`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "! srun -n 8 python hello_world_mpi.py" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Point-to-point communication\n", - "\n", - "Point-to-point communication is sending message/data from one process to another. \n", - "\n", - "For point-to-point communication between Python objects, mpi4py provides the `send` and `recv` methods that are similar to those in MPI. \n", - "\n", - "In the following example, rank 0 passes a Python dictionary object to another processes (rank 1):\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile send_recv.py\n", - "from mpi4py import MPI\n", - "\n", - "comm = MPI.COMM_WORLD\n", - "rank = comm.Get_rank()\n", - "size = comm.Get_size()\n", - "\n", - "if rank == 0:\n", - " data = {'a': 9, 'b': 5.001}\n", - " comm.send(data, dest=1, tag=42)\n", - " print('Process {} sent data:'.format(rank), data)\n", - " \n", - "elif rank == 1:\n", - " data = comm.recv(source=0, tag=42)\n", - " print('Process {} received data:'.format(rank), data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! srun -n 2 python send_recv.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Exercise: Generalize the above example so that the master process sends the dictionary to an arbitrary number of processes. Use point-to-point communication.
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile send_recv_for_loop.py\n", - "# write your solution here\n", - "# hint: add a for loop for the send\n", - "# hint: use the rank as the tag" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# try it out:\n", - "# if your code hangs/deadlocks, you might need to `killall srun` from the terminal.\n", - "! srun -n 6 python send_recv_for_loop.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# show a sample solution\n", - "%pycat send_recv_for_loop_solution.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Nonblocking point-to-point communication\n", - "In the above examples, the functions block the caller until the data buffers involved in the communication can be safely reused by the application program.\n", - "\n", - "For potentially increased performance we can try to overlap communication and computation by using nonblocking send and receives.\n", - "\n", - "`isend` and `irecv` are non-blocking methods that immediately return `Request` objects.\n", - "\n", - "We can use the `wait` method to manage the completion of the communication. \n", - "\n", - "You can then perform computation between `comm.isend(...)` and `req.wait()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile isend_irecv_for_loop.py\n", - "from mpi4py import MPI\n", - "\n", - "comm = MPI.COMM_WORLD\n", - "rank = comm.Get_rank()\n", - "size = comm.Get_size()\n", - "\n", - "if rank == 0:\n", - " data = {'a': 9, 'b': 5.001}\n", - " for i in range(1, size):\n", - " req = comm.isend(data, dest=i, tag=i)\n", - " req.wait()\n", - " print('Process {} sent data:'.format(rank), data)\n", - "\n", - "else:\n", - " req = comm.irecv(source=0, tag=rank)\n", - " data = req.wait()\n", - " print('Process {} received data:'.format(rank), data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! srun -n 12 python isend_irecv_for_loop.py" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Collective communication \n", - "Generally groups of processes need to exchange messages between themselves. Rather than explicitly sending and receiving such messages from point to point, MPI comes with group operations known as collectives.\n", - "- Broadcast, scatter, gather and reduction\n", - "- Implementations can optimize performance" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Broadcast\n", - "Send from one process to all other processes.\n", - "\n", - "![broadcast](03-broadcast.png) \n", - "\n", - "We can rewrite our above exmaple using a broadcast." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile broadcast.py\n", - "from mpi4py import MPI\n", - "\n", - "comm = MPI.COMM_WORLD\n", - "rank = comm.Get_rank()\n", - "size = comm.Get_size()\n", - "\n", - "if rank == 0:\n", - " data = {'a': 9, 'b': 5.001}\n", - " \n", - "else:\n", - " data = None\n", - "data = comm.bcast(data, root=0)\n", - "print('Process {} received data:'.format(rank), data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! srun -n 12 python broadcast.py" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Scatter\n", - "Split data into chunks and send a chunk to individual processes to work on.\n", - "\n", - "![scatter](03-scatter.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile scatter.py\n", - "from mpi4py import MPI\n", - "\n", - "comm = MPI.COMM_WORLD\n", - "size = comm.Get_size()\n", - "rank = comm.Get_rank()\n", - "\n", - "if rank == 0:\n", - " data = [(i+1)**2 for i in range(size)]\n", - "else:\n", - " data = None\n", - "data = comm.scatter(data, root=0)\n", - "print('Process {} received data:'.format(rank), data)\n", - "assert data == (rank+1)**2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "! srun -n 4 python scatter.py" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Gather\n", - "Gather the chunks and bring them to the root process\n", - "\n", - "![gather](03-gather.png) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%writefile gather.py\n", - "from mpi4py import MPI\n", - "\n", - "comm = MPI.COMM_WORLD\n", - "size = comm.Get_size()\n", - "rank = comm.Get_rank()\n", - "\n", - "data = (rank+1)**2\n", - "data = comm.gather(data, root=0)\n", - "if rank == 0:\n", - " for i in range(size):\n", - " assert data[i] == (i+1)**2\n", - "else:\n", - " assert data is None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "! srun -n 4 python gather.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: In contrast, allgather will return the results to all processes." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Reduction\n", - "\n", - "Gather results, and then do some computation.\n", - "\n", - "Examples of reductions are:\n", - "- MPI_SUM - Sums the elements. \n", - "- MPI_PROD - Multiplies all elements. \n", - "- MPI_MAX - Returns the maximum element \n", - "- MPI_MIN - Returns the minimum element\n", - "\n", - "![reduce](03-reduce.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "%%writefile reduction.py\n", - "from mpi4py import MPI\n", - "import numpy as np\n", - "\n", - "comm = MPI.COMM_WORLD\n", - "rank = comm.Get_rank()\n", - "size = comm.Get_size()\n", - "\n", - "# element assigned to be the rank of the processor\n", - "value = np.array(rank,'d')\n", - "\n", - "print(' Rank: ',rank, ' value = ', value)\n", - "\n", - "# initialize the np arrays that will store the results:\n", - "value_sum = np.array(0.0,'d')\n", - "value_max = np.array(0.0,'d')\n", - "\n", - "# perform the reductions:\n", - "comm.Reduce(value, value_sum, op=MPI.SUM, root=0)\n", - "comm.Reduce(value, value_max, op=MPI.MAX, root=0)\n", - "\n", - "if rank == 0:\n", - " print(' Rank 0: value_sum = ',value_sum)\n", - " print(' Rank 0: value_max = ',value_max)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "! srun -n 10 python reduction.py" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Lower case vs. upper case versions\n", - "In Python there are two versions of the various MPI methods:\n", - "\n", - "- lower case (send, recv, gather, etc.)\n", - "- Upper case (Send, Recv, Gather, etc.) \n", - "\n", - "You can transmit arbitrary Python data types using the lower-case version of the methods. mpi4py will serialize the data type, send it to the remote process, then deserialize it back to the original data type (a process known as \"pickling\" and \"unpickling\"). This can add significant overhead to the MPI operation.\n", - "\n", - "For sending \"buffer-like\" objects, you can use the upper-case versions. The data object must support Python's \"single-segment buffer interface\". Examples of objects you can send this way are numpy arrays and strings.\n", - "\n", - "Use the upper-case versions where possible as they will be faster (close to the speed of MPI communication in C).\n", - "\n", - "- the memory of the receiving buffer needs to be allocated prior to the communication\n", - "- the size of the sending buffer should not exceed that of the receiving buffer \n", - "- mpi4py expects the buffer-like objects to have contiguous memory (e.g. as is the case with numpy arrays) \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Here we pass a numpy array from the master node to the other processes:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "%%writefile uppercase.py\n", - "from mpi4py import MPI\n", - "import numpy as np\n", - "\n", - "comm = MPI.COMM_WORLD\n", - "rank = comm.Get_rank()\n", - "size = comm.Get_size()\n", - "\n", - "if rank == 0:\n", - " data = np.arange(4.)\n", - " # master process sends data to worker processes by\n", - " # going through the ranks of all worker processes\n", - " for i in range(1, size):\n", - " comm.Send(data, dest=i, tag=i)\n", - " print('Process {} sent data:'.format(rank), data)\n", - "\n", - "else:\n", - " # initialize the receiving buffer\n", - " data = np.zeros(4)\n", - " # receive data from master process\n", - " comm.Recv(data, source=0, tag=rank)\n", - " print('Process {} received data:'.format(rank), data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "! srun -n 8 python uppercase.py" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note: \n", - "\n", - "- the data array has to exist at the time of the `Recv` call.\n", - "- the `Recv` method takes data as the first argument (in contrast to the `recv` method which returns the data object)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "- `mpi4py` is the most commonly used Python interface to MPI \n", - "- MPI calls are via the communicator object\n", - "- You can communicate arbitrary Python objects\n", - "- NumPy arrays can be communicated with similar speed to C and Fortran" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Using mpi4py in a Notebook\n", - "\n", - "So far we have been running our MPI code with `srun -n X ... python `. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "To use MPI directly in a Jupyter Notebook you need to combine:\n", - "\n", - "- mpi4py, and\n", - "- IPython Parallel " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## IPython Parallel \n", - "\n", - "In regular IPython we have a client (the frontend) and a kernel which executes the code. And they communciate with messages. \n", - "\n", - "So, as IPython already does remote execution... if you have _one_ remote kernel, why not have _one hundred_?\n", - "\n", - "These are called IPython Parallel \"engines\"\n", - "\n", - "
\n", - "\n", - "
\n", - "Rather than having clients (blue) connect directly to kernels (green) as in notebook, you have an intermediary of a hub (with schedulers) - known as the \"controller\". The client communicates only with the controller. The controller keeps track of the available engines and forwards requests from the client to the engines. It schedules the work and monitors its status. The results are communicated through the controller back to the client." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To use IPython for parallel computing, you need to start one instance of the controller and one or more instances of the engines. The controller and each engine can run on different machines or on the same machine.\n", - "\n", - "There are three ways to start the controller and engines:\n", - "\n", - "- Separately, using the **ipcontroller** and **ipengine** commands.\n", - "- In an automated manner using the **ipcluster** command.\n", - "- From a custom **magic** developed inhouse `import ipcmagic`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use the first method, which is \"manual\", but provides the most transparency." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " Note: The following commands need to be entered in a terminal. File > New > Terminal. A terminal will open as a new tab. Grab the tab and pull it to the right to have the terminal next to your notebook.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "$ ipcontroller start &\n", - "$ srun -n 4 ipengine --mpi \n", - "``` " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "The IPython Parallel engines need to be started using the `mpirun` command (or equivalent). On our system:\n", - "\n", - "- Start the **ipcontroller** \n", - "- Start the **ipengines** using `srun` and with `--mpi` argument.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's see how we access our \"Cluster\". [IPython][IP] comes with a module [ipyparallel][IPp] that is used to access the engines, we just started. We first need to import Client.\n", - "\n", - "[IPp]: https://ipyparallel.readthedocs.io/en/latest/\n", - "[IP]: http://www.ipython.org\n", - "\n", - "The client is started by first importing it from ipyparallel and then by initalizing it. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import ipyparallel as ipp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " Note: If you receive an error ModuleNotFoundError: No module named 'ipyparallel', ensure that you have the miniconda-pythonhpc kernel loaded\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rc = ipp.Client(profile=\"default\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "List the ids of the engines attached:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rc.ids" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parallel Magics\n", - "\n", - "IPython makes it very easy to use IPyParallel. It provides the magic commands ``%px`` and ``%%px`` to execute code in parallel. The target attribute is used to pick the engines, you want. By default, all the engines of the last Client object created are used. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px --target 0:2\n", - "import os, socket\n", - "print(os.getpid())\n", - "print(socket.gethostname())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%px\n", - "from mpi4py import MPI\n", - "import numpy as np\n", - "\n", - "comm = MPI.COMM_WORLD\n", - "rank = MPI.COMM_WORLD.Get_rank()\n", - "size = MPI.COMM_WORLD.Get_size()\n", - "\n", - "A = np.zeros((size,size))\n", - "if rank==0:\n", - " A = np.random.randn(size, size)\n", - " print(\"Original array on root process\\n\", A)\n", - "local_a = np.zeros(size)\n", - "\n", - "comm.Scatter(A, local_a, root=0)\n", - "print(\"Process\", rank, \"received\", local_a)" - ] - } - ], - "metadata": { - "celltoolbar": "Slideshow", - "kernelspec": { - "display_name": "miniconda-pythonhpc", - "language": "python", - "name": "miniconda-pythonhpc" - }, - 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z7wY#+w=?|hwGIUG<$zK+kUv@zQ6vhCWCpOw;h-?1RlWTb#7|ClNc6vVSOWOEPB;Ch z`4Yg*|EmG>pt7@uRv36$`uSn@)&4I<0)Sn$22zF%+@49}PmDMTfVn&--2Y>@-&K4m zfA0&dCyr$RjXKD6^AoUUpD}0G)5}hhz>;gtf^6XIQt|!WcJoAjG6NNy0-mVxdVcMn z57Smg+u8nodwc(abvzAGz{+kzN_oodbHMVaCVN|M6fhc5B-4vkZu@%EZhLIYjrqPU+wJ_VFImcmIsbh;X5Xdy{`F@6aw%S5Fuw!tF^ye+ zRuWoEUZ`$|g?Ufgxntg7~tW}MP;^8lj zu`14-e|j6U8*npvLgk(RhnwBkpLV)wwJ>MLQCazamfXpa9{s-#>q~$RnRxoSWLvEk zcj{}ty1Mf(UW)J0IeP3v2e+%0yG!Bx%PVba?#^%Dn7J6#Ua6newm{!k_4GuBQ7{l8V36{!o{^2=Q;Q Date: Mon, 19 Jun 2023 09:57:33 +0200 Subject: [PATCH 11/24] Change for 2023 run of the course Signed-off-by: Theofilos Manitaras --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 59c0ff4..b5c9d33 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# This repository contains the material used for the course [High-Performance Computing with Python], organized at CSCS on 21-23 June 2022 +# This repository contains the material used for the course [High-Performance Computing with Python], organized at CSCS on 21-23 June 2023 ## The course covers the following topics: - Vectorization with NumPy and the SciPy stack From 9a83f5beb55f581fc6f35348c2fd700db6992cf4 Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Mon, 19 Jun 2023 10:38:31 +0200 Subject: [PATCH 12/24] Change dates and names for 2023 run of the course Signed-off-by: Theofilos Manitaras --- numba-cuda/1.numba-cuda-basics.ipynb | 4 +--- numba-cuda/2.performance-analysis.ipynb | 5 ++--- numba-cuda/3.memory-optimization.ipynb | 6 ++---- numba-cuda/slides/gpu_intro.pdf | Bin 964423 -> 953431 bytes 4 files changed, 5 insertions(+), 10 deletions(-) diff --git a/numba-cuda/1.numba-cuda-basics.ipynb b/numba-cuda/1.numba-cuda-basics.ipynb index e632ec6..156a5c5 100644 --- a/numba-cuda/1.numba-cuda-basics.ipynb +++ b/numba-cuda/1.numba-cuda-basics.ipynb @@ -6,8 +6,6 @@ "source": [ "# Using Numba for GPUs\n", "\n", - "*Vasileios Karakasis, **Theofilos Manitaras**, CSCS*\n", - "\n", "You may use Numba to generate GPU code from high-level Python functions. Both CUDA and ROCm GPUs (NVIDIA and AMD, respectively) are supported, but we are going to focus only on the CUDA capabilities of Numba.\n", "\n", "There is an almost one-to-one mapping between the Numba high-level Python abstractions and the different CUDA constructs. This practically means that, as a programmer, you need to take care explicitly of the host/device memory management and you need to be aware of the CUDA programming model.\n", @@ -389,7 +387,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.11.1" } }, "nbformat": 4, diff --git a/numba-cuda/2.performance-analysis.ipynb b/numba-cuda/2.performance-analysis.ipynb index 57d3b43..acb1bcb 100644 --- a/numba-cuda/2.performance-analysis.ipynb +++ b/numba-cuda/2.performance-analysis.ipynb @@ -6,7 +6,6 @@ "source": [ "# Understanding the performance of the vecadd kernel\n", "\n", - "_Vasileios Karakasis, **Theofilos Manitaras**, CSCS_\n", "\n", "In this section we will see how we can avoid the JIT cost of Numba, how we can measure the performance of the kernel without the `%timeit` magic, how we can use `nvprof`, the CUDA profiler to analyze the performance of the kernel, and finally, we will evaluate the performance of the kernel.\n", "\n", @@ -551,7 +550,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -565,7 +564,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.11.1" } }, "nbformat": 4, diff --git a/numba-cuda/3.memory-optimization.ipynb b/numba-cuda/3.memory-optimization.ipynb index 7d1c090..ceab7cb 100644 --- a/numba-cuda/3.memory-optimization.ipynb +++ b/numba-cuda/3.memory-optimization.ipynb @@ -6,8 +6,6 @@ "source": [ "# Understanding the memory performance of the GPU\n", "\n", - "*Vasileios Karakasis, **Theofilos Manitaras**, CSCS*\n", - "\n", "In this section we are going to investigate a crucial aspect of the memory locality on the GPUs. It should be perceived in a slightly different way than on the CPUs. To demonstrate this, we will use BLAS matrix-vector kernel using all the tricks we have learned so far. The threads of the GPU operate row-wise in the input matrix, each one taking care of a single row to compute:" ] }, @@ -342,7 +340,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -356,7 +354,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.11.1" } }, "nbformat": 4, diff --git a/numba-cuda/slides/gpu_intro.pdf b/numba-cuda/slides/gpu_intro.pdf index cd03e6cc555c2377ec76d733e51c9e164fdb9597..ee7799e61cb323af5b23141233bd5bf7c9715537 100644 GIT binary patch delta 48347 zcmeFYb#NU^lP@S{W@hFiW@ct)ScWtS5;^Kvbt5-37u!)ly@P87ll$;T#^yM!~sXSeSUHe#}U6tA|9(w4aym> zNJE(Ct>4BHvRyL9bZqv2+Hz~ZU+i@hb97L{R3T}Pr3#EpV*K9 zb^r-$@~SmF`5$N;033kj{}vl7o`Z@Bls)+Z37Rt5CkdV`sGO>~oC;Kl9hCcBAoHJT zCkyC-V#B^;v5){rSm2Tk^gxNQ*#C)50^s^R@O$Ef?CdOpf^e>GF6Jioa9+#w+pL`I z^K7atAfm2v0&3*0W-NR>$#NTP3&P(iv+2LWgG6LOhbMzJAfYFdS;12RIRM=MetoC< 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z!>#~SCMWZO*IxYEhxrO14kmiYy}NFJ^t`48k>=wFu9@Z|3z~6VnTg{$=&FE3Cig}D zm}ljKOD4;5tqSVDMZ*o+$sAe&{1Iy)kJh*3^JMdGd?B@}#)`#!k71xOXofH5RM|W0 z+sugqGb1U>{E)_JJkVJ2qA!lQ`tpLGFE&iAWr3reXbJUfp*m*q{iDggxKZY%{r9-V zJH>tTIv&x{b Date: Tue, 20 Jun 2023 10:15:50 +0200 Subject: [PATCH 13/24] Add example of OpenMP functionality Signed-off-by: Theofilos Manitaras --- cython/openmp/README.md | 6 ++++++ cython/openmp/hello.py | 4 ++++ cython/openmp/hello_omp.pyx | 13 +++++++++++++ 3 files changed, 23 insertions(+) create mode 100644 cython/openmp/README.md create mode 100644 cython/openmp/hello.py create mode 100644 cython/openmp/hello_omp.pyx diff --git a/cython/openmp/README.md b/cython/openmp/README.md new file mode 100644 index 0000000..04a793d --- /dev/null +++ b/cython/openmp/README.md @@ -0,0 +1,6 @@ +Example that demonstrates accessing of OpenMP functionality from Cython. + +Cythonize with `CFLAGS=-fopenmp cythonize -i -3 hello_omp.pyx`. + +Run with `python hello.py` + diff --git a/cython/openmp/hello.py b/cython/openmp/hello.py new file mode 100644 index 0000000..1304671 --- /dev/null +++ b/cython/openmp/hello.py @@ -0,0 +1,4 @@ +from hello_omp import hello_omp + +if __name__ == '__main__': + hello_omp() diff --git a/cython/openmp/hello_omp.pyx b/cython/openmp/hello_omp.pyx new file mode 100644 index 0000000..7e30c60 --- /dev/null +++ b/cython/openmp/hello_omp.pyx @@ -0,0 +1,13 @@ +from cython cimport nogil +from cython.parallel cimport parallel +from libc.stdio cimport printf + +from openmp cimport omp_get_num_threads, omp_get_thread_num + + +def hello_omp(): + printf("---Hello from serial region---\n") + + with nogil, parallel(): + printf("Hello from %2d/%2d \n", omp_get_thread_num(), + omp_get_num_threads()) From 1d856a99c820923c71ea1190cb65f28680f00320 Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Tue, 20 Jun 2023 11:47:46 +0200 Subject: [PATCH 14/24] Add Cython 3 example Signed-off-by: Theofilos Manitaras --- cython/notebooks/Cython2.ipynb | 132 +++++++++++++++++++++++++++++++-- 1 file changed, 127 insertions(+), 5 deletions(-) diff --git a/cython/notebooks/Cython2.ipynb b/cython/notebooks/Cython2.ipynb index b1012d7..46d8763 100644 --- a/cython/notebooks/Cython2.ipynb +++ b/cython/notebooks/Cython2.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ "metadata": {}, "outputs": [], "source": [ - "%time pi_mc_inferred(10000000)" + "%time pi_mc_inferred(10000)" ] }, { @@ -322,13 +322,135 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Additional STL libraries are available and you can look at their [definition files](https://github.com/cython/cython/tree/master/Cython/Includes/libcpp)" + "### Example of interacting with the `random` STL library (Cython 3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%cython\n", + "\n", + "#distutils: language=c++\n", + "#distutils: extra_compile_args = -std=c++11\n", + "#distutils: extra_link_args = -std=c++11\n", + "\n", + "\n", + "from libcpp.random cimport random_device, mt19937, uniform_real_distribution\n", + "\n", + "cdef class my_uniform:\n", + " cdef:\n", + " mt19937 mt\n", + " uniform_real_distribution[double] uni\n", + " \n", + " def __cinit__(self, ):\n", + " cdef random_device rd\n", + " self.mt = mt19937(rd())\n", + " self.uni = uniform_real_distribution[double](0.0, 1.0)\n", + " \n", + " def rand_uni(self):\n", + " return self.uni(self.mt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = my_uniform()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%timeit x.rand_uni()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%cython -a\n", + "\n", + "#distutils: language=c++\n", + "#distutils: extra_compile_args = -std=c++11\n", + "#distutils: extra_link_args = -std=c++11\n", + "\n", + "from cython cimport infer_types\n", + "\n", + "from libcpp.random cimport random_device, mt19937, uniform_real_distribution\n", + "from libcpp.pair cimport pair\n", + "\n", + "cdef class my_uniform:\n", + " cdef:\n", + " mt19937 mt\n", + " uniform_real_distribution[double] uni\n", + " \n", + " def __cinit__(self, ):\n", + " cdef random_device rd\n", + " self.mt = mt19937(rd())\n", + " self.uni = uniform_real_distribution[double](0.0, 1.0)\n", + " \n", + " cdef rand_uni(self):\n", + " return pair[double, double](self.uni(self.mt), self.uni(self.mt))\n", + " \n", + "@infer_types(True)\n", + "cpdef pi_mc_random(n=1000):\n", + " '''Calculate PI using Monte Carlo method'''\n", + " in_circle = 0\n", + " my_uni = my_uniform()\n", + " for i in range(n):\n", + " x, y = my_uni.rand_uni()\n", + " if x * x + y * y <= 1.0:\n", + " in_circle += 1\n", + " \n", + " return 4.0 * in_circle / n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pi_mc_random(100000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Additional STL libraries for Cython 0.29.x are available and you can look at their [definition files](https://github.com/cython/cython/tree/0.29.x/Cython/Includes/libcpp)\n", + "\n", + "### More STL libraries are available for Cython 3 [here](https://github.com/cython/cython/tree/master/Cython/Includes/libcpp)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -342,7 +464,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.11.1" } }, "nbformat": 4, From 3e4a0ef974ab9d16cdb88215780cd51beda5e626 Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Tue, 20 Jun 2023 16:56:15 +0200 Subject: [PATCH 15/24] Add another OpenMP function Signed-off-by: Theofilos Manitaras --- cython/openmp/hello_omp.pyx | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/cython/openmp/hello_omp.pyx b/cython/openmp/hello_omp.pyx index 7e30c60..a30046f 100644 --- a/cython/openmp/hello_omp.pyx +++ b/cython/openmp/hello_omp.pyx @@ -2,12 +2,15 @@ from cython cimport nogil from cython.parallel cimport parallel from libc.stdio cimport printf -from openmp cimport omp_get_num_threads, omp_get_thread_num +from openmp cimport (omp_get_num_threads, omp_get_thread_num, + omp_get_num_procs) def hello_omp(): - printf("---Hello from serial region---\n") + print("Inside serial region") + print("Number of Processors:", omp_get_num_procs()) + print() with nogil, parallel(): - printf("Hello from %2d/%2d \n", omp_get_thread_num(), + printf("Hello from thread %d out of %d\n", omp_get_thread_num(), omp_get_num_threads()) From 5fb5aaf5a19cba49b522d70e864ce7efe458554c Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Tue, 20 Jun 2023 17:10:32 +0200 Subject: [PATCH 16/24] Add multithreaded random generation Signed-off-by: Theofilos Manitaras --- cython/wrapping_cpp_parallel/README.md | 5 +++ cython/wrapping_cpp_parallel/mt_random.pxd | 9 ++++++ cython/wrapping_cpp_parallel/pi_calculator.py | 13 ++++++++ cython/wrapping_cpp_parallel/pi_mc.pyx | 31 +++++++++++++++++++ 4 files changed, 58 insertions(+) create mode 100644 cython/wrapping_cpp_parallel/README.md create mode 100644 cython/wrapping_cpp_parallel/mt_random.pxd create mode 100644 cython/wrapping_cpp_parallel/pi_calculator.py create mode 100644 cython/wrapping_cpp_parallel/pi_mc.pyx diff --git a/cython/wrapping_cpp_parallel/README.md b/cython/wrapping_cpp_parallel/README.md new file mode 100644 index 0000000..99aa586 --- /dev/null +++ b/cython/wrapping_cpp_parallel/README.md @@ -0,0 +1,5 @@ +Example that demonstrates wrapping the C++11 standard library random number generation utilities using Cython. + +Cythonize with `cythonize -i -3 pi_mc.pyx`. + +Run with `python pi_calculator.py ` diff --git a/cython/wrapping_cpp_parallel/mt_random.pxd b/cython/wrapping_cpp_parallel/mt_random.pxd new file mode 100644 index 0000000..de7b549 --- /dev/null +++ b/cython/wrapping_cpp_parallel/mt_random.pxd @@ -0,0 +1,9 @@ +cdef extern from "" namespace "std" nogil: + cdef cppclass mt19937: + mt19937() + mt19937(unsigned int seed) + + cdef cppclass uniform_real_distribution[T]: + uniform_real_distribution() + uniform_real_distribution(T a, T b) + T operator()(mt19937 gen) diff --git a/cython/wrapping_cpp_parallel/pi_calculator.py b/cython/wrapping_cpp_parallel/pi_calculator.py new file mode 100644 index 0000000..7ff80c3 --- /dev/null +++ b/cython/wrapping_cpp_parallel/pi_calculator.py @@ -0,0 +1,13 @@ +#!/usr/bin/python3 + +import sys +from pi_mc import pi_mc + + +if __name__ == '__main__': + if len(sys.argv) < 2: + print(f'Usage: {sys.argv[0]} ') + exit(-1) + + pi = pi_mc(int(sys.argv[1])) + print(f'PI is: {pi:.5f}') diff --git a/cython/wrapping_cpp_parallel/pi_mc.pyx b/cython/wrapping_cpp_parallel/pi_mc.pyx new file mode 100644 index 0000000..7c955fb --- /dev/null +++ b/cython/wrapping_cpp_parallel/pi_mc.pyx @@ -0,0 +1,31 @@ +# distutils: language = c++ +# distutils: extra_compile_args = -std=c++11 -fopenmp +# distutils: extra_link_args = -std=c++11 -fopenmp + +from cython cimport boundscheck, wraparound, nogil +from mt_random cimport mt19937, uniform_real_distribution +from cython.operator cimport dereference + + +from cython.parallel cimport parallel, prange +from openmp cimport omp_get_thread_num +from libc.stdio cimport fprintf, stderr + +@boundscheck(False) +@wraparound(False) +cpdef double pi_mc(long long n=1000): + '''Calculate PI using Monte Carlo method''' + cdef long long in_circle = 0 + cdef long long i + cdef double x, y + cdef uniform_real_distribution[double] dist = uniform_real_distribution[double](0.0,1.0) + cdef mt19937 *gen + + with nogil, parallel(): + gen = new mt19937(omp_get_thread_num()) + for i in prange(n): + x, y = dist(dereference(gen)), dist(dereference(gen)) + if x * x + y * y <= 1.0: + in_circle += 1 + + return 4.0 * in_circle / n From 704efad356381fffffffb971c2f217915cb45079 Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Tue, 20 Jun 2023 17:30:42 +0200 Subject: [PATCH 17/24] Improvement Signed-off-by: Theofilos Manitaras --- cython/wrapping_cpp_parallel/pi_calculator.py | 2 +- cython/wrapping_cpp_parallel/pi_mc.pyx | 10 ++++------ 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/cython/wrapping_cpp_parallel/pi_calculator.py b/cython/wrapping_cpp_parallel/pi_calculator.py index 7ff80c3..64a7e79 100644 --- a/cython/wrapping_cpp_parallel/pi_calculator.py +++ b/cython/wrapping_cpp_parallel/pi_calculator.py @@ -10,4 +10,4 @@ exit(-1) pi = pi_mc(int(sys.argv[1])) - print(f'PI is: {pi:.5f}') + print(f'PI is: {pi:.10f}') diff --git a/cython/wrapping_cpp_parallel/pi_mc.pyx b/cython/wrapping_cpp_parallel/pi_mc.pyx index 7c955fb..e7c281d 100644 --- a/cython/wrapping_cpp_parallel/pi_mc.pyx +++ b/cython/wrapping_cpp_parallel/pi_mc.pyx @@ -4,12 +4,10 @@ from cython cimport boundscheck, wraparound, nogil from mt_random cimport mt19937, uniform_real_distribution -from cython.operator cimport dereference - from cython.parallel cimport parallel, prange from openmp cimport omp_get_thread_num -from libc.stdio cimport fprintf, stderr +from libc.stdio cimport printf @boundscheck(False) @wraparound(False) @@ -19,12 +17,12 @@ cpdef double pi_mc(long long n=1000): cdef long long i cdef double x, y cdef uniform_real_distribution[double] dist = uniform_real_distribution[double](0.0,1.0) - cdef mt19937 *gen + cdef mt19937 gen with nogil, parallel(): - gen = new mt19937(omp_get_thread_num()) + gen = mt19937(omp_get_thread_num()) for i in prange(n): - x, y = dist(dereference(gen)), dist(dereference(gen)) + x, y = dist(gen), dist(gen) if x * x + y * y <= 1.0: in_circle += 1 From 069c1d83c2e5bd6842a875cfb35d010a7f202182 Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Wed, 21 Jun 2023 16:24:14 +0200 Subject: [PATCH 18/24] Fix bug in primes calculation Signed-off-by: Theofilos Manitaras --- cython/cythonize/primes.pyx | 2 +- cython/wrapping_c/primes.c | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/cython/cythonize/primes.pyx b/cython/cythonize/primes.pyx index e2109f9..e889678 100644 --- a/cython/cythonize/primes.pyx +++ b/cython/cythonize/primes.pyx @@ -14,7 +14,7 @@ cpdef bint is_prime(int n): elif n == 2: return False else: - for i in range(2, ceil(sqrt(n))): + for i in range(1, ceil(sqrt(n))): if n % i == 0: return False diff --git a/cython/wrapping_c/primes.c b/cython/wrapping_c/primes.c index 4917210..4bb52f1 100644 --- a/cython/wrapping_c/primes.c +++ b/cython/wrapping_c/primes.c @@ -9,7 +9,7 @@ bool is_prime(int n) { return false; else { int n_sqrt = ceil(sqrt(n)); - for (int i = 2; i < n_sqrt; i++) { + for (int i = 1; i < n_sqrt; i++) { if (n % i == 0) return false; } From 6cde647ff6e68cd9f9619903b7a2f404ac44f62d Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Wed, 21 Jun 2023 18:23:38 +0200 Subject: [PATCH 19/24] Fix prime calculation Signed-off-by: Theofilos Manitaras --- numba/numba-cpu/Numba_Cpu.ipynb | 357 +++++++++++++++++++++++++++----- 1 file changed, 305 insertions(+), 52 deletions(-) diff --git a/numba/numba-cpu/Numba_Cpu.ipynb b/numba/numba-cpu/Numba_Cpu.ipynb index c13c227..2423ff7 100644 --- a/numba/numba-cpu/Numba_Cpu.ipynb +++ b/numba/numba-cpu/Numba_Cpu.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ " return False\n", " else:\n", " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(3, n_sqrt):\n", + " for i in range(1, n_sqrt):\n", " if n % i == 0:\n", " return False\n", " \n", @@ -79,9 +79,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5925961 True\n" + ] + } + ], "source": [ "n = rng.integers(2, 10000000) # Get a random integer between 2 and 10000000\n", "print(n, is_prime(n))" @@ -89,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -98,9 +106,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_12129/548429467.py:2: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", + " def is_prime_jitted(n):\n" + ] + } + ], "source": [ "@numba.jit\n", "def is_prime_jitted(n):\n", @@ -112,7 +129,7 @@ " return False\n", " else:\n", " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(3, n_sqrt):\n", + " for i in range(1, n_sqrt):\n", " if n % i == 0:\n", " return False\n", "\n", @@ -121,9 +138,72 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 30.7 ms, sys: 0 ns, total: 30.7 ms\n", + "Wall time: 30.2 ms\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_12129/548429467.py:1: NumbaWarning: \n", + "Compilation is falling back to object mode WITH looplifting enabled because Function \"is_prime_jitted\" failed type inference due to: No implementation of function Function() found for signature:\n", + " \n", + " >>> mod(Literal[str](\"%s\" <= 1), int64)\n", + " \n", + "There are 6 candidate implementations:\n", + " - Of which 4 did not match due to:\n", + " Overload of function 'mod': File: : Line N/A.\n", + " With argument(s): '(unicode_type, int64)':\n", + " No match.\n", + " - Of which 2 did not match due to:\n", + " Operator Overload in function 'mod': File: unknown: Line unknown.\n", + " With argument(s): '(unicode_type, int64)':\n", + " No match for registered cases:\n", + " * (int64, int64) -> int64\n", + " * (int64, uint64) -> int64\n", + " * (uint64, int64) -> int64\n", + " * (uint64, uint64) -> uint64\n", + " * (float32, float32) -> float32\n", + " * (float64, float64) -> float64\n", + "\n", + "During: typing of intrinsic-call at /tmp/ipykernel_12129/548429467.py (4)\n", + "\n", + "File \"../../../../../tmp/ipykernel_12129/548429467.py\", line 4:\n", + "\n", + "\n", + " @numba.jit\n", + "/tmp/ipykernel_12129/548429467.py:1: NumbaWarning: \n", + "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"is_prime_jitted\" failed type inference due to: Cannot determine Numba type of \n", + "\n", + "File \"../../../../../tmp/ipykernel_12129/548429467.py\", line 10:\n", + "\n", + "\n", + " @numba.jit\n" + ] + }, + { + "ename": "NumbaNotImplementedError", + "evalue": "Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNumbaNotImplementedError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n", + "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:468\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 464\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;241m.\u001b[39mrstrip()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThis error may have been caused \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby the following argument(s):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00margs_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 466\u001b[0m e\u001b[38;5;241m.\u001b[39mpatch_message(msg)\n\u001b[0;32m--> 468\u001b[0m \u001b[43merror_rewrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtyping\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mUnsupportedError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# Something unsupported is present in the user code, add help info\u001b[39;00m\n\u001b[1;32m 471\u001b[0m error_rewrite(e, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:409\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args..error_rewrite\u001b[0;34m(e, issue_type)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28;01mNone\u001b[39;00m)\n", + "\u001b[0;31mNumbaNotImplementedError\u001b[0m: Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)" + ] + } + ], "source": [ "numbers = rng.integers(2, 100000, size=10000)\n", "%time p1 = [is_prime(n) for n in numbers]\n", @@ -139,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -153,7 +233,7 @@ " return False\n", " else:\n", " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(3, n_sqrt):\n", + " for i in range(1, n_sqrt):\n", " if n % i == 0:\n", " return False\n", "\n", @@ -162,9 +242,38 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "NumbaNotImplementedError", + "evalue": "Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNumbaNotImplementedError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n", + "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:468\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 464\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;241m.\u001b[39mrstrip()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThis error may have been caused \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby the following argument(s):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00margs_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 466\u001b[0m e\u001b[38;5;241m.\u001b[39mpatch_message(msg)\n\u001b[0;32m--> 468\u001b[0m \u001b[43merror_rewrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtyping\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mUnsupportedError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# Something unsupported is present in the user code, add help info\u001b[39;00m\n\u001b[1;32m 471\u001b[0m error_rewrite(e, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:409\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args..error_rewrite\u001b[0;34m(e, issue_type)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28;01mNone\u001b[39;00m)\n", + "\u001b[0;31mNumbaNotImplementedError\u001b[0m: Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)" + ] + }, + { + "ename": "TypingError", + "evalue": "Failed in nopython mode pipeline (step: nopython frontend)\nNo implementation of function Function() found for signature:\n \n >>> mod(Literal[str](\"%s\" <= 1), int64)\n \nThere are 6 candidate implementations:\n - Of which 4 did not match due to:\n Overload of function 'mod': File: : Line N/A.\n With argument(s): '(unicode_type, int64)':\n No match.\n - Of which 2 did not match due to:\n Operator Overload in function 'mod': File: unknown: Line unknown.\n With argument(s): '(unicode_type, int64)':\n No match for registered cases:\n * (int64, int64) -> int64\n * (int64, uint64) -> int64\n * (uint64, int64) -> int64\n * (uint64, uint64) -> uint64\n * (float32, float32) -> float32\n * (float64, float64) -> float64\n\nDuring: typing of intrinsic-call at /tmp/ipykernel_12129/3640876976.py (4)\n\nFile \"../../../../../tmp/ipykernel_12129/3640876976.py\", line 4:\n\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypingError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n", + "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:468\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 464\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;241m.\u001b[39mrstrip()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThis error may have been caused \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby the following argument(s):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00margs_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 466\u001b[0m e\u001b[38;5;241m.\u001b[39mpatch_message(msg)\n\u001b[0;32m--> 468\u001b[0m \u001b[43merror_rewrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtyping\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mUnsupportedError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# Something unsupported is present in the user code, add help info\u001b[39;00m\n\u001b[1;32m 471\u001b[0m error_rewrite(e, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:409\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args..error_rewrite\u001b[0;34m(e, issue_type)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28;01mNone\u001b[39;00m)\n", + "\u001b[0;31mTypingError\u001b[0m: Failed in nopython mode pipeline (step: nopython frontend)\nNo implementation of function Function() found for signature:\n \n >>> mod(Literal[str](\"%s\" <= 1), int64)\n \nThere are 6 candidate implementations:\n - Of which 4 did not match due to:\n Overload of function 'mod': File: : Line N/A.\n With argument(s): '(unicode_type, int64)':\n No match.\n - Of which 2 did not match due to:\n Operator Overload in function 'mod': File: unknown: Line unknown.\n With argument(s): '(unicode_type, int64)':\n No match for registered cases:\n * (int64, int64) -> int64\n * (int64, uint64) -> int64\n * (uint64, int64) -> int64\n * (uint64, uint64) -> uint64\n * (float32, float32) -> float32\n * (float64, float64) -> float64\n\nDuring: typing of intrinsic-call at /tmp/ipykernel_12129/3640876976.py (4)\n\nFile \"../../../../../tmp/ipykernel_12129/3640876976.py\", line 4:\n\n" + ] + } + ], "source": [ "numbers = rng.integers(2, 100000, size=10000)\n", "%time p1 = [is_prime_jitted(n) for n in numbers]\n", @@ -180,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +303,7 @@ " return False\n", " else:\n", " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(3, n_sqrt):\n", + " for i in range(1, n_sqrt):\n", " if n % i == 0:\n", " return False\n", "\n", @@ -203,9 +312,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "NumbaNotImplementedError", + "evalue": "Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNumbaNotImplementedError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n", + "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:468\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 464\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;241m.\u001b[39mrstrip()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThis error may have been caused \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby the following argument(s):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00margs_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 466\u001b[0m e\u001b[38;5;241m.\u001b[39mpatch_message(msg)\n\u001b[0;32m--> 468\u001b[0m \u001b[43merror_rewrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtyping\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mUnsupportedError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# Something unsupported is present in the user code, add help info\u001b[39;00m\n\u001b[1;32m 471\u001b[0m error_rewrite(e, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:409\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args..error_rewrite\u001b[0;34m(e, issue_type)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28;01mNone\u001b[39;00m)\n", + "\u001b[0;31mNumbaNotImplementedError\u001b[0m: Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 115 ms, sys: 0 ns, total: 115 ms\n", + "Wall time: 163 ms\n" + ] + } + ], "source": [ "numbers = rng.integers(2, 100000, size=10000)\n", "%time p = [is_prime_jitted(n) for n in numbers]\n", @@ -221,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +370,7 @@ " return False\n", " else:\n", " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(3, n_sqrt):\n", + " for i in range(1, n_sqrt):\n", " if n % i == 0:\n", " return False\n", "\n", @@ -247,9 +379,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 4.11 ms, sys: 7 µs, total: 4.11 ms\n", + "Wall time: 4.12 ms\n", + "CPU times: user 113 ms, sys: 1.61 ms, total: 114 ms\n", + "Wall time: 200 ms\n" + ] + } + ], "source": [ "numbers = rng.integers(2, 100000, size=10000)\n", "%time p = [is_prime_njitted(n) for n in numbers]\n", @@ -265,9 +408,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python: Mflops/sec: 5.214584504057698\n", + "Numpy: Mflops/sec: 783.9844888537366\n", + "Numba: Mflops/sec: 1818.8185830046095\n" + ] + } + ], "source": [ "from timeit import default_timer as timer\n", "\n", @@ -340,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -357,7 +510,7 @@ " return False\n", " else:\n", " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(3, n_sqrt):\n", + " for i in range(1, n_sqrt):\n", " if n % i == 0:\n", " return False\n", "\n", @@ -366,9 +519,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 544 µs, sys: 42 µs, total: 586 µs\n", + "Wall time: 591 µs\n", + "CPU times: user 351 µs, sys: 214 µs, total: 565 µs\n", + "Wall time: 570 µs\n" + ] + } + ], "source": [ "numbers = rng.integers(2, 1000000, size=1000)\n", "\n", @@ -379,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -421,9 +585,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 17.5 s, sys: 0 ns, total: 17.5 s\n", + "Wall time: 17.5 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(15, 10))\n", "ax = fig.add_subplot(111)\n", @@ -438,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -462,9 +647,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 708 ms, sys: 439 µs, total: 708 ms\n", + "Wall time: 1.21 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(15, 10))\n", "ax = fig.add_subplot(111)\n", @@ -487,9 +693,56 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n", + "================================================================================\n", + " Parallel Accelerator Optimizing: Function mandelbrot_jitted, \n", + "/tmp/ipykernel_12129/2504093024.py (1) \n", + "================================================================================\n", + "\n", + "\n", + "Parallel loop listing for Function mandelbrot_jitted, /tmp/ipykernel_12129/2504093024.py (1) \n", + "---------------------------------------------------------------|loop #ID\n", + "@numba.njit(parallel=True) | \n", + "def mandelbrot_jitted(X, Y, radius2, itermax): | \n", + " mandel = np.empty(shape=X.shape, dtype=np.int32) | \n", + " for i in numba.prange(X.shape[0]):-------------------------| #0\n", + " for j in range(X.shape[1]): | \n", + " it = 0 | \n", + " cx = X[i, j] | \n", + " cy = Y[i, j] | \n", + " x = cx | \n", + " y = cy | \n", + " while x * x + y * y < 4.0 and it < itermax: | \n", + " x, y = x * x - y * y + cx, 2.0 * x * y + cy | \n", + " it += 1 | \n", + " mandel[i, j] = it | \n", + " | \n", + " return mandel | \n", + "--------------------------------- Fusing loops ---------------------------------\n", + "Attempting fusion of parallel loops (combines loops with similar properties)...\n", + "Following the attempted fusion of parallel for-loops there are 1 parallel for-\n", + "loop(s) (originating from loops labelled: #0).\n", + "--------------------------------------------------------------------------------\n", + "----------------------------- Before Optimisation ------------------------------\n", + "--------------------------------------------------------------------------------\n", + "------------------------------ After Optimisation ------------------------------\n", + "Parallel structure is already optimal.\n", + "--------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------\n", + " \n", + "---------------------------Loop invariant code motion---------------------------\n", + "Allocation hoisting:\n", + "No allocation hoisting found\n" + ] + } + ], "source": [ "mandelbrot_jitted.parallel_diagnostics(level=3)" ] @@ -650,9 +903,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.4" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 9e2dc46321d0c3dd1760ccb7cd36c7c47666ce40 Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Thu, 22 Jun 2023 00:23:46 +0200 Subject: [PATCH 20/24] Add Jax Intro Signed-off-by: Theofilos Manitaras --- jax/jax_intro.ipynb | 323 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 323 insertions(+) create mode 100644 jax/jax_intro.ipynb diff --git a/jax/jax_intro.ipynb b/jax/jax_intro.ipynb new file mode 100644 index 0000000..6436068 --- /dev/null +++ b/jax/jax_intro.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a91a759c", + "metadata": {}, + "source": [ + "# Introduction to [JAX](https://github.com/google/jax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c98b942e", + "metadata": {}, + "outputs": [], + "source": [ + "from contextlib import contextmanager\n", + "from timeit import default_timer\n", + "\n", + "@contextmanager\n", + "def cpu_timer():\n", + " start = default_timer()\n", + " yield\n", + " end = default_timer()\n", + " print(f'Elapsed time: {(end - start) * 1000} ms')" + ] + }, + { + "cell_type": "markdown", + "id": "6f4fa1cb", + "metadata": {}, + "source": [ + "## NumPy functionality with JAX" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aae3e5a7", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ['JAX_ENABLE_X64']='True'\n", + "\n", + "#os.environ['XLA_PYTHON_CLIENT_PREALLOCATE']='false'\n", + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import jax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b74fbce", + "metadata": {}, + "outputs": [], + "source": [ + "x = jnp.arange(10)\n", + "y = jnp.arange(10, 20)\n", + "z = x + y" + ] + }, + { + "cell_type": "markdown", + "id": "07acf5d8", + "metadata": {}, + "source": [ + "#### Euclidean distance matrix\n", + "\n", + "$\n", + " d_e(\\mathbf x, \\mathbf y) =\n", + " \\begin{bmatrix}\n", + " \\sum_{i=1}^n (x_{1i}-y_{1i})^2 & \\sum_{i=1}^n(x_{1i}-y_{2i})^2 & \\cdots & \\sum_{i=1}^n (x_{1i}-y_{ni})^2 \\\\ \n", + " \\sum_{i=1}^n(x_{2i}-y_{1i})^2 & \\sum_{i=1}^n(x_{2i}-y_{2i})^2 & \\cdots & \\sum_{i=1}^n(x_{2i}-y_{ni})^2 \\\\ \n", + " \\vdots & \\vdots & \\ddots & \\vdots \\\\\n", + " \\sum_{i=1}^n(x_{ni}-y_{1i})^2 & \\sum_{i=1}^n(x_{ni}-y_{2i})^2 & \\cdots & \\sum_{i=1}^n(x_{ni}-y_{ni})^2 \\\\ \n", + " \\end{bmatrix}\n", + "$\n", + "\n", + "#### Vectorization friendly summation \n", + "$ \n", + "\\sum_{k=1}^n \\left(x_{ik}-y_{jk}\\right)^2 = \\left(\\vec{x_i} - \\vec {y_j}\\right)\\cdot \\left(\\vec{x_i} - \\vec{y_j}\\right)=\\vec{x_i} \\cdot \\vec{x_i} + \\vec{y_j} \\cdot \\vec{y_j} -2\\vec{x_i}\\cdot \\vec{y_j}$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3f941dc", + "metadata": {}, + "outputs": [], + "source": [ + "def euclidean_distance_cpu(x, y):\n", + " x2 = np.einsum('ij,ij->i', x, x)[:, np.newaxis]\n", + " y2 = np.einsum('ij,ij->i', y, y)[np.newaxis, :]\n", + " xy = x @ y.T\n", + " return np.abs(x2 + y2 - 2.0 * xy)\n", + "\n", + "def euclidean_distance_jax(x, y):\n", + " x2 = jnp.einsum('ij,ij->i', x, x)[:, jnp.newaxis]\n", + " y2 = jnp.einsum('ij,ij->i', y, y)[jnp.newaxis, :]\n", + " xy = x @ y.T\n", + " return jnp.abs(x2 + y2 - 2.0 * xy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f057d56b", + "metadata": {}, + "outputs": [], + "source": [ + "np_rng = np.random.default_rng()\n", + "x_cpu = np_rng.random((5000, 4000))\n", + "y_cpu = np_rng.random((5000, 4000))\n", + "x_gpu = jax.device_put(x_cpu).block_until_ready()\n", + "y_gpu = jax.device_put(y_cpu).block_until_ready()\n", + "\n", + "with cpu_timer():\n", + " eu_cpu = euclidean_distance_cpu(x_cpu, y_cpu)\n", + "\n", + "with cpu_timer():\n", + " eu_jax = euclidean_distance_jax(x_gpu, y_gpu).block_until_ready()\n", + " \n", + " \n", + "assert np.allclose(eu_cpu, jax.device_get(eu_jax))" + ] + }, + { + "cell_type": "markdown", + "id": "1c1079c7", + "metadata": {}, + "source": [ + "### Immutability of JAX arrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c67483d", + "metadata": {}, + "outputs": [], + "source": [ + "A = jnp.array([1., 2., 3., 4.])\n", + "B = jnp.array([2., 3., 4., 5.])\n", + "\n", + "# This is not allowed\n", + "#A[2] = 10.0\n", + "\n", + "A.at[2].set(10.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34b91aa9", + "metadata": {}, + "outputs": [], + "source": [ + "print(A)" + ] + }, + { + "cell_type": "markdown", + "id": "bdb8c560", + "metadata": {}, + "source": [ + "### Random number generation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b4e0202", + "metadata": {}, + "outputs": [], + "source": [ + "key = jax.random.PRNGKey(0)\n", + "x = jax.random.uniform(key, (2, 2))\n", + "y = jax.random.uniform(key,(2, 2))\n", + "print(x)\n", + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18cc3453", + "metadata": {}, + "outputs": [], + "source": [ + "key = jax.random.PRNGKey(0)\n", + "key, subkey = jax.random.split(key)\n", + "\n", + "x = jax.random.uniform(key, (2, 2))\n", + "y = jax.random.uniform(subkey, (2, 2))\n", + "print(x)\n", + "print(y)" + ] + }, + { + "cell_type": "markdown", + "id": "7f867954", + "metadata": {}, + "source": [ + "### Scaled Exponential Linear Unit (SELU) [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515)\n", + "\n", + "\n", + "$$\n", + "f(x) = \\left\\{\n", + "\\begin{array}{ll}\n", + " \\lambda x & if & x \\gt 0 \\\\\n", + " \\lambda \\alpha (e^x - 1) & if & x \\le 0 \n", + "\\end{array} \\right.\n", + "$$\n", + "\n", + "$$\n", + "\\begin{array}{ll}\n", + " \\alpha \\simeq 1.67326 \\\\\n", + " \\lambda \\simeq 1.050701\n", + "\\end{array}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71e139f9", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from math import erfc, sqrt, exp, pi, e\n", + "x_cpu = np.linspace(-10.0, 10.0, 10_000_000)\n", + "\n", + "\n", + "alpha = - sqrt(2.0 / pi) / (erfc(1 / sqrt(2)) * exp(1/2) - 1)\n", + "scale = (\n", + " (1 - erfc(1 / sqrt(2)) * sqrt(e)) * sqrt(2 * pi) / \n", + " sqrt(2 * erfc(sqrt(2)) * e ** 2 + pi * e * erfc(1/sqrt(2)) ** 2 \n", + " - 2 * (2 + pi)* erfc(1 / sqrt(2))*sqrt(e) + pi + 2)\n", + ")\n", + "\n", + "def selu_cpu(x, a=alpha, l=scale):\n", + " return np.where(x > 0, l * x, l * (a * np.exp(x) - a))\n", + "\n", + "print('SELU Cpu: ', end='')\n", + "with cpu_timer():\n", + " s_cpu = selu_cpu(x_cpu)\n", + " \n", + "# Similar Functionality with cupy\n", + "'''\n", + "import cupy as cp\n", + "#x_gpu = cp.array(x_cpu)\n", + "selu_kernel = cp.ElementwiseKernel(\n", + " 'float64 X, float64 a, float64 l',\n", + " 'float64 selu',\n", + " f'selu = X > 0 ? l * X : l * (a * exp(X) - a);', \n", + " 'selu_kernel')\n", + " \n", + "@cp.fuse\n", + "def selu_fused(x, a=alpha, l=scale):\n", + " return cp.where(x > 0, l * x, l * (a * cp.exp(x) - a))\n", + "\n", + "print('SELU Cpu: ', end='')\n", + "with cpu_timer():\n", + " s_cpu = selu_cpu(x_cpu)\n", + " \n", + "print('SELU Fused: ', end='')\n", + "with cupy_timer():\n", + " s_gpu_fused = selu_fused(x_gpu)\n", + " \n", + "print('SELU Kernel: ', end='')\n", + "with cupy_timer():\n", + " s_gpu_kernel = selu_kernel(x_gpu, alpha, scale)\n", + " \n", + "assert(np.allclose(s_cpu, s_gpu_fused.get()))\n", + "'''\n", + "\n", + "plt.close('all')\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(x_cpu, s_cpu, '--');\n", + "ax.set_title('SELU function', )\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y(x)')\n", + "ax.grid('Both');" + ] + }, + { + "cell_type": "markdown", + "id": "33374976", + "metadata": {}, + "source": [ + "### (Exercise) Do the same with JAX and measure the performance" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 17977690c25b19b4795111bf6f3f5e0476f3449f Mon Sep 17 00:00:00 2001 From: rafael Date: Thu, 22 Jun 2023 23:11:14 +0200 Subject: [PATCH 21/24] update dask --- dask/01-dask-intro.ipynb | 14 +- ...cityblock-distance-matrix-scipy.dask.ipynb | 6 +- dask/03-dask-arrays.ipynb | 19 +- dask/04-dask-arrays-from-file.ipynb | 22 +- dask/04gpu-dask-arrays-from-file.ipynb | 276 ++++++++++++++++++ dask/05-dask-distributed.ipynb | 2 +- 6 files changed, 323 insertions(+), 16 deletions(-) create mode 100644 dask/04gpu-dask-arrays-from-file.ipynb diff --git a/dask/01-dask-intro.ipynb b/dask/01-dask-intro.ipynb index 5ce0766..7c3e660 100644 --- a/dask/01-dask-intro.ipynb +++ b/dask/01-dask-intro.ipynb @@ -37,11 +37,11 @@ "outputs": [], "source": [ "def square(n):\n", - " time.sleep(1)\n", + " time.sleep(2)\n", " return n * n\n", " \n", "def add(m, n):\n", - " time.sleep(1)\n", + " time.sleep(2)\n", " return m + n" ] }, @@ -87,6 +87,16 @@ "z.visualize(rankdir='LR')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "z.visualize(rankdir='LR', optimize_graph=True, color='order',\n", + " cmap='autumn', node_attr={'penwidth': '2'})" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/dask/02-solution-cityblock-distance-matrix-scipy.dask.ipynb b/dask/02-solution-cityblock-distance-matrix-scipy.dask.ipynb index aae8472..c993d60 100644 --- a/dask/02-solution-cityblock-distance-matrix-scipy.dask.ipynb +++ b/dask/02-solution-cityblock-distance-matrix-scipy.dask.ipynb @@ -113,7 +113,8 @@ "metadata": {}, "outputs": [], "source": [ - "cbdm_dask.compute()" + "# visualize the copmutational graph\n", + "visualize(cbdm_dask)" ] }, { @@ -122,8 +123,7 @@ "metadata": {}, "outputs": [], "source": [ - "# visualize the copmutational graph\n", - "visualize(cbdm_dask)" + "cbdm_dask.compute()" ] }, { diff --git a/dask/03-dask-arrays.ipynb b/dask/03-dask-arrays.ipynb index 983a76f..b9f3d43 100644 --- a/dask/03-dask-arrays.ipynb +++ b/dask/03-dask-arrays.ipynb @@ -42,7 +42,7 @@ "metadata": {}, "outputs": [], "source": [ - "x = da.random.random((20000, 20000), chunks='auto')\n", + "x = da.random.random((8000, 8000))\n", "x" ] }, @@ -61,7 +61,8 @@ "metadata": {}, "outputs": [], "source": [ - "y.visualize()" + "y.visualize() #optimize_graph=True, color='order',\n", + " #cmap='autumn', node_attr={'penwidth': '2'})" ] }, { @@ -117,7 +118,7 @@ "source": [ "***\n", "\n", - "Let's consider now the operation `x.dot(x)`. **Question**: Could you explain the results of the timings?" + "Let's consider now the operation `x @ x`. **Question**: Could you explain the results of the timings?" ] }, { @@ -127,9 +128,9 @@ "outputs": [], "source": [ "%%time\n", - "N = 2000\n", + "N = 6000\n", "x = rng.random((N, N))\n", - "y = x.dot(x)" + "y = x @ x" ] }, { @@ -138,9 +139,9 @@ "metadata": {}, "outputs": [], "source": [ - "N = 2000\n", - "x = da.random.random((N, N), chunks=(500, 500))\n", - "y = x.dot(x)" + "N = 6000\n", + "x = da.random.random((N, N))\n", + "y = x @ x" ] }, { @@ -149,7 +150,7 @@ "metadata": {}, "outputs": [], "source": [ - "y.visualize()" + "y.visualize(rankdir='LR')" ] }, { diff --git a/dask/04-dask-arrays-from-file.ipynb b/dask/04-dask-arrays-from-file.ipynb index 041c73f..6a0a126 100644 --- a/dask/04-dask-arrays-from-file.ipynb +++ b/dask/04-dask-arrays-from-file.ipynb @@ -8,7 +8,7 @@ "\n", "This is an example from [martindurant's repo](https://github.com/martindurant/dask-tutorial-scipy-2018). There you can find much more examples. [Here](https://www.youtube.com/watch?v=mqdglv9GnM8) you can find the fantastic presentation for which that repository was made.\n", "\n", - "We are going to use Dask arrays to compute the mean of daily weather meassurements over a month." + "We are going to use dask arrays to compute the mean of daily weather meassurements over a month." ] }, { @@ -63,6 +63,7 @@ "outputs": [], "source": [ "datadir = os.path.join('/scratch/snx3000/class471/weather-big')\n", + "\n", "dsets = [\n", " h5py.File(os.path.join(datadir, filename), mode='r')['/t2m']\n", " for filename in filenames\n", @@ -183,6 +184,25 @@ "mean = x.mean(axis=0)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mean.visualize()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "mean.compute();" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/dask/04gpu-dask-arrays-from-file.ipynb b/dask/04gpu-dask-arrays-from-file.ipynb new file mode 100644 index 0000000..6deb42f --- /dev/null +++ b/dask/04gpu-dask-arrays-from-file.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dask Arrays from file to GPU\n", + "\n", + "This is an example from [martindurant's repo](https://github.com/martindurant/dask-tutorial-scipy-2018). There you can find much more examples. [Here](https://www.youtube.com/watch?v=mqdglv9GnM8) you can find the fantastic presentation for which that repository was made.\n", + "\n", + "We are going to use CuPy as backend for dask arrays to compute in a GPU the mean of daily weather meassurements over a month." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import h5py\n", + "import dask.array as da\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are working here with global weather data in hdf5 format (Hierarchical Data Format). Each hdf5 file takes about 576MB on `SCRATCH` and corresponds to a day measurements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "filenames = ['2014-01-01.hdf5', '2014-01-02.hdf5', '2014-01-03.hdf5',\n", + " '2014-01-04.hdf5', '2014-01-05.hdf5', '2014-01-06.hdf5',\n", + " '2014-01-07.hdf5', '2014-01-08.hdf5', '2014-01-09.hdf5',\n", + " '2014-01-10.hdf5', '2014-01-11.hdf5', '2014-01-12.hdf5',\n", + " '2014-01-13.hdf5', '2014-01-14.hdf5', '2014-01-15.hdf5',\n", + " '2014-01-16.hdf5', '2014-01-17.hdf5', '2014-01-18.hdf5',\n", + " '2014-01-19.hdf5', '2014-01-20.hdf5', '2014-01-21.hdf5',\n", + " '2014-01-22.hdf5', '2014-01-23.hdf5', '2014-01-24.hdf5',\n", + " '2014-01-25.hdf5', '2014-01-26.hdf5', '2014-01-27.hdf5',\n", + " '2014-01-28.hdf5', '2014-01-29.hdf5', '2014-01-30.hdf5',\n", + " '2014-01-31.hdf5']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's open the hdf5 files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "datadir = os.path.join('/scratch/snx3000/class471/weather-big')\n", + "dsets = [\n", + " h5py.File(os.path.join(datadir, filename), mode='r')['/t2m']\n", + " for filename in filenames\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code in the previous cell doesn't load the content of the hdf5 in memory. It just creates a list of hdf5 dataset objects which are *connected* to the files on disk." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dsets[0].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the values for one of the days" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(dsets[0][::8, ::8], cmap='RdBu_r') # We skip 8 elemnts in rows and columns (with the `::8`) to plot faster\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Often we need to do calculations that depend on all the days. For instance let's say that we need to calculate the average values over the month and plot it.\n", + "\n", + "If we are not used to deal with large datasets, we would probably create a numpy array with all the data and compute the mean over the axis that represents the days. However, with data that doesn't fit in memory, that won't be possible. We would get a `MemoryError` exception (probably not in Piz Daint because the data is not big enough). Then we have to come up with a way to compute the averages array by array and modify significantly our scripts and workflows. \n", + "\n", + "In cases like this is where Dask arrays are useful." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting the hdf5 files to a Dask array\n", + "\n", + "We first create a list of dask arrays, and stack it on a single one. Like this, from the point of view of the programmer, it feels like there is only a single hdf5 file on disk. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arrays = [da.from_array(dset, chunks=(5760, 11520))\n", + " for dset in dsets]\n", + "len(arrays)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = da.stack(arrays) # stack all the arrays in a single one\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_cupy = x.to_backend('cupy')\n", + "x_cupy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we only declared the array `x`. It is not loaded in memory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_cupy[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, nothing is loaded in memory. We have declared the Dask array `x`, which is connected to the hdf5 files on Disk. We we compute something Dask will fetch the data from Disk on demand." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that on the next cells we plot the data, but we don't call the `compute` method. This is because matplotlib *undaskifies* the array. In general that happens with functions that expect a numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mean = x_cupy.mean(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mean.visualize()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "x_cupy_mean = mean.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "type(x_cupy_mean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_cupy_mean.device" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_numpy_mean = x_cupy_mean.get() # cupy -> numpy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the mean\n", + "plt.imshow(x_numpy_mean, cmap='RdBu_r')\n", + "plt.axis('off')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hpcpy2023", + "language": "python", + "name": "hpcpy2023" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/dask/05-dask-distributed.ipynb b/dask/05-dask-distributed.ipynb index 0ccb3b5..315344c 100644 --- a/dask/05-dask-distributed.ipynb +++ b/dask/05-dask-distributed.ipynb @@ -40,7 +40,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3991a7e0-111a-49a6-b8a9-a27d4fa95056", + "id": "f94ed16e-1a5d-40e6-bb56-168ddebf0202", "metadata": {}, "outputs": [], "source": [ From a3591ca8acdc7abb622e0132775325071f18371c Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Fri, 23 Jun 2023 00:50:36 +0200 Subject: [PATCH 22/24] Simplify prime calculation Signed-off-by: Theofilos Manitaras --- cython/cythonize/primes.pyx | 14 +- cython/wrapping_c/primes.c | 17 +- numba/numba-cpu/Numba_Cpu.ipynb | 455 ++++++-------------------------- 3 files changed, 99 insertions(+), 387 deletions(-) diff --git a/cython/cythonize/primes.pyx b/cython/cythonize/primes.pyx index e889678..5fdff0f 100644 --- a/cython/cythonize/primes.pyx +++ b/cython/cythonize/primes.pyx @@ -1,4 +1,4 @@ -from libc.math cimport sqrt, ceil +from libc.math cimport sqrt, floor from cython cimport boundscheck, wraparound, cdivision @@ -9,13 +9,11 @@ from cython cimport boundscheck, wraparound, cdivision cpdef bint is_prime(int n): cdef int i - if n == 1 or n == 3: - return True - elif n == 2: + if n <= 1: return False - else: - for i in range(1, ceil(sqrt(n))): - if n % i == 0: - return False + + for i in range(2, floor(sqrt(n))+ 1): + if n % i == 0: + return False return True diff --git a/cython/wrapping_c/primes.c b/cython/wrapping_c/primes.c index 4bb52f1..26374e9 100644 --- a/cython/wrapping_c/primes.c +++ b/cython/wrapping_c/primes.c @@ -3,16 +3,15 @@ #include "primes.h" bool is_prime(int n) { - if (n == 1 || n == 3) + if (n <= 1) return true; - else if (n == 2) - return false; - else { - int n_sqrt = ceil(sqrt(n)); - for (int i = 1; i < n_sqrt; i++) { - if (n % i == 0) - return false; - } + + int n_sqrt = (int)(sqrt(n)); + + for (int i = 2; i <= n_sqrt; i++) { + if (n % i == 0) + return false; } + return true; }; diff --git a/numba/numba-cpu/Numba_Cpu.ipynb b/numba/numba-cpu/Numba_Cpu.ipynb index 2423ff7..1932b27 100644 --- a/numba/numba-cpu/Numba_Cpu.ipynb +++ b/numba/numba-cpu/Numba_Cpu.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -57,39 +57,27 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def is_prime(n):\n", " if n <= 1:\n", - " raise ArithmeticError('\"%s\" <= 1' % n)\n", - " if n == 2 or n == 3:\n", - " return True\n", - " elif n % 2 == 0:\n", " return False\n", - " else:\n", - " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(1, n_sqrt):\n", - " if n % i == 0:\n", - " return False\n", + " \n", + " n_sqrt = math.floor(math.sqrt(n)) + 1\n", + " for i in range(3, n_sqrt):\n", + " if n % i == 0:\n", + " return False\n", " \n", " return True" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5925961 True\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "n = rng.integers(2, 10000000) # Get a random integer between 2 and 10000000\n", "print(n, is_prime(n))" @@ -97,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -106,104 +94,28 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_12129/548429467.py:2: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n", - " def is_prime_jitted(n):\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "@numba.jit\n", "def is_prime_jitted(n):\n", " if n <= 1:\n", - " raise ArithmeticError('\"%s\" <= 1' % n)\n", - " if n == 2 or n == 3:\n", - " return True\n", - " elif n % 2 == 0:\n", " return False\n", - " else:\n", - " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(1, n_sqrt):\n", - " if n % i == 0:\n", - " return False\n", + " \n", + " n_sqrt = math.floor(math.sqrt(n)) + 1\n", + " for i in range(2, n_sqrt):\n", + " if n % i == 0:\n", + " return False\n", "\n", " return True" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 30.7 ms, sys: 0 ns, total: 30.7 ms\n", - "Wall time: 30.2 ms\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_12129/548429467.py:1: NumbaWarning: \n", - "Compilation is falling back to object mode WITH looplifting enabled because Function \"is_prime_jitted\" failed type inference due to: No implementation of function Function() found for signature:\n", - " \n", - " >>> mod(Literal[str](\"%s\" <= 1), int64)\n", - " \n", - "There are 6 candidate implementations:\n", - " - Of which 4 did not match due to:\n", - " Overload of function 'mod': File: : Line N/A.\n", - " With argument(s): '(unicode_type, int64)':\n", - " No match.\n", - " - Of which 2 did not match due to:\n", - " Operator Overload in function 'mod': File: unknown: Line unknown.\n", - " With argument(s): '(unicode_type, int64)':\n", - " No match for registered cases:\n", - " * (int64, int64) -> int64\n", - " * (int64, uint64) -> int64\n", - " * (uint64, int64) -> int64\n", - " * (uint64, uint64) -> uint64\n", - " * (float32, float32) -> float32\n", - " * (float64, float64) -> float64\n", - "\n", - "During: typing of intrinsic-call at /tmp/ipykernel_12129/548429467.py (4)\n", - "\n", - "File \"../../../../../tmp/ipykernel_12129/548429467.py\", line 4:\n", - "\n", - "\n", - " @numba.jit\n", - "/tmp/ipykernel_12129/548429467.py:1: NumbaWarning: \n", - "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"is_prime_jitted\" failed type inference due to: Cannot determine Numba type of \n", - "\n", - "File \"../../../../../tmp/ipykernel_12129/548429467.py\", line 10:\n", - "\n", - "\n", - " @numba.jit\n" - ] - }, - { - "ename": "NumbaNotImplementedError", - "evalue": "Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNumbaNotImplementedError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n", - "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n", - "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:468\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 464\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;241m.\u001b[39mrstrip()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThis error may have been caused \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby the following argument(s):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00margs_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 466\u001b[0m e\u001b[38;5;241m.\u001b[39mpatch_message(msg)\n\u001b[0;32m--> 468\u001b[0m \u001b[43merror_rewrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtyping\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mUnsupportedError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# Something unsupported is present in the user code, add help info\u001b[39;00m\n\u001b[1;32m 471\u001b[0m error_rewrite(e, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:409\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args..error_rewrite\u001b[0;34m(e, issue_type)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28;01mNone\u001b[39;00m)\n", - "\u001b[0;31mNumbaNotImplementedError\u001b[0m: Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "numbers = rng.integers(2, 100000, size=10000)\n", "%time p1 = [is_prime(n) for n in numbers]\n", @@ -219,61 +131,28 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "@numba.jit(nopython=True)\n", "def is_prime_njitted(n):\n", " if n <= 1:\n", - " raise ArithmeticError('\"%s\" <= 1' % n)\n", - " if n == 2 or n == 3:\n", - " return True\n", - " elif n % 2 == 0:\n", " return False\n", - " else:\n", - " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(1, n_sqrt):\n", - " if n % i == 0:\n", - " return False\n", + "\n", + " n_sqrt = math.floor(math.sqrt(n)) + 1\n", + " for i in range(2, n_sqrt):\n", + " if n % i == 0:\n", + " return False\n", "\n", " return True" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "ename": "NumbaNotImplementedError", - "evalue": "Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNumbaNotImplementedError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n", - "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n", - "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:468\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 464\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;241m.\u001b[39mrstrip()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThis error may have been caused \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby the following argument(s):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00margs_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 466\u001b[0m e\u001b[38;5;241m.\u001b[39mpatch_message(msg)\n\u001b[0;32m--> 468\u001b[0m \u001b[43merror_rewrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtyping\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mUnsupportedError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# Something unsupported is present in the user code, add help info\u001b[39;00m\n\u001b[1;32m 471\u001b[0m error_rewrite(e, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:409\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args..error_rewrite\u001b[0;34m(e, issue_type)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28;01mNone\u001b[39;00m)\n", - "\u001b[0;31mNumbaNotImplementedError\u001b[0m: Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)" - ] - }, - { - "ename": "TypingError", - "evalue": "Failed in nopython mode pipeline (step: nopython frontend)\nNo implementation of function Function() found for signature:\n \n >>> mod(Literal[str](\"%s\" <= 1), int64)\n \nThere are 6 candidate implementations:\n - Of which 4 did not match due to:\n Overload of function 'mod': File: : Line N/A.\n With argument(s): '(unicode_type, int64)':\n No match.\n - Of which 2 did not match due to:\n Operator Overload in function 'mod': File: unknown: Line unknown.\n With argument(s): '(unicode_type, int64)':\n No match for registered cases:\n * (int64, int64) -> int64\n * (int64, uint64) -> int64\n * (uint64, int64) -> int64\n * (uint64, uint64) -> uint64\n * (float32, float32) -> float32\n * (float64, float64) -> float64\n\nDuring: typing of intrinsic-call at /tmp/ipykernel_12129/3640876976.py (4)\n\nFile \"../../../../../tmp/ipykernel_12129/3640876976.py\", line 4:\n\n", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypingError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n", - "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n", - "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:468\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 464\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;241m.\u001b[39mrstrip()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThis error may have been caused \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby the following argument(s):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00margs_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 466\u001b[0m e\u001b[38;5;241m.\u001b[39mpatch_message(msg)\n\u001b[0;32m--> 468\u001b[0m \u001b[43merror_rewrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtyping\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mUnsupportedError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# Something unsupported is present in the user code, add help info\u001b[39;00m\n\u001b[1;32m 471\u001b[0m error_rewrite(e, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:409\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args..error_rewrite\u001b[0;34m(e, issue_type)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28;01mNone\u001b[39;00m)\n", - "\u001b[0;31mTypingError\u001b[0m: Failed in nopython mode pipeline (step: nopython frontend)\nNo implementation of function Function() found for signature:\n \n >>> mod(Literal[str](\"%s\" <= 1), int64)\n \nThere are 6 candidate implementations:\n - Of which 4 did not match due to:\n Overload of function 'mod': File: : Line N/A.\n With argument(s): '(unicode_type, int64)':\n No match.\n - Of which 2 did not match due to:\n Operator Overload in function 'mod': File: unknown: Line unknown.\n With argument(s): '(unicode_type, int64)':\n No match for registered cases:\n * (int64, int64) -> int64\n * (int64, uint64) -> int64\n * (uint64, int64) -> int64\n * (uint64, uint64) -> uint64\n * (float32, float32) -> float32\n * (float64, float64) -> float64\n\nDuring: typing of intrinsic-call at /tmp/ipykernel_12129/3640876976.py (4)\n\nFile \"../../../../../tmp/ipykernel_12129/3640876976.py\", line 4:\n\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "numbers = rng.integers(2, 100000, size=10000)\n", "%time p1 = [is_prime_jitted(n) for n in numbers]\n", @@ -289,55 +168,28 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "@numba.njit\n", "def is_prime_njitted(n):\n", " if n <= 1:\n", - " raise ArithmeticError('n <= 1')\n", - " if n == 2 or n == 3:\n", - " return True\n", - " elif n % 2 == 0:\n", " return False\n", - " else:\n", - " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(1, n_sqrt):\n", - " if n % i == 0:\n", - " return False\n", + "\n", + " n_sqrt = math.floor(math.sqrt(n)) + 1\n", + " for i in range(2, n_sqrt):\n", + " if n % i == 0:\n", + " return False\n", "\n", " return True" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "ename": "NumbaNotImplementedError", - "evalue": "Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNumbaNotImplementedError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n", - "File \u001b[0;32m:1\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n", - "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:468\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 464\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(e)\u001b[38;5;241m.\u001b[39mrstrip()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThis error may have been caused \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby the following argument(s):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00margs_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 466\u001b[0m e\u001b[38;5;241m.\u001b[39mpatch_message(msg)\n\u001b[0;32m--> 468\u001b[0m \u001b[43merror_rewrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtyping\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m errors\u001b[38;5;241m.\u001b[39mUnsupportedError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# Something unsupported is present in the user code, add help info\u001b[39;00m\n\u001b[1;32m 471\u001b[0m error_rewrite(e, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/.local/lib/python3.9/site-packages/numba/core/dispatcher.py:409\u001b[0m, in \u001b[0;36m_DispatcherBase._compile_for_args..error_rewrite\u001b[0;34m(e, issue_type)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 409\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28;01mNone\u001b[39;00m)\n", - "\u001b[0;31mNumbaNotImplementedError\u001b[0m: Failed in object mode pipeline (step: object mode frontend)\nFailed in object mode pipeline (step: object mode backend)\n, raise (Var($16binary_modulo.3, 548429467.py:4))\nDuring: lowering \" raise (Var($16binary_modulo.3, 548429467.py:4))\" at /tmp/ipykernel_12129/548429467.py (4)" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 115 ms, sys: 0 ns, total: 115 ms\n", - "Wall time: 163 ms\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "numbers = rng.integers(2, 100000, size=10000)\n", "%time p = [is_prime_jitted(n) for n in numbers]\n", @@ -353,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -363,36 +215,21 @@ "@njit(cache=True)\n", "def is_prime_njitted_cached(n):\n", " if n <= 1:\n", - " raise ArithmeticError('n <= 1')\n", - " if n == 2 or n == 3:\n", - " return True\n", - " elif n % 2 == 0:\n", " return False\n", - " else:\n", - " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(1, n_sqrt):\n", - " if n % i == 0:\n", - " return False\n", + "\n", + " n_sqrt = math.floor(math.sqrt(n)) + 1\n", + " for i in range(2, n_sqrt):\n", + " if n % i == 0:\n", + " return False\n", "\n", " return True" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 4.11 ms, sys: 7 µs, total: 4.11 ms\n", - "Wall time: 4.12 ms\n", - "CPU times: user 113 ms, sys: 1.61 ms, total: 114 ms\n", - "Wall time: 200 ms\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "numbers = rng.integers(2, 100000, size=10000)\n", "%time p = [is_prime_njitted(n) for n in numbers]\n", @@ -408,19 +245,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python: Mflops/sec: 5.214584504057698\n", - "Numpy: Mflops/sec: 783.9844888537366\n", - "Numba: Mflops/sec: 1818.8185830046095\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from timeit import default_timer as timer\n", "\n", @@ -493,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -503,36 +330,21 @@ "@njit(['boolean(int64)', 'boolean(int32)'])\n", "def is_prime_njitted_eager(n):\n", " if n <= 1:\n", - " raise ArithmeticError('n <= 1')\n", - " if n == 2 or n == 3:\n", - " return True\n", - " elif n % 2 == 0:\n", " return False\n", - " else:\n", - " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(1, n_sqrt):\n", - " if n % i == 0:\n", - " return False\n", + "\n", + " n_sqrt = math.floor(math.sqrt(n)) + 1\n", + " for i in range(2, n_sqrt):\n", + " if n % i == 0:\n", + " return False\n", "\n", " return True" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 544 µs, sys: 42 µs, total: 586 µs\n", - "Wall time: 591 µs\n", - "CPU times: user 351 µs, sys: 214 µs, total: 565 µs\n", - "Wall time: 570 µs\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "numbers = rng.integers(2, 1000000, size=1000)\n", "\n", @@ -543,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -560,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -585,30 +397,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 17.5 s, sys: 0 ns, total: 17.5 s\n", - "Wall time: 17.5 s\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig = plt.figure(figsize=(15, 10))\n", "ax = fig.add_subplot(111)\n", @@ -623,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -647,30 +438,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 708 ms, sys: 439 µs, total: 708 ms\n", - "Wall time: 1.21 s\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig = plt.figure(figsize=(15, 10))\n", "ax = fig.add_subplot(111)\n", @@ -693,56 +463,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "================================================================================\n", - " Parallel Accelerator Optimizing: Function mandelbrot_jitted, \n", - "/tmp/ipykernel_12129/2504093024.py (1) \n", - "================================================================================\n", - "\n", - "\n", - "Parallel loop listing for Function mandelbrot_jitted, /tmp/ipykernel_12129/2504093024.py (1) \n", - "---------------------------------------------------------------|loop #ID\n", - "@numba.njit(parallel=True) | \n", - "def mandelbrot_jitted(X, Y, radius2, itermax): | \n", - " mandel = np.empty(shape=X.shape, dtype=np.int32) | \n", - " for i in numba.prange(X.shape[0]):-------------------------| #0\n", - " for j in range(X.shape[1]): | \n", - " it = 0 | \n", - " cx = X[i, j] | \n", - " cy = Y[i, j] | \n", - " x = cx | \n", - " y = cy | \n", - " while x * x + y * y < 4.0 and it < itermax: | \n", - " x, y = x * x - y * y + cx, 2.0 * x * y + cy | \n", - " it += 1 | \n", - " mandel[i, j] = it | \n", - " | \n", - " return mandel | \n", - "--------------------------------- Fusing loops ---------------------------------\n", - "Attempting fusion of parallel loops (combines loops with similar properties)...\n", - "Following the attempted fusion of parallel for-loops there are 1 parallel for-\n", - "loop(s) (originating from loops labelled: #0).\n", - "--------------------------------------------------------------------------------\n", - "----------------------------- Before Optimisation ------------------------------\n", - "--------------------------------------------------------------------------------\n", - "------------------------------ After Optimisation ------------------------------\n", - "Parallel structure is already optimal.\n", - "--------------------------------------------------------------------------------\n", - "--------------------------------------------------------------------------------\n", - " \n", - "---------------------------Loop invariant code motion---------------------------\n", - "Allocation hoisting:\n", - "No allocation hoisting found\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "mandelbrot_jitted.parallel_diagnostics(level=3)" ] @@ -805,16 +528,12 @@ "@numba.vectorize('boolean(int64)')\n", "def is_prime_v(n):\n", " if n <= 1:\n", - " raise ArithmeticError(f'\"0\" <= 1')\n", - " if n == 2 or n == 3:\n", - " return True\n", - " elif n % 2 == 0:\n", " return False\n", - " else:\n", - " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(3, n_sqrt):\n", - " if n % i == 0:\n", - " return False\n", + "\n", + " n_sqrt = math.floor(math.sqrt(n)) + 1\n", + " for i in range(2, n_sqrt):\n", + " if n % i == 0:\n", + " return False\n", " \n", " return True" ] @@ -846,16 +565,12 @@ " target='parallel')\n", "def is_prime_vp(n):\n", " if n <= 1:\n", - " raise ArithmeticError('n <= 1')\n", - " if n == 2 or n == 3:\n", - " return True\n", - " elif n % 2 == 0:\n", " return False\n", - " else:\n", - " n_sqrt = math.ceil(math.sqrt(n))\n", - " for i in range(3, n_sqrt):\n", - " if n % i == 0:\n", - " return False\n", + "\n", + " n_sqrt = math.floor(math.sqrt(n)) + 1\n", + " for i in range(2, n_sqrt):\n", + " if n % i == 0:\n", + " return False\n", " \n", " return True" ] @@ -903,7 +618,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.4" + "version": "3.11.1" } }, "nbformat": 4, From 6f4abba5ec4e84522d29acb3dd4a9058b094498e Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Fri, 23 Jun 2023 00:51:41 +0200 Subject: [PATCH 23/24] Fix bug Signed-off-by: Theofilos Manitaras --- cython/wrapping_c/primes.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cython/wrapping_c/primes.c b/cython/wrapping_c/primes.c index 26374e9..712b772 100644 --- a/cython/wrapping_c/primes.c +++ b/cython/wrapping_c/primes.c @@ -4,7 +4,7 @@ bool is_prime(int n) { if (n <= 1) - return true; + return false; int n_sqrt = (int)(sqrt(n)); From 408886a13162a03e448f6bb547dbacc5511769ae Mon Sep 17 00:00:00 2001 From: Theofilos Manitaras Date: Fri, 23 Jun 2023 00:53:33 +0200 Subject: [PATCH 24/24] Fix wrong starting index Signed-off-by: Theofilos Manitaras --- numba/numba-cpu/Numba_Cpu.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/numba/numba-cpu/Numba_Cpu.ipynb b/numba/numba-cpu/Numba_Cpu.ipynb index 1932b27..cdaba1a 100644 --- a/numba/numba-cpu/Numba_Cpu.ipynb +++ b/numba/numba-cpu/Numba_Cpu.ipynb @@ -66,7 +66,7 @@ " return False\n", " \n", " n_sqrt = math.floor(math.sqrt(n)) + 1\n", - " for i in range(3, n_sqrt):\n", + " for i in range(2, n_sqrt):\n", " if n % i == 0:\n", " return False\n", " \n",