The goal of this project is to deliver a high-performance pipeline software framework that is:

• Scalable - it runs on a desktop and/or HPC centres on multiple nodes.
• Reliable - it has a user-friendly API, no memory leaks, minimal memory allocations, and it is thoroughly tested.
• Portable - it runs on multiple platforms both in a bare metal or containerized environment.
• Documented - it has complete user and developer documentation.

Most importantly, the framework should simplify the development process by supporting it in the following ways:

• Separate algorithm and module development from execution flow
• Decouple processing modules from data models
• Enforce clearly defined data structures and interfaces
• Processing modules are plug-and-play
• Bridge C/C++ code and Python code
• Rely on a minimum of third-party dependencies, as necessary.

The framework is implemented under the core folder.

A brief, high-level overview of the project:

• The code is implemented with modern C++, and Python 3.8 in mind.
• All code should compile without warnings.
• Fully documented with DOxygen comments.
• Tested with about a dozen or so analyzers and tools.
• The code is containerized.
• Pipelines of processing modules can run as C++ or Python, in order to facilitate integration of different code projects into a single, unified pipeline.
• Coverage tests should achieve 100% coverage.
• The code is extensively benchmarked.

The project components and their functionality are as follows

You may wish to consider treating each sub-folder as a separate repository for your own projects.

Refer to the README.md files in each of the respective sub-folders for more details. Some modules may make use of data models in the data_models folder. If so, please consult the relevant data models and follow the instructions in the relevant README.md.