Interactive benchmarking analysis via Ginko Performance Explorer

HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. This summer I will be working closely with the community and mentors to produce automatized benchmarks/scaling tests for faster detection of performance degradation and make the results easily accessible on the project’s GitHub repository.