Heat is a flexible and seamless open-source software for high performance data analytics and machine learning. It provides highly optimized algorithms and data structures for tensor computations using CPUs, GPUs and distributed cluster systems on top of MPI. The goal of Heat is to fill the gap between data analytics and machine learning libraries with a strong focus on single-node performance, and traditional high-performance computing (HPC). Heat’s generic Python-first programming interface integrates seamlessly with the existing data science ecosystem and makes it as effortless as using numpy to write scalable scientific and data science applications.
Heat allows you to tackle your actual Big Data challenges that go beyond the computational and memory needs of your laptop and desktop.
In line with HiRSE_PS we would like to achieve at least the following objectives:
- Continuous Benchmarking
- Portation to IPUs and XPUs
- Optimized Communication and Distribution Semantics
Currently Heat is part of the Google Summer of Code 2022. Several projects will center around additional core features and applications.