Introduction

Goal

The goal of Heat is to fill the gap between machine learning libraries that have a strong focus on exploiting GPUs for performance, and traditional, distributed high-performance computing (HPC). The basic idea is to provide a dtype, distributed tensor library with machine learning methods based on it.

Among other things, the implementation will allow us to tackle use cases that would otherwise exceed memory limits of a single node.

Features

• high-performance n-dimensional tensors

• CPU, GPU and distributed computation using MPI

• powerful machine learning methods using above mentioned tensors