P3.20 Claudia Comito (Jülich Supercomputing Center, Forschungszentrum Jülich )
Theme: Data science challenges: tools from statistics to machine learning
HeAT: a High-Performance-Computing library for Scientific Big Data Analytics
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 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.
