HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics

1 code implementation • 27 Jul 2020 • Markus Götz, Daniel Coquelin, Charlotte Debus, Kai Krajsek, Claudia Comito, Philipp Knechtges, Björn Hagemeier, Michael Tarnawa, Simon Hanselmann, Martin Siggel, Achim Basermann, Achim Streit

With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis.

A Recursive Algebraic Coloring Technique for Hardware-Efficient Symmetric Sparse Matrix-Vector Multiplication

1 code implementation • 15 Jul 2019 • Christie L. Alappat, Georg Hager, Olaf Schenk, Jonas Thies, Achim Basermann, Alan R. Bishop, Holger Fehske, Gerhard Wellein

The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building block for many numerical linear algebra kernel operations or graph traversal applications.

Distributed, Parallel, and Cluster Computing Performance

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

1 code implementation • 29 Jul 2015 • Moritz Kreutzer, Jonas Thies, Melven Röhrig-Zöllner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein

Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators such as the Intel Xeon Phi.

Distributed, Parallel, and Cluster Computing Mathematical Software