GHOST: Building Blocks for High Performance Sparse Linear Algebra on Heterogeneous Systems

Moritz Kreutzer, Jonas Thies, Melven Röhrig-Zöllner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)

Abstract

While many of the architectural details of future exascale-class high performance computer systems are still a matter of intense research, there appears to be a general consensus that they will be strongly heterogeneous, featuring “standard” as well as “accelerated” resources. Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators such as the Intel Xeon Phi. Any software infrastructure that claims usefulness for such environments must be able to meet their inherent challenges: massive multi-level parallelism, topology, asynchronicity, and abstraction. The “General, Hybrid, and Optimized Sparse Toolkit” (GHOST) is a collection of building blocks that targets algorithms dealing with sparse matrix representations on current and future large-scale systems. It implements the “MPI+X” paradigm, has a pure C interface, and provides hybrid-parallel numerical kernels, intelligent resource management, and truly heterogeneous parallelism for multicore CPUs, Nvidia GPUs, and the Intel Xeon Phi. We describe the details of its design with respect to the challenges posed by modern heterogeneous supercomputers and recent algorithmic developments. Implementation details which are indispensable for achieving high efficiency are pointed out and their necessity is justified by performance measurements or predictions based on performance models. We also provide instructions on how to make use of GHOST in existing software packages, together with a case study which demonstrates the applicability and performance of GHOST as a component within a larger software stack. The library code and several applications are available as open source.

Original languageEnglish
Pages (from-to)1046-1072
Number of pages27
JournalInternational Journal of Parallel Programming
Volume45
Issue number5
DOIs
Publication statusPublished - 1 Oct 2017
Externally publishedYes

Keywords

  • Data parallelism
  • Heterogeneous computing
  • Large scale computing
  • Software library
  • Sparse linear algebra
  • Task parallelism

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