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

Moritz Kreutzer, Jonas Thies, Melven Rohrig-Zollner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein
Erlangen Regional Computing Center, Friedrich-Alexander-Universitat, Erlangen-Nurnberg, 91058 Erlangen, Germany
arXiv:1507.08101 [cs.DC], (29 Jul 2015)

   title={GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems},

   author={Kreutzer, Moritz and Thies, Jonas and Rohrig-Zollner, Melven and Pieper, Andreas and Shahzad, Faisal and Galgon, Martin and Basermann, Achim and Fehske, Holger and Hager, Georg and Wellein, Gerhard},






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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. The library code and several applications are available as open-source. In addition, instructions on how to make use of GHOST in existing software packages are provided.
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