Introduction to GraphBLAS

Kepner, Jeremy; Aaltonen, Peter; Bader, David A.; Buluç, Aydın; Franchetti, Franz; Gilbert, John R.; Hutchison, Shana; Kumar, M.; Lumsdaine, Andrew; Meyerhenke, Henning; McMillan, Scott; Moreira, José Cláudio Fonseca; Owens, John D.; Yang, Carl; Zalewski, Marcin; Mattson, Timothy G.

doi:10.1201/9781003033707-24

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“…Application of those programs to all vertices in a graph has been likened to a sparse matrix-vector multiplication (SpMV) [38]. GraphBLAS [39] is built around this observation. Selective application of vertex programs has been modelled using a frontier [3].…”

Section: Graph Processingmentioning

confidence: 99%

Software-defined floating-point number formats and their application to graph processing

Vandierendonck

2022

Proceedings of the 36th ACM International Conference on Supercomputing

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This paper proposes software-defined floating-point number formats for graph processing workloads, which can improve performance in irregular workloads by reducing cache misses. Efficient arithmetic on software-defined number formats is challenging, even when based on conversion to wider, hardware-supported formats. We derive efficient conversion schemes that are tuned to the IA64 and AVX512 instruction sets. We demonstrate that: (i) reduced-precision number formats can be applied to graph processing without loss of accuracy; (ii) conversion of floating-point values is possible with minimal instructions; (iii) conversions are most efficient when utilizing vectorized instruction sets, specifically on IA64 processors. Experiments on twelve real-world graph data sets demonstrate that our techniques result in speedups up to 89% for PageRank and Accelerated PageRank, and up to 35% for Single-Source Shortest Paths. The same techniques help to accelerate the integer-based maximal independent set problem by up to 262%. CCS Concepts• Computing methodologies → Vector / streaming algorithms; • Computer systems organization → Processors and memory architectures; • Mathematics of computing → Numerical analysis; • Software and its engineering → Domain specific languages.

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Section: Graph Processingmentioning

confidence: 99%

Software-defined floating-point number formats and their application to graph processing

Vandierendonck

2022

Proceedings of the 36th ACM International Conference on Supercomputing

View full text Add to dashboard Cite

show abstract

Introduction to GraphBLAS

Cited by 1 publication

References 0 publications

Software-defined floating-point number formats and their application to graph processing

Software-defined floating-point number formats and their application to graph processing

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