The Design and Performance of Batched BLAS on Modern High-Performance Computing Systems

Jack Dongarra; Sven Hammarling; Nicholas J. Higham; Samuel D. Relton; Pedro Valero-Lara; Mawussi Zounon

doi:10.1016/j.procs.2017.05.138

The Design and Performance of Batched BLAS on Modern High-Performance Computing Systems

Jack Dongarra, Sven Hammarling, Nicholas J. Higham, Samuel D. Relton, Pedro Valero-Lara, Mawussi Zounon

Computer Science and Mathematics Div

Research output: Contribution to journal › Conference article › peer-review

52 Scopus citations

Abstract

A current trend in high-performance computing is to decompose a large linear algebra problem into batches containing thousands of smaller problems, that can be solved independently, before collating the results. To standardize the interface to these routines, the community is developing an extension to the BLAS standard (the batched BLAS), enabling users to perform thousands of small BLAS operations in parallel whilst making efficient use of their hardware. We discuss the benefits and drawbacks of the current batched BLAS proposals and perform a number of experiments, focusing on a general matrix-matrix multiplication (GEMM), to explore their affect on the performance. In particular we analyze the effect of novel data layouts which, for example, interleave the matrices in memory to aid vectorization and prefetching of data. Utilizing these modifications our code outperforms both MKL¹ CuBLAS² by up to 6 times on the self-hosted Intel KNL (codenamed Knights Landing) and Kepler GPU architectures, for large numbers of double precision GEMM operations using matrices of size 2 × 2 to 20 × 20.

Original language	English
Pages (from-to)	495-504
Number of pages	10
Journal	Procedia Computer Science
Volume	108
DOIs	https://doi.org/10.1016/j.procs.2017.05.138
State	Published - 2017
Event	International Conference on Computational Science ICCS 2017 - Zurich, Switzerland Duration: Jun 12 2017 → Jun 14 2017

Bibliographical note

Publisher Copyright:
© 2017 The Authors. Published by Elsevier B.V.

Keywords

BLAS
Batched BLAS
High-performance computing
Memory management
Parallel processing
Scientific computing

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10.1016/j.procs.2017.05.138

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title = "The Design and Performance of Batched BLAS on Modern High-Performance Computing Systems",

abstract = "A current trend in high-performance computing is to decompose a large linear algebra problem into batches containing thousands of smaller problems, that can be solved independently, before collating the results. To standardize the interface to these routines, the community is developing an extension to the BLAS standard (the batched BLAS), enabling users to perform thousands of small BLAS operations in parallel whilst making efficient use of their hardware. We discuss the benefits and drawbacks of the current batched BLAS proposals and perform a number of experiments, focusing on a general matrix-matrix multiplication (GEMM), to explore their affect on the performance. In particular we analyze the effect of novel data layouts which, for example, interleave the matrices in memory to aid vectorization and prefetching of data. Utilizing these modifications our code outperforms both MKL1 CuBLAS2 by up to 6 times on the self-hosted Intel KNL (codenamed Knights Landing) and Kepler GPU architectures, for large numbers of double precision GEMM operations using matrices of size 2 × 2 to 20 × 20.",

keywords = "BLAS, Batched BLAS, High-performance computing, Memory management, Parallel processing, Scientific computing",

author = "Jack Dongarra and Sven Hammarling and Higham, {Nicholas J.} and Relton, {Samuel D.} and Pedro Valero-Lara and Mawussi Zounon",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors. Published by Elsevier B.V.; International Conference on Computational Science ICCS 2017 ; Conference date: 12-06-2017 Through 14-06-2017",

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AU - Relton, Samuel D.

AU - Valero-Lara, Pedro

AU - Zounon, Mawussi

PY - 2017

Y1 - 2017

N2 - A current trend in high-performance computing is to decompose a large linear algebra problem into batches containing thousands of smaller problems, that can be solved independently, before collating the results. To standardize the interface to these routines, the community is developing an extension to the BLAS standard (the batched BLAS), enabling users to perform thousands of small BLAS operations in parallel whilst making efficient use of their hardware. We discuss the benefits and drawbacks of the current batched BLAS proposals and perform a number of experiments, focusing on a general matrix-matrix multiplication (GEMM), to explore their affect on the performance. In particular we analyze the effect of novel data layouts which, for example, interleave the matrices in memory to aid vectorization and prefetching of data. Utilizing these modifications our code outperforms both MKL1 CuBLAS2 by up to 6 times on the self-hosted Intel KNL (codenamed Knights Landing) and Kepler GPU architectures, for large numbers of double precision GEMM operations using matrices of size 2 × 2 to 20 × 20.

AB - A current trend in high-performance computing is to decompose a large linear algebra problem into batches containing thousands of smaller problems, that can be solved independently, before collating the results. To standardize the interface to these routines, the community is developing an extension to the BLAS standard (the batched BLAS), enabling users to perform thousands of small BLAS operations in parallel whilst making efficient use of their hardware. We discuss the benefits and drawbacks of the current batched BLAS proposals and perform a number of experiments, focusing on a general matrix-matrix multiplication (GEMM), to explore their affect on the performance. In particular we analyze the effect of novel data layouts which, for example, interleave the matrices in memory to aid vectorization and prefetching of data. Utilizing these modifications our code outperforms both MKL1 CuBLAS2 by up to 6 times on the self-hosted Intel KNL (codenamed Knights Landing) and Kepler GPU architectures, for large numbers of double precision GEMM operations using matrices of size 2 × 2 to 20 × 20.

KW - BLAS

KW - Batched BLAS

KW - High-performance computing

KW - Memory management

KW - Parallel processing

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SN - 1877-0509

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EP - 504

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - International Conference on Computational Science ICCS 2017

Y2 - 12 June 2017 through 14 June 2017

ER -

The Design and Performance of Batched BLAS on Modern High-Performance Computing Systems

Abstract

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Keywords

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