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Optimizing matrix-matrix multiplication on intel's advanced vector extensions multicore processor.

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Title: Optimizing matrix-matrix multiplication on intel's advanced vector extensions multicore processor.
Authors: Hemeida, A.M.1 (AUTHOR) ashraf@aswu.edu.eg, Hassan, S.A.2 (AUTHOR), Alkhalaf, Salem3 (AUTHOR), Mahmoud, M.M.M.2 (AUTHOR), Saber, M.A.2 (AUTHOR), Bahaa Eldin, Ayman M.4 (AUTHOR), Senjyu, Tomonobu5 (AUTHOR), Alayed, Abdullah H.6 (AUTHOR)
Source: Ain Shams Engineering Journal. Dec2020, Vol. 11 Issue 4, p1179-1190. 12p.
Subjects: Matrix multiplications, Multicore processors, Image processing, C++, Electronic data processing
Abstract: This paper is focused on Intel Advanced Vector Extension (AVX) which has been borne of the modern developments in AMD processors and Intel itself. Said prescript processes a chunk of data both individually and altogether. AVX is supporting variety of applications such as image processing. Our goal is to accelerate and optimize square single-precision matrix multiplication from 2080 to 4512, i.e. big size ranges. Our optimization is designed by using AVX instruction sets, OpenMP parallelization, and memory access optimization to overcome bandwidth limitations. This paper is different from other papers by concentrating on several main technique and the results therein. Making parallel implementation guidelines of said algorithms, where the target architecture's characteristics need to be taken into consideration when said algorithms are applied are presented. This work has a comparative study of using most popular compilers: Intel C++ compiler 17.0 over Microsoft Visual Studio C++ compiler 2015. Additionally, a comparative study between single-core and multicore platforms has been examined. The obtained results of the proposed optimized algorithms are achieved a performance improvement of 71%, 59%, and 56% for C = A.B, C = A.BT, and C = AT.B separately compared with results that are achieved by implementing the latest Intel Math Kernel Library 2017 SGEMV subroutines. [ABSTRACT FROM AUTHOR]
Copyright of Ain Shams Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 147504402
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Optimizing matrix-matrix multiplication on intel's advanced vector extensions multicore processor.
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  Data: <searchLink fieldCode="AR" term="%22Hemeida%2C+A%2EM%2E%22">Hemeida, A.M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ashraf@aswu.edu.eg</i><br /><searchLink fieldCode="AR" term="%22Hassan%2C+S%2EA%2E%22">Hassan, S.A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alkhalaf%2C+Salem%22">Alkhalaf, Salem</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mahmoud%2C+M%2EM%2EM%2E%22">Mahmoud, M.M.M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Saber%2C+M%2EA%2E%22">Saber, M.A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bahaa+Eldin%2C+Ayman+M%2E%22">Bahaa Eldin, Ayman M.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Senjyu%2C+Tomonobu%22">Senjyu, Tomonobu</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alayed%2C+Abdullah+H%2E%22">Alayed, Abdullah H.</searchLink><relatesTo>6</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Ain+Shams+Engineering+Journal%22">Ain Shams Engineering Journal</searchLink>. Dec2020, Vol. 11 Issue 4, p1179-1190. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Matrix+multiplications%22">Matrix multiplications</searchLink><br /><searchLink fieldCode="DE" term="%22Multicore+processors%22">Multicore processors</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22C%2B%2B%22">C++</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink>
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  Data: This paper is focused on Intel Advanced Vector Extension (AVX) which has been borne of the modern developments in AMD processors and Intel itself. Said prescript processes a chunk of data both individually and altogether. AVX is supporting variety of applications such as image processing. Our goal is to accelerate and optimize square single-precision matrix multiplication from 2080 to 4512, i.e. big size ranges. Our optimization is designed by using AVX instruction sets, OpenMP parallelization, and memory access optimization to overcome bandwidth limitations. This paper is different from other papers by concentrating on several main technique and the results therein. Making parallel implementation guidelines of said algorithms, where the target architecture's characteristics need to be taken into consideration when said algorithms are applied are presented. This work has a comparative study of using most popular compilers: Intel C++ compiler 17.0 over Microsoft Visual Studio C++ compiler 2015. Additionally, a comparative study between single-core and multicore platforms has been examined. The obtained results of the proposed optimized algorithms are achieved a performance improvement of 71%, 59%, and 56% for C = A.B, C = A.BT, and C = AT.B separately compared with results that are achieved by implementing the latest Intel Math Kernel Library 2017 SGEMV subroutines. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Ain Shams Engineering Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.asej.2020.01.003
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        Text: English
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        PageCount: 12
        StartPage: 1179
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      – SubjectFull: Matrix multiplications
        Type: general
      – SubjectFull: Multicore processors
        Type: general
      – SubjectFull: Image processing
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      – SubjectFull: C++
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      – SubjectFull: Electronic data processing
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              Text: Dec2020
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              Y: 2020
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