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Superblock-based performance optimization for Sunway Math Library on SW26010 many-core processor.

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Název: Superblock-based performance optimization for Sunway Math Library on SW26010 many-core processor.
Autoři: Cao, Hao, Guo, Shaozhong, Hao, Jiangwei, Xia, Yuanyuan, Xu, Jinchen
Zdroj: Journal of Supercomputing; Mar2022, Vol. 78 Issue 4, p4827-4849, 23p
Témata: MATHEMATICS, COMPILERS (Computer programs)
Abstrakt: The SW26010 many-core processor is based on the Sunway architecture that is composed of management and computing processing elements (MPE and CPE, respectively), each of which is equipped with a stand-alone math library. The issue is that each Sunway Math Library (SML) version is written in assembly which is outside the power of compilers that take high-level languages as input; existing optimization approaches thus mainly rely on manual strategies, which are considered inefficient. In this paper, we leverage the concept of superblock scheduling, a well-known compilation technique, and present a tool named SMPOT to optimize the SML. SMPOT first builds a superblock using a novel tail duplication algorithm, and then uses code motion restrictions to avoid code compensation, followed by matching the machine model. Finally, it reorders instructions on the main path by an activation algorithm based on available computing resources. The experimental results show that SMPOT can effectively improve the performance of the SML. The main path performance of MPE functions is improved by 10.61% on average and overall performance by 5.40%. The main path performance of CPE functions is improved by 5.72% on average and overall performance by 2.98%. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Supercomputing is the property of Springer Nature 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.)
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  Label: Title
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  Data: Superblock-based performance optimization for Sunway Math Library on SW26010 many-core processor.
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  Data: <searchLink fieldCode="AR" term="%22Cao%2C+Hao%22">Cao, Hao</searchLink><br /><searchLink fieldCode="AR" term="%22Guo%2C+Shaozhong%22">Guo, Shaozhong</searchLink><br /><searchLink fieldCode="AR" term="%22Hao%2C+Jiangwei%22">Hao, Jiangwei</searchLink><br /><searchLink fieldCode="AR" term="%22Xia%2C+Yuanyuan%22">Xia, Yuanyuan</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Jinchen%22">Xu, Jinchen</searchLink>
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  Data: Journal of Supercomputing; Mar2022, Vol. 78 Issue 4, p4827-4849, 23p
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The SW26010 many-core processor is based on the Sunway architecture that is composed of management and computing processing elements (MPE and CPE, respectively), each of which is equipped with a stand-alone math library. The issue is that each Sunway Math Library (SML) version is written in assembly which is outside the power of compilers that take high-level languages as input; existing optimization approaches thus mainly rely on manual strategies, which are considered inefficient. In this paper, we leverage the concept of superblock scheduling, a well-known compilation technique, and present a tool named SMPOT to optimize the SML. SMPOT first builds a superblock using a novel tail duplication algorithm, and then uses code motion restrictions to avoid code compensation, followed by matching the machine model. Finally, it reorders instructions on the main path by an activation algorithm based on available computing resources. The experimental results show that SMPOT can effectively improve the performance of the SML. The main path performance of MPE functions is improved by 10.61% on average and overall performance by 5.40%. The main path performance of CPE functions is improved by 5.72% on average and overall performance by 2.98%. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature 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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              Text: Mar2022
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