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High-performance conjugate gradient benchmark: A comprehensive survey.

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Title: High-performance conjugate gradient benchmark: A comprehensive survey.
Authors: Rizwan, Muhammad1,2 (AUTHOR), Kim, Imbunm1 (AUTHOR), Choi, Jaeyoung1 (AUTHOR) choi@ssu.ac.kr
Source: International Journal of High Performance Computing Applications. May2026, Vol. 40 Issue 3, p291-321. 31p.
Subjects: High performance computing, Benchmark problems (Computer science), Computer architecture, Supercomputers, Conjugate gradient methods, Parallel algorithms, Mathematical optimization
Abstract: This survey paper focuses on examining the optimization techniques and trends for the High-Performance Conjugate Gradient (HPCG) benchmark employed in the last 10 years. The HPCG benchmark was introduced to eliminate the limitations of the High-Performance Linpack (HPL) benchmark and reflect the realistic performance measure of modern supercomputer architectures. Our study evaluates HPCG optimizations performed by High-Performance Computing (HPC) researchers on diverse hardware architectures such as CPU, GPU, MIC, and FPGA, etc., with a focus on optimizing the reference HPCG benchmark code for data formats, parallelization strategies, and architecture-specific tuning. We reviewed the optimizations performed by the researchers and presented a comprehensive analysis of these optimizations. This work offers the first comprehensive review of HPCG optimizations, aiming to discuss the previous findings and provide a systematic analysis for further optimizations in the future. Our study aims to guide researchers in identifying the most suitable directions to expand their knowledge and develop further optimization strategies in the HPCG benchmark. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of High Performance Computing Applications is the property of Sage Publications Inc. 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
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+High+Performance+Computing+Applications%22">International Journal of High Performance Computing Applications</searchLink>. May2026, Vol. 40 Issue 3, p291-321. 31p.
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  Data: This survey paper focuses on examining the optimization techniques and trends for the High-Performance Conjugate Gradient (HPCG) benchmark employed in the last 10 years. The HPCG benchmark was introduced to eliminate the limitations of the High-Performance Linpack (HPL) benchmark and reflect the realistic performance measure of modern supercomputer architectures. Our study evaluates HPCG optimizations performed by High-Performance Computing (HPC) researchers on diverse hardware architectures such as CPU, GPU, MIC, and FPGA, etc., with a focus on optimizing the reference HPCG benchmark code for data formats, parallelization strategies, and architecture-specific tuning. We reviewed the optimizations performed by the researchers and presented a comprehensive analysis of these optimizations. This work offers the first comprehensive review of HPCG optimizations, aiming to discuss the previous findings and provide a systematic analysis for further optimizations in the future. Our study aims to guide researchers in identifying the most suitable directions to expand their knowledge and develop further optimization strategies in the HPCG benchmark. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of High Performance Computing Applications is the property of Sage Publications Inc. 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193487888
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        Value: 10.1177/10943420251393314
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      – Code: eng
        Text: English
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        PageCount: 31
        StartPage: 291
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      – SubjectFull: High performance computing
        Type: general
      – SubjectFull: Benchmark problems (Computer science)
        Type: general
      – SubjectFull: Computer architecture
        Type: general
      – SubjectFull: Supercomputers
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      – SubjectFull: Conjugate gradient methods
        Type: general
      – SubjectFull: Parallel algorithms
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      – SubjectFull: Mathematical optimization
        Type: general
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      – TitleFull: High-performance conjugate gradient benchmark: A comprehensive survey.
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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