An Effective Genetic Algorithm for Mixed Precision
The precision of floating-point numbers is a critical task in high-performance computing. Many scientific applications rely on floating-point arithmetic, but excessive precision can lead to unnecessary computational overhead. Reducing precision may introduce unacceptable errors. Addressing this trad...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10948503/ |
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| author | Wanyu Zhang Yu Shang Min Tsao Yiwei Li Xiaoyu Song |
| author_facet | Wanyu Zhang Yu Shang Min Tsao Yiwei Li Xiaoyu Song |
| author_sort | Wanyu Zhang |
| collection | DOAJ |
| description | The precision of floating-point numbers is a critical task in high-performance computing. Many scientific applications rely on floating-point arithmetic, but excessive precision can lead to unnecessary computational overhead. Reducing precision may introduce unacceptable errors. Addressing this trade-off is essential for optimizing performance while ensuring numerical accuracy. In this paper, we present a genetic algorithm-based approach for tuning the precision of floating-point computations. Our method leverages algorithmic differentiation and first-order Taylor series approximation to assess the impact of precision variations efficiently. We employ stochastic partitioning algorithms with multiple precision combinations that meet the error requirements. Moreover, we present a genetic heuristic algorithm to determine the maximum number of variables that can sustain precision alterations without compromising the desired error threshold. The proposed approach is evaluated across various benchmark programs, analyzing the effects of precision tuning under increasing error thresholds. Our findings reveal that, for a majority of these programs, reducing precision through partitioning leads to significant performance enhancements, with improvements of up to 15%. |
| format | Article |
| id | doaj-art-e3d82a5c44384f59bf6dac3b07ddd48a |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e3d82a5c44384f59bf6dac3b07ddd48a2025-08-20T02:26:24ZengIEEEIEEE Access2169-35362025-01-0113627396275210.1109/ACCESS.2025.355750510948503An Effective Genetic Algorithm for Mixed PrecisionWanyu Zhang0https://orcid.org/0009-0008-9926-9961Yu Shang1Min Tsao2https://orcid.org/0009-0004-2670-3895Yiwei Li3Xiaoyu Song4https://orcid.org/0000-0002-6583-9400Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USADepartment of Electrical and Computer Engineering, Portland State University, Portland, OR, USASiemens EDA, Fremont, CA, USASiemens EDA, Fremont, CA, USADepartment of Electrical and Computer Engineering, Portland State University, Portland, OR, USAThe precision of floating-point numbers is a critical task in high-performance computing. Many scientific applications rely on floating-point arithmetic, but excessive precision can lead to unnecessary computational overhead. Reducing precision may introduce unacceptable errors. Addressing this trade-off is essential for optimizing performance while ensuring numerical accuracy. In this paper, we present a genetic algorithm-based approach for tuning the precision of floating-point computations. Our method leverages algorithmic differentiation and first-order Taylor series approximation to assess the impact of precision variations efficiently. We employ stochastic partitioning algorithms with multiple precision combinations that meet the error requirements. Moreover, we present a genetic heuristic algorithm to determine the maximum number of variables that can sustain precision alterations without compromising the desired error threshold. The proposed approach is evaluated across various benchmark programs, analyzing the effects of precision tuning under increasing error thresholds. Our findings reveal that, for a majority of these programs, reducing precision through partitioning leads to significant performance enhancements, with improvements of up to 15%.https://ieeexplore.ieee.org/document/10948503/Floating-point arithmeticalgorithmic differentiationTaylor seriesdynamic program analysismixed precisiongenetic algorithm |
| spellingShingle | Wanyu Zhang Yu Shang Min Tsao Yiwei Li Xiaoyu Song An Effective Genetic Algorithm for Mixed Precision IEEE Access Floating-point arithmetic algorithmic differentiation Taylor series dynamic program analysis mixed precision genetic algorithm |
| title | An Effective Genetic Algorithm for Mixed Precision |
| title_full | An Effective Genetic Algorithm for Mixed Precision |
| title_fullStr | An Effective Genetic Algorithm for Mixed Precision |
| title_full_unstemmed | An Effective Genetic Algorithm for Mixed Precision |
| title_short | An Effective Genetic Algorithm for Mixed Precision |
| title_sort | effective genetic algorithm for mixed precision |
| topic | Floating-point arithmetic algorithmic differentiation Taylor series dynamic program analysis mixed precision genetic algorithm |
| url | https://ieeexplore.ieee.org/document/10948503/ |
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