Time–Energy Correlation for Multithreaded Matrix Factorizations
The relationship between time and energy is an important aspect related to energy savings in modern multicore architectures. In this paper, we investigated and analyzed the correlation between time and energy. We compared the execution time and energy consumption of the LU factorization algorithms (...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/17/6290 |
| _version_ | 1797582632138047488 |
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| author | Beata Bylina Monika Piekarz |
| author_facet | Beata Bylina Monika Piekarz |
| author_sort | Beata Bylina |
| collection | DOAJ |
| description | The relationship between time and energy is an important aspect related to energy savings in modern multicore architectures. In this paper, we investigated and analyzed the correlation between time and energy. We compared the execution time and energy consumption of the LU factorization algorithms (versions with and without pivoting) and Cholesky with the Math Kernel Library (MKL) on a multicore machine. To reduce the energy of these multithreaded factorizations, the Dynamic Voltage and Frequency Scaling (DVFS) technique was used. This technique allows the clock frequency to be scaled without changing the implementation. In particular, we studied the correlations between time and energy using two metrics: Energy Delay Product (EDP) and Greenup, Powerup, and Speedup (GPS-UP). An experimental evaluation was performed on an Intel Xeon Gold multicore machine as a function of the number of threads and the clock speed. Our test results showed that scalability in terms of execution time, expressed by the Speedup metric, had values close to a linear function as the number of threads increased. In contrast, the scalability in terms of energy consumption, expressed by the Greenup metric, had values close to a logarithmic function as the number of threads increased. The use of the EDP and GPS-UP metrics allowed us to evaluate the impact of the optimized code (DVFS and increase in the number of threads) on the time and energy consumption and to determine a better green category representing energy savings without losing performance. |
| first_indexed | 2024-03-10T23:24:08Z |
| format | Article |
| id | doaj.art-e0e18eedba3344cebb7386d509a80f12 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T23:24:08Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e0e18eedba3344cebb7386d509a80f122023-11-19T08:05:54ZengMDPI AGEnergies1996-10732023-08-011617629010.3390/en16176290Time–Energy Correlation for Multithreaded Matrix FactorizationsBeata Bylina0Monika Piekarz1Institute of Computer Science, Maria Curie-Sklodowska University, Pl. M. Curie-Skłodowskiej 5, 20-031 Lublin, PolandInstitute of Computer Science, Maria Curie-Sklodowska University, Pl. M. Curie-Skłodowskiej 5, 20-031 Lublin, PolandThe relationship between time and energy is an important aspect related to energy savings in modern multicore architectures. In this paper, we investigated and analyzed the correlation between time and energy. We compared the execution time and energy consumption of the LU factorization algorithms (versions with and without pivoting) and Cholesky with the Math Kernel Library (MKL) on a multicore machine. To reduce the energy of these multithreaded factorizations, the Dynamic Voltage and Frequency Scaling (DVFS) technique was used. This technique allows the clock frequency to be scaled without changing the implementation. In particular, we studied the correlations between time and energy using two metrics: Energy Delay Product (EDP) and Greenup, Powerup, and Speedup (GPS-UP). An experimental evaluation was performed on an Intel Xeon Gold multicore machine as a function of the number of threads and the clock speed. Our test results showed that scalability in terms of execution time, expressed by the Speedup metric, had values close to a linear function as the number of threads increased. In contrast, the scalability in terms of energy consumption, expressed by the Greenup metric, had values close to a logarithmic function as the number of threads increased. The use of the EDP and GPS-UP metrics allowed us to evaluate the impact of the optimized code (DVFS and increase in the number of threads) on the time and energy consumption and to determine a better green category representing energy savings without losing performance.https://www.mdpi.com/1996-1073/16/17/6290energytimemulticore architectureDynamic Voltage Frequency Scaling (DVFS)LU factorizationCholesky factorization |
| spellingShingle | Beata Bylina Monika Piekarz Time–Energy Correlation for Multithreaded Matrix Factorizations Energies energy time multicore architecture Dynamic Voltage Frequency Scaling (DVFS) LU factorization Cholesky factorization |
| title | Time–Energy Correlation for Multithreaded Matrix Factorizations |
| title_full | Time–Energy Correlation for Multithreaded Matrix Factorizations |
| title_fullStr | Time–Energy Correlation for Multithreaded Matrix Factorizations |
| title_full_unstemmed | Time–Energy Correlation for Multithreaded Matrix Factorizations |
| title_short | Time–Energy Correlation for Multithreaded Matrix Factorizations |
| title_sort | time energy correlation for multithreaded matrix factorizations |
| topic | energy time multicore architecture Dynamic Voltage Frequency Scaling (DVFS) LU factorization Cholesky factorization |
| url | https://www.mdpi.com/1996-1073/16/17/6290 |
| work_keys_str_mv | AT beatabylina timeenergycorrelationformultithreadedmatrixfactorizations AT monikapiekarz timeenergycorrelationformultithreadedmatrixfactorizations |