The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
GPU Energy Consumption Optimization With a Global-Based Neural Network Method
Open accessAbstract
With the widespread use of smart technologies, graphics processing unit (GPU) power-optimization issues are becoming increasingly important. Many researchers have tried to use dynamic voltage and frequency scaling (DVFS) technology to optimize a GPU's internal energy consumption. However, DVFS energy management often has difficulty balancing GPU performance and energy efficiency. This paper aims to implement a DVFS energy management strategy. We constructed a new type of neural network to a GPU-based energy management scheme, implemented the global-based DVFS model, and explored its implementation details. Using a master-slave model, we built a global energy control solution strategy. This strategy performs global collaborative DVFS adjustments on the GPU's energy consumption module based on task characteristics. Through the software construction and implementation of the global-based DVFS model, we proved that the strategy improves the GPU performance while improving the GPU's energy efficiency. We conducted performance and energy tests on three GPUs on the Tesla, Fermi, and Kepler platforms. The experiments showed that this strategy improved the performance and power consumption of GPUs based on each of the platforms.
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“standard CPUs consume generally up to 85 watts and standard GPUs consume between 200 watts to 500 watts [11] [12].”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [12]
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Parboil: A Revised Benchmark Suite for Scientific and Commercial Throughput Computing
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A survey and measurement study of GPU DVFS on energy conservation
Mei, X., Wang, Q., & Chu, X. (2016) · Digital Communications and Networks · doi:10.1016/j.dcan.2016.10.001 · Cited by 100
Efficient GPU Spatial-Temporal Multitasking
Liang, Y. E., Huynh, H. P., Rupnow, K., Goh, R. S. M., & Chen, D. (2014) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2014.2313342 · Cited by 99
A measurement study of GPU DVFS on energy conservation
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Power and Performance Characterization and Modeling of GPU-Accelerated Systems
Abe, Y., Sasaki, H., Kato, S., Inoue, K., Edahiro, M., & Peres, M. (2014) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2014.23 · Cited by 76
Racing and Pacing to Idle: Theoretical and Empirical Analysis of Energy Optimization Heuristics
Kim, D. H. K., Imes, C., & Hoffmann, H. (2015) · IEEE International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA) · doi:10.1109/cpsna.2015.23 · Cited by 52
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Energy, Power, and Performance Characterization of GPGPU Benchmark Programs
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A Modeling Approach for Energy Saving Based on GA-BP Neural Network
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Self-citation
Research works printed but not linked: 3
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Cited by
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
GPGPU Performance Estimation With Core and Memory Frequency Scaling
Wang, Q., & Chu, X. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3004623 · Cited by 54
Wang, Q., Mei, X., Liu, H., Leung, Y.-W., Li, Z., & Chu, X. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3181096 · Cited by 47
Krzywaniak, A., Czarnul, P., & Proficz, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08751-6_48 · Cited by 25
GPU-Based Embedded Intelligence Architectures and Applications
Ang, L.-M., & Seng, K. P. (2021) · Electronics · doi:10.3390/electronics10080952 · Cited by 24
Metadata
- Authors
- Huang, Y., Guo, B., & Shen, Y.
- Affiliations
- Sichuan University (CN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 64303-64314
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2943912161
- Cited by
- 22
- References
- 15
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 4
- FWCI
- 2.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- State Key Program of National Natural Science Foundation of China (61332001)
- National Natural Science Foundation of China (61772352, 61472050)
- Science and Technology Planning Project of Sichuan Province (2018ZDZX0010, 2017GZDZX0003)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- GPU power consumption, dynamic voltage and frequency scaling, energy management, master-slave control, energy-performance trade-off, power consumption
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