Improving Energy Saving of One-Sided Matrix Decompositions on CPU-GPU Heterogeneous Systems Article Swipe
Jieyang Chen
,
Xin Liang
,
Kai Zhao
,
Hadi Zamani Sabzi
,
Laxmi N. Bhuyan
,
Zizhong Chen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3572848.3577496
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3572848.3577496
One-sided dense matrix decompositions (e.g., Cholesky, LU, and QR) are the key components in scientific computing in many different fields. Although their design has been highly optimized for modern processors, they still consume a considerable amount of energy. As CPU-GPU heterogeneous systems are commonly used for matrix decompositions, in this work, we aim to further improve the energy saving of one-sided matrix decompositions on CPU-GPU heterogeneous systems. We first build an Algorithm-Based Fault Tolerance protected overclocking technique (ABFT-OC) to enable us to exploit reliable overclocking for key matrix decomposition operations. Then, we design an energy-saving matrix decomposition framework, Bi-directional Slack Reclamation(BSR), that can intelligently combine the capability provided by ABFT-OC and DVFS to maximize energy saving and maintain performance and reliability. Experiments show that BSR is able to save up to 11.7% more energy compared with the current best energy saving optimization approach with no performance degradation and up to 14.1% Energy * Delay^2 reduction. Also, BSR enables the Pareto efficient performance-energy trade-off, which is able to provide up to 1.43x performance improvement without costing extra energy.
Related Topics
Concepts
Computer science
Cholesky decomposition
Key (lock)
Energy (signal processing)
Parallel computing
Energy consumption
QR decomposition
Matrix (chemical analysis)
Reliability (semiconductor)
Efficient energy use
Supercomputer
Embedded system
Distributed computing
Operating system
Engineering
Statistics
Power (physics)
Materials science
Physics
Composite material
Eigenvalues and eigenvectors
Quantum mechanics
Electrical engineering
Mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1145/3572848.3577496
- OA Status
- green
- Cited By
- 4
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4315588752
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4315588752Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3572848.3577496Digital Object Identifier
- Title
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Improving Energy Saving of One-Sided Matrix Decompositions on CPU-GPU Heterogeneous SystemsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-02-21Full publication date if available
- Authors
-
Jieyang Chen, Xin Liang, Kai Zhao, Hadi Zamani Sabzi, Laxmi N. Bhuyan, Zizhong ChenList of authors in order
- Landing page
-
https://doi.org/10.1145/3572848.3577496Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2301.03166Direct OA link when available
- Concepts
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Computer science, Cholesky decomposition, Key (lock), Energy (signal processing), Parallel computing, Energy consumption, QR decomposition, Matrix (chemical analysis), Reliability (semiconductor), Efficient energy use, Supercomputer, Embedded system, Distributed computing, Operating system, Engineering, Statistics, Power (physics), Materials science, Physics, Composite material, Eigenvalues and eigenvectors, Quantum mechanics, Electrical engineering, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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