Communication-reduced Conjugate Gradient Variants for GPU-accelerated Clusters
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arXiv
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| Format: | Preprint |
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2025
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| author | Bernaschi, Massimo Carrozzo, Mauro G. Celestini, Alessandro Piperno, Giacomo D'Ambra, Pasqua |
| author_facet | Bernaschi, Massimo Carrozzo, Mauro G. Celestini, Alessandro Piperno, Giacomo D'Ambra, Pasqua |
| contents | Linear solvers are key components in any software platform for scientific and engineering computing. The solution of large and sparse linear systems lies at the core of physics-driven numerical simulations relying on partial differential equations (PDEs) and often represents a significant bottleneck in datadriven procedures, such as scientific machine learning. In this paper, we present an efficient implementation of the preconditioned s-step Conjugate Gradient (CG) method, originally proposed by Chronopoulos and Gear in 1989, for large clusters of Nvidia GPU-accelerated computing nodes. The method, often referred to as communication-reduced or communication-avoiding CG, reduces global synchronizations and data communication steps compared to the standard approach, enhancing strong and weak scalability on parallel computers. Our main contribution is the design of a parallel solver that fully exploits the aggregation of low-granularity operations inherent to the s-step CG method to leverage the high throughput of GPU accelerators. Additionally, it applies overlap between data communication and computation in the multi-GPU sparse matrix-vector product. Experiments on classic benchmark datasets, derived from the discretization of the Poisson PDE, demonstrate the potential of the method. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_03743 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Communication-reduced Conjugate Gradient Variants for GPU-accelerated Clusters Bernaschi, Massimo Carrozzo, Mauro G. Celestini, Alessandro Piperno, Giacomo D'Ambra, Pasqua Numerical Analysis 65F10, 65Y05 G.4 Linear solvers are key components in any software platform for scientific and engineering computing. The solution of large and sparse linear systems lies at the core of physics-driven numerical simulations relying on partial differential equations (PDEs) and often represents a significant bottleneck in datadriven procedures, such as scientific machine learning. In this paper, we present an efficient implementation of the preconditioned s-step Conjugate Gradient (CG) method, originally proposed by Chronopoulos and Gear in 1989, for large clusters of Nvidia GPU-accelerated computing nodes. The method, often referred to as communication-reduced or communication-avoiding CG, reduces global synchronizations and data communication steps compared to the standard approach, enhancing strong and weak scalability on parallel computers. Our main contribution is the design of a parallel solver that fully exploits the aggregation of low-granularity operations inherent to the s-step CG method to leverage the high throughput of GPU accelerators. Additionally, it applies overlap between data communication and computation in the multi-GPU sparse matrix-vector product. Experiments on classic benchmark datasets, derived from the discretization of the Poisson PDE, demonstrate the potential of the method. |
| title | Communication-reduced Conjugate Gradient Variants for GPU-accelerated Clusters |
| topic | Numerical Analysis 65F10, 65Y05 G.4 |
| url | https://arxiv.org/abs/2501.03743 |