GPU-Resident Gaussian Process Regression Leveraging Asynchronous Tasks with HPX
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912920396365824 |
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| author | Möllmann, Henrik Pflüger, Dirk Strack, Alexander |
| author_facet | Möllmann, Henrik Pflüger, Dirk Strack, Alexander |
| contents | Gaussian processes (GPs) are a widely used regression tool, but the cubic complexity of exact solvers limits their scalability. To address this challenge, we extend the GPRat library by incorporating a fully GPU-resident GP prediction pipeline. GPRat is an HPX-based library that combines task-based parallelism with an intuitive Python API.
We implement tiled algorithms for the GP prediction using optimized CUDA libraries, thereby exploiting massive parallelism for linear algebra operations. We evaluate the optimal number of CUDA streams and compare the performance of our GPU implementation to the existing CPU-based implementation. Our results show the GPU implementation provides speedups for datasets larger than 128 training samples. We observe speedups of up to 4.3 for the Cholesky decomposition itself and 4.6 for the GP prediction. Furthermore, combining HPX with multiple CUDA streams allows GPRat to match, and for large datasets, surpass cuSOLVER's performance by up to 11 percent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19683 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GPU-Resident Gaussian Process Regression Leveraging Asynchronous Tasks with HPX Möllmann, Henrik Pflüger, Dirk Strack, Alexander Distributed, Parallel, and Cluster Computing Gaussian processes (GPs) are a widely used regression tool, but the cubic complexity of exact solvers limits their scalability. To address this challenge, we extend the GPRat library by incorporating a fully GPU-resident GP prediction pipeline. GPRat is an HPX-based library that combines task-based parallelism with an intuitive Python API. We implement tiled algorithms for the GP prediction using optimized CUDA libraries, thereby exploiting massive parallelism for linear algebra operations. We evaluate the optimal number of CUDA streams and compare the performance of our GPU implementation to the existing CPU-based implementation. Our results show the GPU implementation provides speedups for datasets larger than 128 training samples. We observe speedups of up to 4.3 for the Cholesky decomposition itself and 4.6 for the GP prediction. Furthermore, combining HPX with multiple CUDA streams allows GPRat to match, and for large datasets, surpass cuSOLVER's performance by up to 11 percent. |
| title | GPU-Resident Gaussian Process Regression Leveraging Asynchronous Tasks with HPX |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2602.19683 |