FSCHOL: An OpenCL-based HPC Framework for Accelerating Sparse Cholesky Factorization on FPGAs
The proposed FSCHOL framework consists of an FPGA kernel implementing a throughput-optimized hardware architecture for accelerating the supernodal multifrontal algorithm for sparse Cholesky factorization and a host program implementing a novel scheduling algorithm for finding the optimal execution o...
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| Veröffentlicht in: | Proceedings (Symposium on Computer Architecture and High Performance Computing) S. 209 - 220 |
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| Hauptverfasser: | , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.10.2021
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| Schlagworte: | |
| ISSN: | 2643-3001 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | The proposed FSCHOL framework consists of an FPGA kernel implementing a throughput-optimized hardware architecture for accelerating the supernodal multifrontal algorithm for sparse Cholesky factorization and a host program implementing a novel scheduling algorithm for finding the optimal execution order of supernodes computations for an elimination tree on the FPGA to eliminate the need for off-chip memory access for storing intermediate results. Moreover, the proposed scheduling algorithm minimizes on-chip memory requirements for buffering intermediate results by resolving the dependency of parent nodes in an elimination tree through temporal parallelism. Experiment results for factorizing a set of sparse matrices in various sizes from SuiteSparse Matrix Collection show that the proposed FSCHOL implemented on an Intel Stratix 10 GX FPGA development board achieves on average 5.5× and 9.7× higher performance and 10.3× and 24.7× lower energy consumption than implementations of CHOLMOD on an Intel Xeon E5-2637 CPU and an NVIDIA V100 GPU, respectively. |
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| ISSN: | 2643-3001 |
| DOI: | 10.1109/SBAC-PAD53543.2021.00032 |