CJS
In: Proceedings of the 9th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, 2018-06-20
Online
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Zugriff:
The classic Jacobi method, widely used for solving linear systems, is slow, especially when dealing with large matrices. This paper proposes a Custom Jacobi Solver (CJS) for large-scale linear systems. It is based on a column-wise Jacobi step operation which allows for increased dependence distance, enabling deep pipelining. Our solver allows customisation at run time between the classic Jacobi method and its more convergence efficient-counterpart, the weighted Jacobi method. It can be dynamically scaled to multiple FPGAs by appropriately partitioning the matrix data among the FPGAs. After evaluating our solver on a number of different datasets, CJS proves to be up to 71 times faster when comparing an 8-FPGA solution with a 12-core CPU C++ implementation.
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CJS
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Autor/in / Beteiligte Person: | Luk, Wayne ; Guo, Liucheng ; Salmon, Mark ; Cross, Andreea-Ingrid |
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Zeitschrift: | Proceedings of the 9th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, 2018-06-20 |
Veröffentlichung: | ACM, 2018 |
Medientyp: | unknown |
DOI: | 10.1145/3241793.3241802 |
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