用于多项式系统求解的GPU上的模矩阵乘法

IF 0.4 Q4 MATHEMATICS, APPLIED ACM Communications in Computer Algebra Pub Date : 2023-06-01 DOI:10.1145/3614408.3614411
Jérémy Berthomieu, S. Graillat, Dimitri Lesnoff, Théo Mary
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引用次数: 1

摘要

求解Gröbner基阶变化的稀疏- fglm算法的瓶颈是在有限素域上的迭代矩阵-高-瘦矩阵积。我们的贡献是双重的。首先,我们将现有的纯cpu算法移植到GPU架构上,并对我们的实现进行性能分析,表明我们几乎可以实现硬件的最大理论吞吐量。其次,除了FLINT中基于gmp的实现之外,现有的仅cpu算法无法处理大于26位的素数;我们通过提出一种有效的多字矩阵积算法来克服这一限制,该算法可以处理最多35位的素数;我们在GPU上进行了基准测试。
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Modular Matrix Multiplication on GPU for Polynomial System Solving
The bottleneck of the SPARSE-FGLM algorithm for Gröbner bases change of order is an iterative matrix - tall and skinny matrix product over a finite prime field. Our contribution is twofold. First, we port existing CPU-only algorithms for matrix products over prime fields to GPU architectures, and carry out a performance analysis of our implementation that shows that we can nearly achieve the maximum theoretical throughput of the hardware. Second, existing CPU-only algorithms could not handle primes with more than 26 bits, other than the GMP-based implementation in FLINT; we overcome this limitation by proposing an efficient multiword matrix product algorithm that can deal with primes with at most 35 bits; we benchmarked it on GPU.
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