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PDCAT
2009
Springer

Balanced Dense Polynomial Multiplication on Multi-Cores

14 years 5 months ago
Balanced Dense Polynomial Multiplication on Multi-Cores
Abstract— In symbolic computation, polynomial multiplication is a fundamental operation akin to matrix multiplication in numerical computation. We present efficient implementation strategies for FFT-based dense polynomial multiplication targeting multi-cores. We show that balanced input data can maximize parallel speedup and minimize cache complexity for bivariate multiplication. However, unbalanced input data, which are common in symbolic computation, are challenging. We provide efficient techniques, what we call contraction and extension, to reduce multivariate (and univariate) multiplication to balanced bivariate multiplication. Our implementation in Cilk++ demonstrates good speedup on multi-cores. Keywords- parallel symbolic computation; parallel polynomial multiplication; parallel multi-dimensional FFT/TFT; Cilk++; multi-core;
Marc Moreno Maza, Yuzhen Xie
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where PDCAT
Authors Marc Moreno Maza, Yuzhen Xie
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