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An object-oriented bulk synchronous parallel library for multicore programming

机译:用于多核编程的面向对象的批量同步并行库

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We show that the bulk synchronous parallel (BSP) model, originally designed for distributed-memory systems, is also applicable for shared-memory multicore systems and, furthermore, that BSP libraries are useful in scientific computing on these systems. A proof-of-concept MulticoreBSP library has been implemented in Java, and is used to show that BSP algorithms can attain proper speedups on multicore architectures. This library is based on the BSPlib implementation, adapted to an object-oriented setting. In comparison, the number of function primitives is reduced, while the overall design simplicity is improved. We detail applying the BSP model and library on the sparse matrix-vector (SpMV) multiplication problem, and show by performing numerical experiments that the resulting BSP SpMV algorithm attains speedups, in one case reaching a speedup of 3.5 for 4 threads. Whereas not described in detail in this paper, algorithms for the fast Fourier transform and the dense LU decomposition are also investigated; in one case, attaining super-linear speedups of 5 for 4 threads. The predictability of BSP algorithms in the case of the SpMV is also investigated.
机译:我们表明,最初为分布式内存系统设计的批量同步并行(BSP)模型也适用于共享内存多核系统,此外,BSP库可用于在这些系统上进行科学计算。概念验证MulticoreBSP库已用Java实现,用于显示BSP算法可以在多核体系结构上获得适当的加速。该库基于BSPlib实现,适用于面向对象的设置。相比之下,功能基元的数量减少了,而总体设计的简洁性得到了提高。我们详细介绍了BSP模型和库在稀疏矩阵矢量(SpMV)乘法问题上的应用,并通过进行数值实验表明,所得的BSP SpMV算法实现了加速,在一种情况下,四个线程的加速达到3.5。尽管本文没有详细描述,但也研究了快速傅里叶变换和密集LU分解的算法。在一种情况下,实现4个线程的5的超线性加速。还研究了在SpMV情况下BSP算法的可预测性。

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