Improving the Performance of Dynamical Simulations Via Multiple Right-Hand Sides
Improving the Performance of Dynamical Simulations Via Multiple Right-Hand Sides
复制标题
通过多个右侧提高动态仿真的性能
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
M. Smelyanskiy
中科院分区：
文献类型：
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作者：
Xing Liu;Edmond Chow;K. Vaidyanathan;M. Smelyanskiy
本文提出了一种算法方法，用于提高许多类型的随机动力学模拟的性能。该方法是重新设计现有的算法，这些算法使用稀疏的矩阵矢量产物（SPMV）与单个矢量相反，使用更有效的内核，广义SPMV（GSPMV），该元素同时使用多个向量计算。在本文中，我们展示了如何重新设计动态仿真以以最初并不明显的方式利用GSPMV，因为一次只有一个向量。我们研究GSPMV的性能是向量数量的函数，并证明了在Stokesian动力学方法中使用GSPMV来模拟细胞中大分子的运动。具体而言，对于我们的应用程序，我们发现，使用现代的多功能Intel微处理器，最多可容纳64个节点，我们通常只能在乘以单个向量所需的时间两倍的时间中乘以8至16个向量。重新设计Stokesian Dynamics算法以利用GSPMV，我们在单节点，数据并行模拟中测量了30％的性能加速。
This paper presents an algorithmic approach for improving the performance of many types of stochastic dynamical simulations. The approach is to redesign existing algorithms that use sparse matrix-vector products (SPMV) with single vectors to instead use a more efficient kernel, the generalized SPMV (GSPMV), which computes with multiple vectors simultaneously. In this paper, we show how to redesign a dynamical simulation to exploit GSPMV in way that is not initially obvious because only one vector is available at a time. We study the performance of GSPMV as a function of the number of vectors, and demonstrate the use of GSPMV in the Stokesian dynamics method for the simulation of the motion of macromolecules in the cell. Specifically, for our application, we find that with modern multicore Intel microprocessors in clusters of up to 64 nodes, we can typically multiply by 8 to 16 vectors in only twice the time required to multiply by a single vector. After redesigning the Stokesian dynamics algorithm to exploit GSPMV, we measure a 30 percent speedup in performance in single-node, data parallel simulations.