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Explicit Fourth-Order Runge-Kutta Method on Intel Xeon Phi Coprocessor

机译:英特尔至强融核协处理器上的显式四阶Runge-Kutta方法

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This paper concerns an Intel Xeon Phi implementation of the explicit fourth-order Runge-Kutta method (RK4) for very sparse matrices with very short rows. Such matrices arise during Markovian modeling of computer and telecommunication networks. In this work an implementation based on Intel Math Kernel Library (Intel MKL) routines and the authors' own implementation, both using the CSR storage scheme and working on Intel Xeon Phi, were investigated. The implementation based on the Intel MKL library uses the high-performance BLAS and Sparse BLAS routines. In our application we focus on OpenMP style programming. We implement SpMV operation and vector addition using the basic optimizing techniques and the vectorization. We evaluate our approach in native and offload modes for various number of cores and thread allocation affinities. Both implementations (based on Intel MKL and made by the authors) were compared in respect of the time, the speedup and the performance. The numerical experiments on Intel Xeon Phi show that the performance of authors' implementation is very promising and gives a gain of up to two times compared to the multithreaded implementation (based on Intel MKL) running on CPU (Intel Xeon processor) and even three times in comparison with the application which uses Intel MKL on Intel Xeon Phi.
机译:本文涉及显式四阶Runge-Kutta方法(RK4)的Intel Xeon Phi实现,该方法适用于行数非常短的稀疏矩阵。这种矩阵是在计算机和电信网络的马尔可夫模型期间产生的。在这项工作中,研究了基于Intel Math Kernel Library(Intel MKL)例程的实现以及作者自己的实现,均使用CSR存储方案并在Intel Xeon Phi上工作。基于Intel MKL库的实现使用高性能BLAS和稀疏BLAS例程。在我们的应用程序中,我们专注于OpenMP风格的编程。我们使用基本的优化技术和向量化技术来实现SpMV运算和向量加法。我们针对各种数量的内核和线程分配亲和力,在本机模式和卸载模式下评估了我们的方法。比较了两种实现方式(基于英特尔MKL并由作者完成),在时间,加速和性能方面进行了比较。在Intel Xeon Phi上进行的数值实验表明,作者实现的性能非常有前途,与运行在CPU(Intel Xeon处理器)上的多线程实现(基于Intel MKL)相比,最多可获得两倍的收益。与在Intel Xeon Phi上使用Intel MKL的应用程序相比。

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