基于KNL众核处理器平台的并行矩量法性能优化
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摘要
基于Intel第二代Xeon Phi代号为Knights Landing(KNL)众核处理器平台,利用MPI+OpenMP混合编程策略对并行矩量法(Method of Moments, MoM)进行了优化.利用OpenMP编程技术和KNL的计算资源,提高了CPU(Center Processing Unit)使用率;线程的引入,大幅度减少了矩阵填充过程中进程间的冗余积分;为发挥KNL的512位矢量宽度优势,通过向量化优化进一步提高了循环结构的执行效率;对计算密集型、CPU利用率高的矩阵求解过程,通过引入的OpenMP编程策略,减少了MPI(Message Passing Interface)通信时间,加速了求解.数值结果表明,通过在KNL众核处理器平台上的优化,可以极大地提升矩量法计算复杂电磁问题的效率.
The parallel method of moments(MoM) is successfully optimized using the MPI+OpenMP hybrid programming strategy, based on the second-generation Intel Xeon Phi many-core processor platform, codenamed Knights Landing(KNL). Using OpenMP programming technology, the utilization rate of the CPU(Center Processing Unit) is increased, and the computing resources of KNL are fully utilized. The introduction of threads substantially reduces the inter-process redundant integrals in the filling matrix process. In order to give full play to the advantage of KNL's 512-bit vector width, the efficiency of the loop structure is further enhanced through vector optimization. For the matrix solution process, which typically requires intensive computation and high CPU utilization, MPI(Message Passing Interface) communication time is reduced and the solution process is accelerated by introducing an OpenMP programming strategy.Numerical results show that the efficiency of solving complex electromagnetic problems by parallel MoM is greatly improved through optimization on the KNL many-core processor platform.
The parallel method of moments(MoM) is successfully optimized using the MPI+OpenMP hybrid programming strategy, based on the second-generation Intel Xeon Phi many-core processor platform, codenamed Knights Landing(KNL). Using OpenMP programming technology, the utilization rate of the CPU(Center Processing Unit) is increased, and the computing resources of KNL are fully utilized. The introduction of threads substantially reduces the inter-process redundant integrals in the filling matrix process. In order to give full play to the advantage of KNL's 512-bit vector width, the efficiency of the loop structure is further enhanced through vector optimization. For the matrix solution process, which typically requires intensive computation and high CPU utilization, MPI(Message Passing Interface) communication time is reduced and the solution process is accelerated by introducing an OpenMP programming strategy.Numerical results show that the efficiency of solving complex electromagnetic problems by parallel MoM is greatly improved through optimization on the KNL many-core processor platform.
引文
[1] CHEN Y, ZHANG G, LIN Z, et al. Solution of EM problems using hybrid parallel MIC/CPU implementation of higher-order MoM[C]//IEEE, International Symposium on Microwave, Antenna, Propagation, and Emc Technologies. IEEE, 2016:789-791.
[2]张光辉.CPU/MIC异构平台中矩量法与时域有限差分法的研究[D].西安:西安电子科技大学,2015.
[3]左胜,陈岩,张玉,等.一种可扩展异构并行核外高阶矩量法[J].西安电子科技大学学报(自然科学版),2017, 44(1):146-151.
[4]赖明澈.数据并行协处理器体系结构的研究与实现[D].长沙:国防科学技术大学,2005.
[5] HARRINGTON R F, HARRINGTON J L. Field Computation by Moment Methods[M]. New York:Oxford University Press, 1996.
[6] ZHANG Y, SARKAR T K. Parallel Solution of Integral Equation Based EM Problems in the Frequency Domain[M]. Hoboken, NJ:Wiley-IEEE Press, 2009.
[7]RAO S M, WILTON D R, GLISSON A W. Electromagnetic scattering by surfaces of arbitrary shape[J]. IEEE Transactions on Antennas&Propagation, 1982, 30(3):409-418.
[8]张玉,赵勋旺,陈岩,等.计算电磁学中的超大规模并行矩量法[M].西安:西安电子科技大学出版社,2016.
[9] RANA V S, LIN M, CHAPMAN B. A scalable task parallelism approach for LU decomposition with multicore CPUs[C]//Proceedings of the 2nd Internationsl Workshop on Extreme Scale Programming Models and Middleware. Piscataway, NJ, USA:IEEE Press, 2016:17-23.
[10] ZHANG G, CHEN Y, ZHANG Y, et al. MIC accelerated LU decomposition for method of moments[C]//IEEE International Symposium on Antennas and Propagation&Usnc/ursi National Radio Science Meeting. IEEE,2015:756-757.
[11] JEFFERS J, REINDERS J. Intel Xeon Phi协处理器高性能编程指南[M].陈健,李慧,杨昆,等,译.北京:人民邮电出版社,2014.
[12]高伟,赵荣彩,韩林,等.SIMD自动向量化编译优化概述[J].软件学报,2015, 26(6):1265-1284.
[13]周领良,朱延超,刘轶,等.基于Cache命中率校准的并行程序性能预测[C]//2014全国高性能计算学术年会论文集.中国计算机学会,2015:814-817.
[14]艾维丽.浅析Cache命中率与块的大小之间的关系[J].价值工程,2011,32:153.
[15]叶凝,应忍冬,朱新忠,等.众核处理器系统可靠性优化方案[J].计算机与现代化,2013, 218(10):143-148.
[16] MIWA M, NAKASHIMA K. Progression of MPI Non-blocking Collective Operations Using Hyper-Threading[C]//2015 23rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing(PDP). IEEE, 2015:163-171.
[17] QUN N H, KHALIB Z I A, WARIP M N, et al. Hyper-threading technology:Not a good choice for speeding up CPU-bound code[C]//International Conference on Electronic Design. IEEE, 2017:578-581.
[18] RAJESH N, MALATHI K, RAJU S, et al. Design of vivaldi antenna with wideband radar cross section reduction[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(4):2102-2105.
[19] HU C F, LI N J, CHEN W J, et al. High-precision RCS measurement of aircraft's weak scattering source[J].Chinese Journal of Aeronautics, 2016, 29(3):772-778.
[2]张光辉.CPU/MIC异构平台中矩量法与时域有限差分法的研究[D].西安:西安电子科技大学,2015.
[3]左胜,陈岩,张玉,等.一种可扩展异构并行核外高阶矩量法[J].西安电子科技大学学报(自然科学版),2017, 44(1):146-151.
[4]赖明澈.数据并行协处理器体系结构的研究与实现[D].长沙:国防科学技术大学,2005.
[5] HARRINGTON R F, HARRINGTON J L. Field Computation by Moment Methods[M]. New York:Oxford University Press, 1996.
[6] ZHANG Y, SARKAR T K. Parallel Solution of Integral Equation Based EM Problems in the Frequency Domain[M]. Hoboken, NJ:Wiley-IEEE Press, 2009.
[7]RAO S M, WILTON D R, GLISSON A W. Electromagnetic scattering by surfaces of arbitrary shape[J]. IEEE Transactions on Antennas&Propagation, 1982, 30(3):409-418.
[8]张玉,赵勋旺,陈岩,等.计算电磁学中的超大规模并行矩量法[M].西安:西安电子科技大学出版社,2016.
[9] RANA V S, LIN M, CHAPMAN B. A scalable task parallelism approach for LU decomposition with multicore CPUs[C]//Proceedings of the 2nd Internationsl Workshop on Extreme Scale Programming Models and Middleware. Piscataway, NJ, USA:IEEE Press, 2016:17-23.
[10] ZHANG G, CHEN Y, ZHANG Y, et al. MIC accelerated LU decomposition for method of moments[C]//IEEE International Symposium on Antennas and Propagation&Usnc/ursi National Radio Science Meeting. IEEE,2015:756-757.
[11] JEFFERS J, REINDERS J. Intel Xeon Phi协处理器高性能编程指南[M].陈健,李慧,杨昆,等,译.北京:人民邮电出版社,2014.
[12]高伟,赵荣彩,韩林,等.SIMD自动向量化编译优化概述[J].软件学报,2015, 26(6):1265-1284.
[13]周领良,朱延超,刘轶,等.基于Cache命中率校准的并行程序性能预测[C]//2014全国高性能计算学术年会论文集.中国计算机学会,2015:814-817.
[14]艾维丽.浅析Cache命中率与块的大小之间的关系[J].价值工程,2011,32:153.
[15]叶凝,应忍冬,朱新忠,等.众核处理器系统可靠性优化方案[J].计算机与现代化,2013, 218(10):143-148.
[16] MIWA M, NAKASHIMA K. Progression of MPI Non-blocking Collective Operations Using Hyper-Threading[C]//2015 23rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing(PDP). IEEE, 2015:163-171.
[17] QUN N H, KHALIB Z I A, WARIP M N, et al. Hyper-threading technology:Not a good choice for speeding up CPU-bound code[C]//International Conference on Electronic Design. IEEE, 2017:578-581.
[18] RAJESH N, MALATHI K, RAJU S, et al. Design of vivaldi antenna with wideband radar cross section reduction[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(4):2102-2105.
[19] HU C F, LI N J, CHEN W J, et al. High-precision RCS measurement of aircraft's weak scattering source[J].Chinese Journal of Aeronautics, 2016, 29(3):772-778.