面向Peta级计算机的霍奇金-赫胥黎型神经元的神经回路模拟

Daisuke Miyamoto, T. Kazawa, R. Kanzaki
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引用次数: 6

摘要

将仿真环境“NEURON”移植并优化到K计算机上,以多室霍奇金-赫胥黎模型模拟昆虫脑。为了使用SPARC64VIIIfx (K计算机的CPU)的SIMD单元,我们交换了隔室环路和离子通道环路的顺序,并应用扇区缓存。这些调优将单核性能从340 MFLOPS/核提高到1560 MFLOPS/核(效率约为10%)。gNEURONh的尖峰交换方法(MPI_Allgather)在10000核以上的情况下需要大量的时间,简单的异步点对点方法(MPI_Isend)也不有效,因为函数调用量大,互连路径距离长。为了解决这些问题,我们采用MPI/OpenMP混合并行化来减少互连通信,并开发了一个程序来优化三维环面网络中计算节点上神经元的位置。根据这些结果,我们获得了187 TFLOPS和196,608个CPU内核。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Circuit Simulation of Hodgkin-Huxley Type Neurons Toward Peta Scale Computers
We ported and optimized simulation environment "NEURON" on K computer to simulate a insect brain as multi-compartment Hodgkin-Huxley type model. To use SIMD units of SPARC64VIIIfx (CPU of K computer), we exchanged the order of the compartment loop and the ion channel loop and apply sector caches. These tuning improved single core performance 340 MFLOPS/core to 1560 MFLOPS/core (about 10% efficiency).Spike exchange method of gNEURONh (MPI_Allgather) demands large amount of time in case of 10,000 cores or more and simple asynchronous point-to-point method (MPI_Isend) is not effective either, because of a large number of function calls and long distance of interconnect pathway. To tackle these problems, we adopted MPI/OpenMP hybrid parallelization to reduce interconnect communications and we developed a program to optimize location of neurons on calculation nodes in the 3D torus network. As a these results, we obtained 187 TFLOPS with 196,608 CPU cores.
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