Exploring On-Node Parallelism with Neutral, a Monte Carlo Neutral Particle Transport Mini-App
Exploring On-Node Parallelism with Neutral, a Monte Carlo Neutral Particle Transport Mini-App
复制标题
使用 Neutral（蒙特卡罗中性粒子传输小应用程序）探索节点上并行性
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Simon McIntosh
中科院分区：
文献类型：
--
作者：
Matt Martineau;Simon McIntosh
在这项研究中，我们描述了一种新的蒙特卡罗中性粒子传输迷你应用程序Neil的开发和优化。尽管之前在大规模负载平衡算法方面的研究取得了成功，但如何利用安装在最新超级计算机上的多样化体系结构尚不清楚。我们探索了不同的算法方法，并对应用程序在现代硬件上的性能进行了广泛的研究，这些硬件包括Intel Xeon和Xeon Phi CPU、POWER8 CPU和NVIDIA GPU。当应用于粒子传输时，蒙特卡罗方法并不像预期的那样令人尴尬地并行，因为它依赖于暴露随机内存访问模式的计算网格。该算法需要使用原子操作，并且由于粒子历史的随机分支，在节点级表现出负载不平衡。算法的特点使得利用现代高性能计算体系结构的高存储带宽和浮点成为挑战，本文讨论的两种并行化方案都是由于数据局部性差而导致的主要延迟问题，并且受到使用原子操作进行计数计算的限制。通过在所有CPU上使用超线程并在NVIDIA P100 GPU上实现最佳性能，我们看到了性能的显著提升。一个关键的观察是，容忍延迟的体系结构可能能够隐藏算法的负面特征。
In this research we describe the development and optimisation of a new Monte Carlo neutral particle transport mini-app, neutral. In spite of the success of previous research efforts to load balance the algorithm at scale, it is not clear how to take advantage of the diverse architectures being installed in the newest supercomputers. We explore different algorithmic approaches, and perform extensive investigations into the performance of the application on modern hardware including Intel Xeon and Xeon Phi CPUs, POWER8 CPUs, and NVIDIA GPUs.When applied to particle transport the Monte Carlo method is not embarrassingly parallel, as might be expected, due to dependencies on the computational mesh that expose random memory access patterns. The algorithm requires the use of atomic operations, and exhibits load imbalance at the node-level due to the random branching of particle histories. The algorithmic characteristics make it challenging to exploit the high memory bandwidth and FLOPS of modern HPC architectures.Both of the parallelisation schemes discussed in this paper are dominated latency issues caused by poor data locality, and are restricted by the use of atomic operations for tallying calculations. We saw a significant improvement in performance through the use of hyperthreading on all CPUs and best performance on the NVIDIA P100 GPU. A key observation is that architectures that are tolerant to latencies may be able to hide the negative characteristics of the algorithms.
DOI：
10.1177/1094342014528252
发表时间：
2015-05
期刊：
The international journal of high performance computing applications
影响因子：
--
作者：
McIntosh-Smith S;Price J;Sessions RB;Ibarra AA
通讯作者：
Ibarra AA