使用yt和英特尔Distribution for Python加速仿真分析
Speeding simulation analysis up with yt and Intel Distribution for Python
摘要：
随着现代科学模拟的规模和复杂性不断增加，甚至其分析和后处理也变得越来越苛刻，需要使用HPC资源和方法。yt是一个并行的开源后处理Python包，用于天体物理学中的数值模拟，由于其跨格式兼容性，活跃的开发人员社区以及与其他几种专业Python工具的集成而广受欢迎。英特尔Python发行版通过优化低级库Numpy和Scipy（利用优化的英特尔数学核心库（Intel-MKL）和英特尔MPI库进行分布式计算）来增强yt的性能和并行可扩展性。库软件包yt用于若干分析任务，包括导出量的积分、体积绘制、2D相位图、宇宙学晕分析和合成X射线观测的产生。在本文中，我们提供了yt和英特尔Python发行版的安装以及每个分析任务的执行的简要教程。与Anaconda python发行版相比，使用所提供的解决方案可以在英特尔至强可扩展处理器（代号Skylake）上实现高达4.6倍的净加速。
英文摘要：
As modern scientific simulations grow ever more in size and complexity, even their analysis and post-processing becomes increasingly demanding, calling for the use of HPC resources and methods. yt is a parallel, open source post-processing python package for numerical simulations in astrophysics, made popular by its cross-format compatibility, its active community of developers and its integration with several other professional Python instruments. The Intel Distribution for Python enhances yt's performance and parallel scalability, through the optimization of lower-level libraries Numpy and Scipy, which make use of the optimized Intel Math Kernel Library (Intel-MKL) and the Intel MPI library for distributed computing. The library package yt is used for several analysis tasks, including integration of derived quantities, volumetric rendering, 2D phase plots, cosmological halo analysis and production of synthetic X-ray observation. In this paper, we provide a brief tutorial for the installation of yt and the Intel Distribution for Python, and the execution of each analysis task. Compared to the Anaconda python distribution, using the provided solution one can achieve net speedups up to 4.6x on Intel Xeon Scalable processors (codename Skylake).
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