Speeding simulation analysis up with yt and Intel Distribution for Python

TL;DR

This paper demonstrates how combining yt with Intel Distribution for Python significantly accelerates scientific simulation analysis, achieving up to 4.6x speedup on Intel Xeon processors.

Contribution

It introduces an optimized setup using yt and Intel Distribution for Python that enhances performance and scalability for astrophysical simulation analysis.

Findings

Achieved up to 4.6x speedup compared to Anaconda Python.

Improved performance through optimized libraries like Intel MKL and MPI.

Provided a tutorial for installation and execution of analysis tasks.

Abstract

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).