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As modern scientific simulations grow ever more in size and complexity, eventheir analysis and post-processing becomes increasingly demanding, calling forthe use of HPC resources and methods. yt is a parallel, open sourcepost-processing python package for numerical simulations in astrophysics, madepopular by its cross-format compatibility, its active community of developersand its integration with several other professional Python instruments. TheIntel Distribution for Python enhances yt's performance and parallelscalability, through the optimization of lower-level libraries Numpy and Scipy,which make use of the optimized Intel Math Kernel Library (Intel-MKL) and theIntel MPI library for distributed computing. The library package yt is used forseveral analysis tasks, including integration of derived quantities, volumetricrendering, 2D phase plots, cosmological halo analysis and production ofsynthetic X-ray observation. In this paper, we provide a brief tutorial for theinstallation of yt and the Intel Distribution for Python, and the execution ofeach analysis task. Compared to the Anaconda python distribution, using theprovided solution one can achieve net speedups up to 4.6x on Intel XeonScalable processors (codename Skylake).

Speeding simulation analysis up with yt and Intel Distribution for Python