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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 yts 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).
We perform a detailed analysis of the C++ implementation of the Cluster Affiliation Model for Big Networks (BigClam) on the Stanford Network Analysis Project (SNAP). BigClam is a popular graph mining algorithm that is capable of finding overlapping c
Datacenters provide the infrastructure for cloud computing services used by millions of users everyday. Many such services are distributed over multiple datacenters at geographically distant locations possibly in different continents. These datacente
Python has become the de facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High
The usage of the high-level scripting language Python has enabled new mechanisms for data interrogation, discovery and visualization of scientific data. We present yt, an open source, community-developed astrophysical analysis and visualization toolk
Data engineering is becoming an increasingly important part of scientific discoveries with the adoption of deep learning and machine learning. Data engineering deals with a variety of data formats, storage, data extraction, transformation, and data m