Learn how to write an MPI program in Python*, and take advantage of Intel® multicore architectures using OpenMP threads and Intel® AVX512 instructions.
Fine-Tuning Optimization for a Numerical Method for Hyperbolic Equations Applied to a Porous Media Flow Problem with Intel® ToolsThis paper presents an analysis for potential optimization for a Godunov-type semi-discrete central scheme, for a particular hyperbolic problem implicated in porous media flow, using OpenMP* and Intel® Advanced Vector Extensions 2.
This article demonstrates techniques that software developers can use to identify and fix NUMA-related performance issues in their applications.
Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
MILC software represents a set of codes written by the MIMD Lattice Computation collaboration used to study quantum chromodynamics. This article provides instructions for code access, build and run directions for the “ks_imp_rhmc” application on Intel® Xeon® Gold and Intel® Xeon Phi™ processors for better performance on a single node.
Code Sample included: Learn how to use MPI-3 shared memory feature using the corresponding APIs on the Intel® Xeon Phi™ processor.
Get a background on vectorization and learn different techniques to evaluate its effectiveness.