Learn how to write an MPI program in Python*, and take advantage of Intel® multicore architectures using OpenMP threads and Intel® AVX512 instructions.
In the previous article, we discussed the performance and accuracy of Binarized Neural Networks (BNN). We also introduced a BNN coded from scratch in the Wolfram Language. The key component of this neural network is Matrix Multiplication.
Cython* is a superset of Python* that additionally supports C functions and C types on variable and class attributes. Cython generates C extension modules, which can be used by the main Python program using the import statement.
Learn techniques for vectorizing code, adding thread-level parallelism, and enabling memory optimization.
Matrix multiplication (MM) of two matrices is one of the most fundamental operations in linear algebra. The algorithm for MM is very simple, it could be easily implemented in any programming language. This paper shows that performance significantly improves when different optimization techniques are applied.
This paper examines software performance optimization for an implementation of a non-library version of DGEMM executing on the Intel® Xeon Phi™ processor (code-named Knights Landing, with acronym K
Exercise in performance optimization on Intel Architecture, including Intel® Xeon Phi™ processors.
This document is designed to help users get started writing code and running MPI applications using the Intel® MPI Library on a development platform that includes the Intel® Xeon Phi™ processor.
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.