Learn how to write an MPI program in Python*, and take advantage of Intel® multicore architectures using OpenMP threads and Intel® AVX512 instructions.
Цикл лабораторных работ по программированию на многоядерных вычислительных системах. Материал каждой лабораторной работы включает описание, постановку задачи, шаблоны и примеры, а так же краткие комментарии к лабораторной работе для преподавателей.
Purpose of this demo is to show an advantage of Westmere Crypto Acceleration Engine.
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.
The NEMO* (Nucleus for European Modelling of the Ocean) numerical solutions framework encompasses models of ocean, sea ice, tracers, and biochemistry equations and their related physics.This recipe shows the performance advantages of using the Intel® Xeon Phi™ processor 7250.