Learn techniques for vectorizing code, adding thread-level parallelism, and enabling memory optimization.
Storage efficiency technologies like compression and deduplication with rapid generation of cryptographic hashes are available now via the Intel® Intelligent Storage Acceleration Library (Intel® ISA-L). Code sample illustrates how to use this powerful feature.
Code Sample included: Learn how to use MPI-3 shared memory feature using the corresponding APIs on the Intel® Xeon Phi™ processor.
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 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.
For applications such as high frequency trading (HFT), search engines and telecommunications, it is essential that latency can be minimized.
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.