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.
Learn techniques for vectorizing code, adding thread-level parallelism, and enabling memory optimization.
With multi-core processors now common place in PCs, and core counts continually climbing, software developers must adapt. By learning to tackle potential performance bottlenecks and issues with concurrency, engineers can future-proof their code to seamlessly handle additional cores as they are added to consumer systems.
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.