Improving the Performance of Principal Component Analysis with Intel® Data Analytics Acceleration LibraryThis article discusses an unsupervised machine-learning algorithm called principal component analysis (PCA) that can be used to simplify the data. It also describes how Intel® Data Analytics Acceleration Library (Intel® DAAL) helps optimize this algorithm to improve the performance when running it on systems equipped with Intel® Xeon® processors.
Release Notes of Intel® Media SDK include important information, such as system requirements, what's new, feature table and known issues since the previous release.
Intel® Math Kernel Library Improved Small Matrix Performance Using Just-in-Time (JIT) Code Generation for Matrix Multiplication (GEMM)
The most commonly used and performance-critical Intel® Math Kernel Library (Intel® MKL) functions are the general matrix multiply (GEMM) functions.
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Boosting Deep Learning Training & Inference Performance on Intel® Xeon® and Intel® Xeon Phi™ ProcessorsIn this work we present how, without a single line of code change in the framework, we can further boost the performance for deep learning training by up to 2X and inference by up to 2.7X on top of the current software optimizations available from open source TensorFlow* and Caffe* on Intel® Xeon® processors.