This page contains common questions and answers on multi-threading in the Intel IPP.
How to configure OpenMP in the Intel IPP library to maximize multi-threaded performance of the Intel IPP primitives.
Programming for Multicore and Many-core Products including Intel® Xeon® processors and Intel® Xeon Phi™ X100 Product Family coprocessorsThe programming models in use today, used for multicore processors every day, are available for many-core coprocessors as well. Therefore, explaining how to program both Intel Xeon processors and Intel Xeon Phi coprocessor is best done by explaining the options for parallel programming. This paper provides the foundation for understanding how multicore processors and many-core coprocessors are...
本文将介绍使用面向 TensorFlow 的英特尔® 优化* 进行 CPU 推理的性能注意事项
Q: How to get Intel® Integrated Performance Primitives (Intel® IPP) Static threaded libraries?
There are two listed below limitations with Intel® Math Kernel Library (Intel® MKL) 11.3 Update 3 which were discovered recently.
Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
This article will describe performance considerations for CPU inference using Intel® Optimization for TensorFlow*