How to configure OpenMP in the Intel IPP library to maximize multi-threaded performance of the Intel IPP primitives.
This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
This article demonstrates techniques that software developers can use to identify and fix NUMA-related performance issues in their applications.
We had an ask from one of the various "Birds of a Feather" meetings Intel® holds at venues such as at the Super Computing* (SC) and International Super Computing* (ISC) conferences.
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.
Learn practical tips for using the vectorization advisor, which is part of Intel® Advisor.
This article focuses on the steps to improve software performance with vectorization. Included are examples of full applications along with some simpler cases to illustrate the steps to vectorization.
I can. And if you read this post you will also be able to write one, too. (Might be a cool party trick or a sucker bet to make a little cash.)