Learn about the algorithms used to achieve vectorization in GCC 5.0.
This algorithm can be used to improve sparse matrix-vector and matrix-matrix multiplication in any numerical computation. As we know, there are lots of applications involving semi-sparse matrix computation in High Performance Computing. Additionally, in popular perceptual computing low-level engines, especially speech and facial recognition, semi-sparse matrices are found to be very common....
Parallel Universe is Intel's quarterly magazine that explores inroads and innovations in software development. The new issue takes a deep dive into the subject of vectorization and what it can do for you. Our first feature article looks at the SIMD directives for explicit vector programming now available in OpenMP. The second article walks you through Vectorization Advisor, a new tool in the...
What three code modernization techniques would I suggest to help a programmer improve the execution performance of her code? With too many specific things to choose from, these are three recommendations for any programmer anywhere and anytime.
This series of two articles discusses how data and memory layout affect performance and suggests specific steps to improve software performance. The basic steps shown in these two articles can yield significant performance gains. These two articles are designed at an intermediate level. It is assumed the reader desires to optimize software performance using common C, C++ and Fortran* programming...
An Intro to Multi-Level Parallelism for High-Performance Computing by Clay Breshears | Life Sciences Software Architect, Intel
As Shared by Mathieu Gravey, Grand-Prize Winner of the Intel Modern Code Developer Challenge
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.)
As I mentioned in my previous post about writing a vectorized reduction code from Intel vector intrinsics, that part of the code was just the finishing touch on a loop computing squared difference of complex values.