Calling Intel IPP in Microsoft Visual Studio ( In this article, we used VS 2013 )
Tim Mattson (Intel) has authored an extensive series of excellent videos as in introduction to OpenMP*.
Learn more about an in-depth analysis of code modernization performance conducted by optimizing original CPU code and re-running tests on the latest GPU/CPU hardware.
Intel MKL 11.3 has introduced Intel TBB support.
In interpreted languages, it just takes longer to get stuff done - I earlier gave the example where the Python source code a = b + c would result in a BINARY_ADD byte code which takes 78 machine instructions to do the add, but it's a single native ADD instruction if run in compiled language like C or C++. How can we speed this up? Or as the performance expert would say, how do I decrease...
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.
Intel® Parallel Studio XE is a very popular product from Intel that includes the Intel® Compilers, Intel® Performance Libraries, tools for analysis, debugging and tuning, tools for MPI and the Intel® MPI Library. Did you know that some of these are available for free? Here is a guide to “what is available free” from the Intel Parallel Studio XE suites.
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.