We are introducing a Technical Preview of Intel® Distribution of Python*, with packages such as NumPy* and SciPy* accelerated using Intel MKL. Python developers can now enjoy much improved performance of many mathematical and linear algebra functions, with up to ~100x speedup in some cases, comparing to the vanilla Python distributions. The technical preview is available for everybody at no cost. Click here to register and download.
Intel MKL is a popular math library used by many to create fast and reliable applications in science, engineering, and finance. Do you know it is now available for free (at no cost)? The community licensing program gives anyone, individuals or organizations, free license for the latest version of Intel MKL. There is no royalty for distributing the library in an application. The only restrictions are:
Intel® Math Kernel Library (Intel® MKL) is a highly optimized, extensively threaded, and thread-safe library of mathematical functions for engineering, scientific, and financial applications that require maximum performance. Intel MKL 11.3 Update 1 packages are now ready for download. Intel MKL is available as part of the Intel® Parallel Studio XE and Intel® System Studio .
Intel MKL Users,
We would like to Introduce a new feature Intel® MKL Cookbook, an online Document with recipes for assembling Intel MKL routines for solving complex problems.Please give us your valuable feedback on these Cookbook recipes, and let us know if you want us to include more recipes and/or improve existing recipes.
Thank you for Evaluating
Intel MKL Team
Intel MKL users,
We would like to hear from you how you are using Intel MKL with threading. Do you use the parallel or sequential MKL? How do your multithreaded applications use MKL? We would appreciate you to complete a short survey. It takes no more than 5 minutes. Your feedback will help us to make Intel MKL a better product. Thanks!
Survey link: https://idz.qualtrics.com/SE/?SID=SV_5Bmh232m96WJK3b
I cant seem to figure out how to set the Properties to compile and link MKL95 in a DLL.
Here is the code:
I'm Using MKL Pardiso Sparse Solver (PARDISO).
Some time ago, I Using Intel Parallel Studio 2013 Version (MKL 11.2).
Recently, I'm Update Intel Parallel Studio 2016 (MKL 11.3).
This Version Support Cluster Sparse Solver. so I using this Function.
But Solving result is difference. (about 0.02)
Originally does the other result come out?
Or is there the condition getting the little more exact result?
In my program, I am not set restriction of OpenMP thread number, but I found it only using 4 OpenMP threads on my machine. Why?
My CPU configuration is (reported by Intel VTune ):
Name: Intel(R) Core(TM) Processor 2xxx Series
Frequency: 2.2 GHz
Logical CPU Count: 8
OS is windows 10.
Following is console output by pardiso and openmp while setting KMP_SETTINGS=1 and pardiso message level parameter to 1: