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 packages are now ready for download. Intel MKL is available as part of the Intel® Parallel Studio XE and Intel® System Studio .
Intel® MKL 11.3 introduces the new optimization for different functions, and provides MPI wrappers which support custom MPI* implementations such as Cray* MPI, and IBM* Platform MPI. It adds Sparse Matrix vector Multiplication format support: a new two stage API for sparse BLAS level 2 and level 3 routines. This release also introduces TBB threading layer providing MKL compatibility with TBB based applications. Join us for a webinar learn the new Intel® MKL and Intel® IPP products.
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 would like to do a QR factorization using LAPACK. From the documentation available here https://software.intel.com/en-us/node/521003#E832D468-0891-40EC-9468-686... , I've decided to use geqrf for the factorization.
As I need to solve a Least square problem, I need to solve R.x = (Q1)^T b as explained in the documentation. You can apply matrix Q with the function ormqr. But, which routine should I use to solve R.x = y ?
OS: Fedora 22
Hardware : 8 Thread(s) per core: 2 Vendor ID: GenuineIntel Model name:
Intel(R) Core(TM) i7-2600K CPU @ 3.40GHz - Sandybridge
Here is my build configuration:
I am using Pardiso, from the MKL that ships with icpc 16.0.0 on Mac OSX.
I have already computed a LU factorization, and I want to use Pardiso as an iterative solver using the previous LU factorization as a preconditionner. For that, I've set iparm = 21 for 2 digits of accuracy. But pardiso_64 returns an error -4 and iparm contains -18.
- 第 1 页