Intel® Math Kernel Library

Intel® Math Kernel Library (Intel® MKL) 11.3 Beta and Intel® Parallel Studio XE 2016 Beta

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(R) Math Kernel Library ( Intel(R) MKL ) version 11.3 Beta is now available, as part of the Intel® Parallel Studio XE 2016 Beta program. 

Announcing new product: Intel® Data Analytics Acceleration Library 2016 Beta

We are pleased to announce the release of Intel® Data Analytics Acceleration Library 2016 Beta! Intel® Data Analytics Acceleration Library is a C++ and Java API library of optimized analytics building blocks for all data analysis stages, from data acquisition to data mining and machine learning. It is a library essential for engineering high performance data application solutions. Click here to see more.

Intel® MKL Cookbook Recipes

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

Forum poll: Intel MKL and threading

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

 

Cholesky with pdpotrf()

I am performing a Cholesky factorization with pdpotrf(). I am reading the whole matrix in the master node and then I distribute it. Then, every node is handling a submatrix and call pdpotrf(). Then I just send back the submatrices to the master node and compose the solution.

I am amazed by that. How does it do it? I mean what algorithm does it implement? I suspect it's block partitioning and every node is communicating (I hope not much, but I would really like to know how much).

Broken dgeqp3 in Version 11.2 (Update 3) (Linux)

Hi all,

This was working in December 2014 when I last ran my code against MKL, but after upgrading to 11.2u3 I'm getting a response of -9 from the info parameter when calling dgeqp3... which is *really* weird because that indicates that the info parameter itself is wrong (being parameter 9).

My code works against reference LAPACK[1] and ATLAS / OpenBLAS so I'm inclined to suggest that a regression has appeared.

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