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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!

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Intel® MKL VML Training Material

This article contains training material (in PDF format) on Intel® MKL Vector Math (VML), which includes details of VML features and performance, examples and its application in Finance.
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  • Back-substitution of Pardiso not executing in parallel


    In an iterative algorithm I'm reusing the factorization of a symmetric indefinite matrix to solve for different right-hand sides. The back-substitution is performed in a loop with a single right-hand side being sent to pardiso at each time.

    I'm using paridiso_64 and mkl 11.1u2.

    Inplace permutation


    I need to permute a vector according to an index array jpvt returned by ?geqp3.  In the example lapack/source/dgeqpfx.f it is done using an auxiliary array. I would like to do the transformation inplace (or at least with O(1) extra memory), but as you can see from the attached source code, I don’t seem to be able to use ?laswp correctly. Can this be used for this kind of transformation ? If yes, how ? If no, is there another way to do this ?

    Thank you in advance.


    Do I need Pardiso_64? iparm(18) reports negative value

    I am attempting to solve a large complex, symmetric matrix using PARDISO.  The matrix has approximately 1,000,000 equations, and 91,000,000 non-zeros.  I suspect that during Pardiso's factorization stage, the number of non-zeroes in the factors exceed the 32-bit integer size limit (i.e. 2^31).  I suspect this because pardiso returns a very large negative number for iparm(18), after the factorization stage.

    So ... here's the questions.

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