Intel® MKL 11.0 Update 2 introduced a new component called Extended Eigensolver routines. These routines solve standard and generalized Eigenvalue problems for symmetric/Hermitian and symmetric/Hermitian positive definite sparse matrices. Specifically, these routines computes all the Eigenvalues and the corresponding Eigenvectors within a given search interval [λmin, λmax]:
intel mkl
Statically linking MKL library and IPP library in same project produce the link errors
Problem Description: Statically linking MKL and IPP in the same project produce the link errors like the following:
1>ipps_l.lib(pscopyg9as_g9.obj) : error LNK2005: _g9_ownsSet_32s_G9 already defined in mkl_core.lib(pscopyg9as_20120907.obj)
1>ipps_l.lib(pscopyg9as_g9.obj) : error LNK2005: _g9_ownsSet_16u_G9 already defined in mkl_core.lib(pscopyg9as_20120907.obj)
1>ipps_l.lib(pscopyg9as_g9.obj) : error LNK2005: _g9_ownsSet_8u_G9 already defined in mkl_core.lib(pscopyg9as_20120907.obj)
Optimize Financial Applications using Intel® Math Kernel Library
Intel® Math Kernel Library (Intel® MKL) contains a wealth of highly optimized math functions that are fundamental to a wide variety of Financial Applications. Intel MKL uses Industry Standard interfaces and can be easily integrated into your current application framework. The Webinar provides an overview of Intel MKL to accelerate financial applications. Topics include:
Webinar: Get Ready for Intel® Math Kernel Library on Intel® Xeon Phi™ Coprocessors
Intel recently unveiled the new Intel® Xeon Phi™ product – a coprocessor based on the Intel® Many Integrated Core architecture. Intel® Math Kernel Library (Intel® MKL) 11.0 introduces high-performance and comprehensive math functionality support for the Intel® Xeon Phi™ coprocessor. You can download the audio recording of the webinar and the presentation slides from the links below.
Using Intel® MKL with R
Overview
R is a programming language for statistical computing. The open source package also provides an environment for creating and running R programs. This guide will show how to use the BLAS and LAPACK libraries within Intel® Math Kernel Library (Intel® MKL) to improve the performance of R. To use other Intel MKL functions, you read this article on Extending R with Intel MKL.
Reference: http://www.r-project.org/
Webinar: Getting Reproducible Results with Intel® MKL 11.0
Intel(R) MKL 11.0 introduces new functionality allowing users to balance the need for reproducible results with performance. The webinar recording and presentation linked below discusses the mechanisms that cause variability in floating point results, the new controls to limit these, and the performance trade-offs involved.
Conditional Numerical Reproducibility (CNR) in Intel MKL 11.0
New functionality in Intel MKL 11.0 now allows you to balance performance with reproducible results by allowing greater flexibility in code path choice and by ensuring that algorithms are deterministic. To learn more about Conditional Numerical Reproducibility (CNR) see the following resources:
A simple example to measure the performance of an Intel® MKL function
The time required by the first MKL call should be ignored for the perfromance measurements. The first MKL call has overhead due to buffer allocation and thread initialization. Ignoring the first MKL call gives more consistent times for small problems.
MKL FFT performance – comparison of local and distributed-memory implementations
MKL FFT performance using local and distributed-memory implementations. The article shows some performance results for FFT and CFFT.
Intel® MKL 10.3 Install Guide
Installation instructions for the Intel® Math Kernel Library
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