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]:
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)
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:
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.
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.
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.
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: