Intel® Math Kernel Library (Intel® MKL) 10.3 Release Notes

This document provides a general summary of new features and important notes about the Intel® Math Kernel Library (Intel® MKL) software product.

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What's New in Intel® MKL 10.3 update 1

PARDISO/DSS: Added true F90 overloaded API (see the Intel MKL reference manual for more information)
PARDISO: Improved the statistical reporting to be more reader friendly
Sparse BLAS: Improved performance of ?BSRMM functions on the latest Intel& processors
FFTs: Support for negative strides
FFT examples: Added examples for split-complex FFTs in C and Fortran using both the DFTI and FFTW3 interfaces
VML: Improved performance of real in-place Add/Sub/Mul/Sqr functions on systems supporting SSE2 and SSE3
Poisson Library: Changed the default behavior of the Poisson library functions from sequential to threaded operation
Bug fixes

What's New in Intel® MKL 10.3

BLAS
- New functions for computing 2 matrix-vector products at once: [D/S]GEM2VU, [Z/C]GEM2VC
- New functions for computing mixed precision general matrix-vector products: [DZ/SC]GEMV
- New function for computing the sum of two scaled vectors: *AXPBY
- Intel® AVX optimizations in key functions: SMP LINPACK, level 3 BLAS, DDOT, DAXPY
LAPACK
- New C interfaces for LAPACK supporting row-major ordering
- Integrated Netlib LAPACK 3.2.2 including one new computational routine (*GEQRFP) and two new auxiliary routines (*GEQR2P and *LARFGP) and the earlier LAPACK 3.2.1 update
- Intel® AVX optimizations in key functions: DGETRF, DPOTRF, DGEQRF
PARDISO
- Improved performance of factor and solve steps in multi-core environments
- Introduced the ability to solve for sparse right-hand sides and perform partial solves—produces partial solution vector
- Improved performance of the out-of-core (OOC) factorization step
- Support for zero-based (C-style) array indexing
- Zeros on the diagonal of the matrix are no longer required in sparse data structures for symmetric matrices
- New ILP64 PARDISO interface allows the use of both LP64 and ILP64 versions when linked to the LP64 libraries
- The memory required for storing files on the disk in OOC mode can now be estimated just after reordering
Sparse BLAS
- Format conversion functions now support all data types (single and double precision for real and complex data) and can return sorted or unsorted arrays
FFTs
- New MPI FFTW 3.3alpha1 wrappers cover new cluster functionality
- Improved load-balancing of cluster FFTs provides improved performance
- Intel AVX optimizations in all 1D/2D/3D FFTs
- Improved performance of 2D and 3D mixed-radix FFTs for single and double precision data for all systems supporting the SSE4.2 instruction set
- Support for split-complex data represented as two real arrays introduced for 2D/3D FFTs
- Support for 1D complex-to-complex transforms of large prime lengths
- Introduced Hybrid parallelism (MPI + OpenMP*) on cluster 1D complex transforms and increased performance on vector lengths which are a multiple of the number of MPI processes
VML
- A new function for computing (ax+b)/(cy+d) where a, b, c, and d are scalars, and x and y are real vectors: v[s/d]LinearFrac()
- Intel AVX optimizations for real functions
- A new mode for setting denormals to zero, overflow support for complex vectors, and for every VML function a new function with an additional parameter for setting the accuracy mode
VSL
- A set of new Summary Statistics functions was added covering basic statistics, covariance and correlation, pooled, group, partial, and robust covariance/correlation, quantiles and streaming quantiles, outliers detection algorithm, and missing values support
  - Performance optimized algorithms: MI algorithm for support of missing values, TBS algorithm for computation of robust covariance, BACON algorithm for detection of outliers, ZW algorithm for computation of quantiles (streaming data case), and 1PASS algorithm for computation of pooled covariance
- Improved performance of SFMT19937 Basic Random Number Generator (BRNG)
- Intel® AVX optimizations: MT19937 and MT2203 BRNGs
Documentation: Product documentation is available in the Microsoft Help Viewer* 1.x format that integrates with Microsoft Visual Studio* 2010
Added runtime dispatching dynamic libraries allowing link to a single interface library which loads dependent libraries dynamically at runtime depending on runtime CPU detection and/or library function calls
The custom dynamic libraries builder now uses the runtime dispatching dynamic libraries on the Linux* and Mac OS* X operating systems
A new directory structure has been established to simplify integration of Intel MKL with the Intel® Parallel Studio XE family of products and directories formerly designated as "em64t" are now designated by the "intel64" tag
Intel® Itanium® architecture (IA-64) support is not included in this release. Intel® MKL 10.2 is the latest release for IA-64
The sparse solver functionality has been fully integrated into the core Intel MKL libraries and the libraries with "solver" in the filename have been removed from the product

Product Contents

The Intel® Math Kernel Library (Intel® MKL) version 10.3 and updates consists of three installation packages: one package for both IA-32 and Intel® 64 architectures, one for IA-32 only, and one for Intel® 64 architecture only.

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Attributions

As referenced in the End User License Agreement, attribution requires, at a minimum, prominently displaying the full Intel product name (e.g. "Intel® Math Kernel Library") and providing a link/URL to the Intel® MKL homepage (http://www.intel.com/software/products/mkl) in both the product documentation and website.

The original versions of the BLAS from which that part of Intel® MKL was derived can be obtained from http://www.netlib.org/blas/index.html.

The original versions of LAPACK from which that part of Intel® MKL was derived can be obtained from http://www.netlib.org/lapack/index.html. The authors of LAPACK are E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen. Our FORTRAN 90/95 interfaces to LAPACK are similar to those in the LAPACK95 package at http://www.netlib.org/lapack95/index.html. All interfaces are provided for pure procedures.

The original versions of ScaLAPACK from which that part of Intel® MKL was derived can be obtained from http://www.netlib.org/scalapack/index.html. The authors of ScaLAPACK are L. S. Blackford, J. Choi, A. Cleary, E. D'Azevedo, J. Demmel, I. Dhillon, J. Dongarra, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, and R. C. Whaley.

PARDISO in Intel® MKL is compliant with the 3.2 release of PARDISO that is freely distributed by the University of Basel. It can be obtained at http://www.pardiso-project.org.

Some FFT functions in this release of Intel® MKL have been generated by the SPIRAL software generation system (http://www.spiral.net/) under license from Carnegie Mellon University. The Authors of SPIRAL are Markus Puschel, Jose Moura, Jeremy Johnson, David Padua, Manuela Veloso, Bryan Singer, Jianxin Xiong, Franz Franchetti, Aca Gacic, Yevgen Voronenko, Kang Chen, Robert W. Johnson, and Nick Rizzolo.

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Optimization Notice
The Intel® Math Kernel Library (Intel® MKL) contains functions that are more highly optimized for Intel microprocessors than for other microprocessors. While the functions in Intel® MKL offer optimizations for both Intel and Intel-compatible microprocessors, depending on your code and other factors, you will likely get extra performance on Intel microprocessors. While the paragraph above describes the basic optimization approach for Intel® MKL as a whole, the library may or may not be optimized to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include Intel® Streaming SIMD Extensions 2 (Intel® SSE2), Intel® Streaming SIMD Extensions 3 (Intel® SSE3), and Supplemental Streaming SIMD Extensions 3 (Intel® SSSE3) instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Intel recommends that you evaluate other library products to determine which best meets your requirements.

Optimization Notice

The Intel® Math Kernel Library (Intel® MKL) contains functions that are more highly optimized for Intel microprocessors than for other microprocessors. While the functions in Intel® MKL offer optimizations for both Intel and Intel-compatible microprocessors, depending on your code and other factors, you will likely get extra performance on Intel microprocessors.

While the paragraph above describes the basic optimization approach for Intel® MKL as a whole, the library may or may not be optimized to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include Intel® Streaming SIMD Extensions 2 (Intel® SSE2), Intel® Streaming SIMD Extensions 3 (Intel® SSE3), and Supplemental Streaming SIMD Extensions 3 (Intel® SSSE3) instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors.

Intel recommends that you evaluate other library products to determine which best meets your requirements.