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Intel® Math Kernel Library 10.3 For Windows*, Linux* & Mac OS* X this highly optimized computing math library Multicore and multiprocessor ready Standard APIs in C and Fortran Automatic parallelization Royalty free redistribution Intel® Math Kernel Library (Intel® MKL) is a library of highly optimized, extensively parallelized math.
Overview Performance: Ready to Use Intel® Math Kernel Library (Intel® MKL) includes a wealth of math processing routines to accelerate application performance and reduce development time. Intel® MKL includes highly vectorized and threaded Linear Algebra, Fast Fourier Transforms (FFT), Vector Math and Statistics functions. The easiest way to take advantage of all of that processing power is to use a carefully optimized computing math library.
Even the best compiler can’t compete with the level of performance possible from a hand-optimized library. If your application already relies on the BLAS or LAPACK functionality, simply re-link with Intel® MKL to get better performance on Intel and compatible architectures. Because Intel has done the engineering on these ready-to-use, royalty-free functions, there will be more time to add features which customers request. Using Intel MKL can save development, debug and maintenance time in the long run because today’s code will run optimally on future generations of Intel processors with minimal effort. Quotes “I’m a C and Fortran developer and have high praise for the Intel® Math Kernel Library.
One nice feature I’d like to stress is the bitwise reproducibility of MKL which helps me get the assurance I need that I’m getting the same floating point results from run to run.” Franz Bernice CEO and Senior Developer, MSTC Modern Software Technology “Intel MKL is indispensable for any high-performance computer user on x86 platforms.” Prof. Jack Dongarra, Innovative Computing Lab, University of Tennessee, Knoxville Features Comprehensive Math Functionality – Covers Range of Application Needs Intel® MKL includes highly vectorized and threaded Linear Algebra, Fast Fourier Transforms (FFT), Vector Math and Statistics functions. Through a single C or Fortran API call, these functions automatically scale across previous, current and future processor architectures by selecting the best code path for each. Cluster-based versions of LAPACK, FFT and sparse solver – are also included to support MPI-based distributed memory computing. Standard APIs – For Immediate Performance Results Wherever available, Intel® MKL uses de facto industry standard APIs so that minimal code changes are required to switch from another library. This makes it quick and easy to improve your application performance through simple function substitutions or relinking. Simply substituting Intel® MKL’s LAPACK (Linear Algebra PACKage), for example, can yield 500% or higher performance improvement.
In addition to the industry-standard BLAS and LAPACK linear algebra APIs, Intel® MKL also supports MIT’s FFTW C interface for Fast Fourier Transforms. Highest Performance and Scalability across Past, Present & Future Processors – Easily and Automatically Behind a single C or Fortran API, Intel® MKL includes multiple code paths — each optimized for specific generations of Intel and compatible processors. With no code-branching required by application developers, Intel® MKL utilizes the best code path for maximum performance. New optimized code paths are added under these same APIs before future processors are released so developers just link to the newest version of Intel® MKL and their applications are ready to take full advantage of the latest processor architectures. In the case of the Intel® Many Integrated Core Architecture (Intel® MIC Architecture), Intel® MKL can automatically determine the best load balancing between the host CPU and the Intel® Xeon® Phi™ coprocessor in addition to full native optimization support. Flexibility to Meet Developer Requirements Developers have many requirements to meet.
Sometimes these requirements conflict and need to be balanced. Need consistent floating point results with the best application performance possible?
Want faster vector math performance and don’t need maximum accuracy? Intel® MKL gives you control over the necessary tradeoffs including:. Results consistency vs.
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Performance. Accuracy vs. Performance. Compilers, linking and threading models. Languages and operating systems Intel® MKL is also compatible with your choice of compilers, languages, operating systems, linking and threading models. One library solution across multiple environments means only one library to learn and manage.
Linear Algebra Intel® MKL BLAS provides optimized vector-vector (Level 1), matrix-vector (Level 2) and matrix-matrix (Level 3) operations for single and double precision real and complex types. Level 1 BLAS routines operate on individual vectors, e.g., compute scalar product, norm, or the sum of vectors. Level 2 BLAS routines provide matrix-vector products, rank 1 and 2 updates of a matrix, and triangular system solvers. Level 3 BLAS level 3 routines include matrix-matrix products, rank k matrix updates, and triangular solvers with multiple right-hand sides. Intel® MKL LAPACK provides extremely well-tuned LU, Cholesky, and QR factorization and driver routines that can be used to solve linear systems of equations. Eigenvalue and least-squares solvers are also included, as are the latest LAPACK 3.4.1 interfaces and enhancements. Intel® MKL also includes Sparse BLAS and sparse solvers such as PARDISO and iterative sparse solvers.
New in this release of Intel MKL is the Parallel Direct Sparse Solver for Clusters to solve systems of sparse matrices with millions of rows and columns. Fast Fourier Transforms Intel® MKL FFTs include many optimizations and should provide significant performance gains over other libraries for medium and large transform sizes. The library supports a broad variety of FFTs, from single and double precision 1D to multi-dimensional, complex-to-complex, real-to-complex, and real-to-real transforms of arbitrary length. Support for both FFTW.
interfaces simplifies the porting of your FFTW-based applications. Vector Math Intel® MKL provides optimized vector implementations of computationally intensive core mathematical operations and functions for single and double precision real and complex types. The basic vector arithmetic operations include element-by-element summation, subtraction, multiplication, division, and conjugation as well as rounding operations such as floor, ceil, and round to the nearest integer. Additional functions include power, square root, inverse, logarithm, trigonometric, hyperbolic, (inverse) error and cumulative normal distribution, and pack/unpack. Enhanced capabilities include accuracy, denormalized number handling, and error mode controls, allowing users to customize the behavior to meet their individual needs. Statistics Intel® MKL includes random number generators and probability distributions that can deliver significant application performance. The functions provide the user the ability to pair Random-Number Generators such as Mersenne Twister and, Niederreiter with a variety of Probability Distributions including Uniform, Gaussian and Exponential.
Intel® MKL also provides computationally intensive core/building blocks for statistical analysis both in and out-of-core. This enables users to compute basic statistics, estimation of dependencies, data outlier detection, and missing value replacements.
These features can be used to speed-up applications in computational finance, life sciences, engineering/simulations, databases, and other areas. Data Fitting Intel® MKL includes a rich set of splines functions for 1-dimensional interpolation. These are useful in a variety of application domains including data analytics (e.g. Histograms), geometric modeling and surface approximation. Splines included are linear, quadratic, cubic, look-up, stepwise constant and user-defined. What’s New.
Direct Sparse Solver for Clusters extends the capabilities of Intel MKL PARDISO, enabling users to solve large distributed sparse systems of equations on clusters up to 2x faster than competing solutions. Linear Algebra Intel® MKL BLAS provides optimized vector-vector (Level 1), matrix-vector (Level 2) and matrix-matrix (Level 3) operations for single and double precision real and complex types. Level 1 BLAS routines operate on individual vectors, e.g., compute scalar product, norm, or the sum of vectors.
Level 2 BLAS routines provide matrix-vector products, rank 1 and 2 updates of a matrix, and triangular system solvers. Level 3 BLAS level 3 routines include matrix-matrix products, rank k matrix updates, and triangular solvers with multiple right-hand sides. Intel® MKL LAPACK provides extremely well-tuned LU, Cholesky, and QR factorization and driver routines that can be used to solve linear systems of equations.
Eigenvalue and least-squares solvers are also included, as are the latest LAPACK 3.4.1 interfaces and enhancements. If your application already relies on the BLAS or LAPACK functionality, simply re-link with Intel® MKL to get better performance on Intel and compatible architectures.
Fast Fourier Transforms Intel® MKL FFTs include many optimizations and should provide significant performance gains over other libraries for medium and large transform sizes. The library supports a broad variety of FFTs, from single and double precision 1D to multi-dimensional, complex-to-complex, real-to-complex, and real-to-real transforms of arbitrary length. Support for both FFTW. interfaces simplifies the porting of your FFTW-based applications.
Intel® - Math Kernel Library (Intel® MKL) For Windows., Linux, and Mac OS. Intel® Software Development Products for Intel® Platforms and Technologies Intel® Math Kernel Library 10.1 for Windows., Linux., and Mac OS.
X Overview Intel® Math Kernel Library (Intel® MKL) offers highly optimized, extensively threaded, math routines for scientific, engineering, and financial applications that require maximum performance. Intel MKL is available as a standalone product as well as with the Intel C and Fortran Compiler Professional Editions and the Intel Cluster Toolkit. Intel® Math Kernel Library Flash Demo Highlights Version 10.1 Now Available! See below for a list of the and performance improvements. Debian and Ubuntu are now supported Click for the full list of supported OS’s, Compilers and Processors. Check out for interesting new technologies related to Intel MKL.
Features Outstanding performance on Intel® processors Achieve outstanding performance with the math library that is highly optimized for Intel® Itanium® 2, Intel® Xeon®, Intel® Pentium® 4, and Intel® Core™2 Duo processor-based systems. Special attention has been paid to optimizing multi-threaded performance for the new Quad-Core Intel® Xeon® processor 5300 series and the Intel® Core™ i7. Intel MKL performance is competitive with that of other math software packages on non-Intel processors. Multi-core ready Excellent scaling on multiprocessor systems¹ Use the built-in parallelism of Intel MKL to automatically obtain excellent scaling on multiprocessors, including the latest dual and quad-core systems. Intel MKL Level-3 BLAS, Fast Fourier transforms, and Vector Math are threaded using OpenMP. Thread-Safety All Intel MKL functions are thread-safe. A non-threaded serial version of Intel MKL is also provided.
Automatic runtime processor detection An automatic runtime check ensures that processor-specific optimized code is executed, and that your application achieves optimal performance on every system. Support for C and Fortran interfaces Unlike some alternative math libraries that require you to purchase multiple products to get C and Fortran interfaces, Intel MKL includes both. Support for all Intel® processors in one package Other math libraries require you to purchase multiple products for support of Intel Itanium 2, Intel Xeon, and Pentium 4 processors. Intel MKL includes support for ALL of these processors in a single, inexpensive package. Royalty-free distribution rights Redistribute unlimited copies of the runtime libraries with your software.
User forum Share experiences with others at the Intel engineer-moderated. Intel® Premier Support Receive one year of world-class technical support with every purchase of Intel MKL. During this period, you can download product upgrades free of charge, including major version releases. For more information, visit the. Functionality Linear Algebra - Deploy BLAS and LAPACK routines that are highly optimized for Intel processors, and provide significant performance improvements over alternative implementations.
Intel MKL is compliant with the new 3.1 release of LAPACK. Linear Algebra - The Intel MKL implementation of ScaLAPACK can provide significant performance improvements over the standard NETLIB implementation. Linear Algebra- Solve large, sparse linear systems of equations with the PARDISO Direct Sparse Solver – an easy-to-use, thread-safe, high-performance, and memory-efficient software library licensed from the University of Basel. Intel MKL also includes Conjugate Gradient and FGMRES iterative sparse solvers.
Employ multi-dimensional FFT routines (1D up to 7D) with mixed radix support and a modern, easy-to-use C/Fortran interface. Intel MKL also provides a set of C routines ('wrappers') that mimic the FFTW 2.x and 3.0 interfaces making it easy for current FFTW users to compare performance with Intel MKL. Increase application speeds with vectorized implementations of computationally-intensive, core mathematical functions (power, trigonometric, exponential, hyperbolic, logarithmic, etc). Speed up your simulations using our vector random number generators, which can provide substantial performance improvements over scalar random number generator alternatives. Intel provides free LINPACK benchmark packages to help you obtain the highest possible benchmark results for your Intel® architecture-based systems. New In This Release In this release of Intel Math Kernel Library (Intel MKL 10.1), we have focused on three primary objectives.
First and always foremost is providing optimized multi-threaded performance for the newest Intel Processors (Quad-Core Intel Xeon processor 5300 series, its close relative the Dual-Core Intel® Xeon® processor 5100 series, and the Intel® Core™ i7). Secondly, we have re-architected Intel MKL with a new “layered” architecture to better support the varied usage models of our users. Lastly, we have merged the standard and cluster editions of Intel MKL into a single, comprehensive package.
Optimizations for the new Quad-Core Intel Xeon processor 5300 series and the Intel® Core™ i7 For more information, see section “” below. New “Layered” Architecture Intel MKL 10.0 introduced a re-architected product—providing multiple layers, and allowing the base Intel MKL package to support numerous configurations of interfaces, compilers, and processors in a single package. Many other library vendors have specific versions that must first be found, downloaded, installed, and tested depending on the particular configuration of your development environment.
This new Intel MKL architecture is intended to provide maximum support for our varied customers’ needs, while minimizing the effort it takes to obtain and utilize the great performance of Intel MKL. For more information, please refer to the “Using Intel MKL Parallelism” section of the Intel MKL. Threading Layer All Intel MKL threading has been isolated to this layer.
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Link to the version of this layer that matches your development environment, and rest assured that Intel MKL will not have threading incompatibilities with the threading in your application - Fully Compliant with Microsoft, GCC, and Intel Compiler Threading Separate versions of this layer are provided that have been compiled with different compilers (Intel, MSFT, GCC) enabling Intel MKL to be fully compliant with threading mechanisms used by whatever development environment your overall application is using. Serial Version of Intel MKL A version of the threading layer that has no threading is also provided. This ensures that Intel MKL will not conflict with your application should you choose not to use the threading within Intel MKL.
Interface Layer This layer enables: - LP64, ILP64 interfaces An ILP64 (64-bit integer data) interface is now included in the base package of Intel MKL (no longer a separate download). The new, layered model has made this possible with minimal increase in product package size. Separate layers for different compiler parameter return value mechanisms (Intel, GCC, MSFT).
Cray-style naming support Computational Layer - The heart of Intel MKL. A runtime check is performed so that processor-specific optimized code is executed. Users can build custom shared objects to include only the specific code needed and thus reduce the size of this layer if size is an issue. PARDISO Direct Sparse Solver New support for Out-of-core memory for real symmetric matrices - Support of separate backward/forward substitution for DSS/PARDISO. A new parameter for turning off iterative refinement for DSS interface. A new parameter for checking sparse matrix structure has been introduced for PARDISO interface.
The sparse solver functionality now integrated into the core math library and it is no longer necessary to link a separate solver library. The sparse solver functionality can now be linked dynamically.
Sparse BLAS Routines for computing the sum and product of two sparse matrices stored in the compressed sparse row format. Routines for converting between different sparse matrix formats have been added. Routines for converting between different sparse matrix formats have been added. Sparse 0-based indexing Single precision support added Level-3 Sparse BLAS triangular solvers were threaded LAPACK The capability to track and/or interrupt the progress of lengthy LAPACK computations has been added via a callback function mechanism. A function called mklprogress can be defined in a user application, which will be called regularly from a subset of the MKL LAPACK routines. Refer to the specific function descriptions to see which LAPACK functions support the feature. Discrete Fourier Transform Interface (DFTI) The DftiCopyDescriptor function has been added for convenience when using the FFTs.
The size of statically linked executables calling DFTI has been reduced significantly. Complex storage is now available for real-to-real transforms. Iterative Solver Preconditioner ILUT accelerator/preconditioner for the Intel MKL RCI iterative solvers Vector Math Functions New Mul, Conj, MulbyConj, CIS, Abs functions.
New “Enhanced Performance” mode. EP Mode is for applications where math function inaccuracies do not dominate parameter inaccuracies (e.g. Monte Carlo simulations and Media applications). All VML functions are now threaded Optimized versions of the Cumulative Normal Distribution (CdfNorm), its inverse (CdfNormInv), and the inverse complementary error function (ErfcInv) have been added to the Vector Math Library.
User’s Guide We have greatly improved our Intel MKL User’s Guide. It is an indispensable tool for working with Intel MKL. Visit the page to download or view it online.
Performance Improvements in Version 10.1 Performance optimizations were done in all areas of the library. Below are some specific, measured performance gains. A list of performance improvements in past versions of Intel MKL is available on the Performance Improvements page. Performance charts are shown on each product domain page (BLAS/LAPACK, FFT, VML, etc). Compatibility Operating Systems Intel MKL 10.1 supports Linux., Windows., and Mac OS.
X. Linux variants include: Red Hat., Suse., Debian., Ubuntu., Asianux., and other Linux Standard Base 3.1 variants. For a complete list, please see. Development Environments Intel MKL is easily used and integrated with popular development tools and environments, such as Microsoft Visual Studio., Xcode., Eclipse., and the GNU Compiler Collection (GCC). Processors Intel MKL 10.1 supports the following families of Intel processors: Intel® Itanium processors family Intel® Xeon® processor family Intel® Core® processor family Intel® Pentium® processor family AMD Opteron.
and Athelon. processor families For a complete list, please see the below. Operating Systems Linux. Operating Systems Supported Linux Operating Systems Intel® Xeon®, Intel® Core™2 Duo, Intel® Pentium® 4 32-bit processors Intel® 64 architecture enabled processors Itanium® Processors Red Hat Enterprise Linux Versions 3.0, 4.0, 5.0 Red Hat Fedora Core Versions 5, 6, 7 SUSE Linux Enterprise Server Versions 9.0, 10.0 SGI Propack Versions 4.0, 5.0 Debian 3.1, 4.0 (NEW!) Ubuntu 6.0, 7.0 (NEW!) Asianux 3.0 (Red Flag, Hansoft, Miracle) Mandriva/Mandrake 10.1 Turbo Linux 10, 11 Linux Standard Base 3.1.
Compilers Windows. Compilers Supported Windows Compilers Intel® Xeon®, Intel® Core™2 Duo, Intel® Core™ i7, Intel® Pentium® 4 32-bit processors Intel® 64 architecture enabled processors Itanium® 2 Processors Intel® Visual Fortran Compiler for Windows.
versions 9.1, 10.0, 10.1, 11.0 Intel® C Compiler for Windows versions 9.1, 10.0, 10.1, 11.0 Microsoft Visual C. 2002 Microsoft Visual C.NET 2003 Microsoft Visual C.NET 2005 Microsoft Visual C.
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2005 Team Suite Microsoft Platform SDK compiler. ¹ Performance tests and ratings are measured using specific computer systems and/or components and reflect the appropriate performance of Intel products as measured by those tests. Any difference in system design or configuration may affect actual performance. Buyers should consult other sources of information to evaluate the performance of systems or components they are considering purchasing. Get on performance tests and on the performance of Intel products.
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