Intel MKL999LSGE01 Manual De Usuario
Details
Linear Algebra
Intel® Math Kernel Library (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.
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 3.3.1 interfaces and enhancements.
systems of equations. Eigenvalue and least-squares solvers are also included, as are the latest LAPACK 3.3.1 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.
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.
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.
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.
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.
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
Feature
Benefit
Conditional Numerical
Reproducibility
Reproducibility
Overcome the inherently non-associativity characteristics of floating-point arithmetic results with new support in
the Intel MKL. New in this release is the ability to achieve reproducibility without memory alignment.
the Intel MKL. New in this release is the ability to achieve reproducibility without memory alignment.
New and improved optimizations for
Haswell Intel® Core™, Ivy Bridge
Intel® Xeon, future Broadwell
processors and Intel® Xeon Phi™
coprocessors
Haswell Intel® Core™, Ivy Bridge
Intel® Xeon, future Broadwell
processors and Intel® Xeon Phi™
coprocessors
Intel MKL is optimized for the latest and upcoming processor architectures to deliver the best performance in the
industry. For example, new optimizations for the fusedmultiply-add (FMA) instruction set introduced in Haswell
Core processors deliver up to 2x performance improvement for floating point calculations.
industry. For example, new optimizations for the fusedmultiply-add (FMA) instruction set introduced in Haswell
Core processors deliver up to 2x performance improvement for floating point calculations.
Automatic offload and compute load
balancing between Intel Xeon
processors and Intel Xeon Phi
coprocessors – Now for Windows*
balancing between Intel Xeon
processors and Intel Xeon Phi
coprocessors – Now for Windows*
For selected linear algebra functions, Intel MKL can automatically determine the best way to utilize a system
containing one or more Intel Xeon Phi coprocessors. The developer simply calls the MKL function and it will take
advantage of the coprocessor if present on the system. New functions added for this release plus Windows OS
support.
containing one or more Intel Xeon Phi coprocessors. The developer simply calls the MKL function and it will take
advantage of the coprocessor if present on the system. New functions added for this release plus Windows OS
support.
Extended Eigensolver Routines
based on the FEAST algorithm
based on the FEAST algorithm
New sparse matrix Eigensolver routines handle larger problem sizes and use less memory. API-compatibility with
the open source FEAST Eigenvalue Solver makes it easy to switch to the highly optimized Intel MKL
implementation.
the open source FEAST Eigenvalue Solver makes it easy to switch to the highly optimized Intel MKL
implementation.