Intel® Math Kernel Library (Intel® MKL) 11.3 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 11.3 Update 4

  • BLAS:
    • Introduced new packed matrix multiplication interfaces (?gemm_alloc, ?gemm_pack ,?gemm_compute, ?gemm_free) for single and double precisions.
    • Improved performance over standard S/DGEMM on Intel® Xeon processor E5-xxxx v3 and later processors.
  • LAPACK:
    • Improved LU factorization, solve, and inverse (?GETR?) performance for very small sizes (<16).
    • Improved General Eigensolver (?GEEV and ?GEEVD) performance for the case when eigenvectors are needed.
    • Added TBB parallelism for ?ORGQR/?UNGQR.

Known Limitations:

  • cblas_?gemm_alloc is not supported on Windows* OS for the IA-32 architectures with single dynamic library linking.

What's New in Intel MKL 11.3 Update 3

  • Performance improvements for Intel Optimized MP LINPACK Benchmark on Intel® Advanced Vector Extensions 512 (Intel® AVX512) and second generation of Intel® Xeon Phi™ Product Family (codename Knights Landing)
  • BLAS:
    • Improved small matrix [S,D]GEMM performance on Intel AVX2, Intel AVX512 and on second generation of Intel® Xeon Phi™ Product Family (codename Knights Landing)
    • Improved threading (OpenMP) performance of xGEMMT, xHEMM, xHERK, xHER2K, xSYMM, xSYRK, xSYR2K on Intel AVX512, and on second generation of Intel® Xeon Phi™ Product Family (codename Knights Landing)
    • Improved [C,Z]GEMV, [C,Z]TRMV, and [C,Z]TRSV performance on Intel AVX2, Intel AVX512, Intel® Xeon® product family,and on second generation of Intel® Xeon Phi™ Product Family (codename Knights Landing)
  • LAPACK:
    • Updated Intel MKL LAPACK to the latest LAPACK version 3.6 specification. New features introduced in this version are:
      • SVD by Jacobi ([CZ]GESVJ) and preconditioned Jacobi ([CZ]GEJSV) algorithms
      • SVD via EVD allowing computation of a subset of singular values and vectors (?GESVDX)
      • Level 3 BLAS versions of generalized Schur (?GGES3), generalized EVD (?GGEV3), generalized SVD (?GGSVD3) and reduction to generalized upper Hessenberg form (?GGHD3)
      • Multiplication of general matrix by a unitary/orthogonal matrix possessing 2x2 structure ( [DS]ORM22/[CZ]UNM22)
    • Improved performance of LU (?GETRF) and QR(?GEQRF) on Intel AVX512 and on second generation of Intel® Xeon Phi™ Product Family (codename Knights Landing)
    • Improved check of parameters for correctness in all LAPACK routines to enhance security
  • ScaLAPACK:
    • Improved hybrid (MPI + OpenMP) performance of ScaLAPACK/PBLAS by increasing default block size returned by pilaenv
  • SparseBlas:
    • Added examples that cover spmm and spmmd functionality
    • Improved performance of parallel mkl_sparse_d_mv for general BSR matrices on Intel AVX2
    • Parallel Direct Sparse Solver for Clusters:
      • Improved performance of solving step for small matrices (less than 10000 elements)
      • Added mkl_progress support in Parallel Direct sparse solver for Clusters and fixed mkl_progress in Intel MKL PARDISO
  • Vector Mathematical Functions:
    • Improved implementation of Thread Local Storage (TLS) allocation/de-allocation, which helps with thread safety for DLLs in Windows when they are custom-made from static libraries
    • Improved the automatic threading algorithm leading to more even distribution of vectors across larger numbers of threads and improved the thread creation logic on Intel® Xeon Phi(TM), leading to improved performance on average
  • HPCG (High Performance Conjugate Gradients Benchmark):
    • Benchmark now reports HPCG v3.0 compliant scores by default
    • Improved code portability to allow users to build the benchmark by themselves

What's New in Intel MKL 11.3 Update 2

  • Introduced mkl_finalize function to facilitate usage models when Intel MKL dynamic libraries or third party dynamic libraries are linked with Intel MKL statically are loaded and unloaded explicitly
  • Enabled the second generation of Intel® Xeon Phi™ coprocessor in Automatic Offload (AO) and Compiler Assisted Offload (CAO) modes on Linux
  • Compiler offload mode now allows using Intel MKL dynamic libraries
  • Dynamic libraries for OS X* have run-time dynamic library search path (rpath) to ensure Intel MKL-based applications compatibility with OS X 10.11 (El Capitan) System Integrity Protection (SIP). Please refer to Intel MKL Link Line Advisor for link line changes
  • BLAS:
    • Improved Intel® Threaded Building Blocks (Intel® TBB) 64-bit threading performance of xSYRK, xHERK, xSYR2K and xHER2K for large K and small N sizes on Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512) and Intel® Many Integrated Core architecture (Intel® MIC Architecture)
    • Improved performance of 64-bit HPL on Intel AVX2, Intel AVX-512, Intel® Xeon® processors and Intel MIC Architectures
    • Improved BLAS Level 3 performance on Intel AVX-512 Intel MIC Architecture
    • Added Intel TBB threading for all BLAS level-1 functions
    • Added OpenMP threading for some BLAS level-1 functions: [C,Z]DOTU, ZDOTC, [S,D,C,Z]SCAL, [CS,ZD]SCAL, [S,D]ROTM, I[S,D,C,Z]AMIN, I[S,D,C,Z]AMAX, [S,D, SC, DZ]ASUM and SDSDOT
    • Improved GEMM performance on the second generation of Intel Xeon Phi coprocessors
  • LAPACK:
    • Improved performance of QR factorization for square and for tall-and-skinny matrices
    • Improved performance of Cholesky factorization for small matrices
    • Improved performance of ?TPTRS, ?GBTRF and ?GBTRS for small matrices
    • Improved performance of ?GELQF and ?GESVD for the case when M < N
    • Improved performance for row-major matrix storage in LAPACKE interfaces to routines ?LACPY, ?LASET, ?POEQU, ?POEQUB, [SD]POCON, [SD]PORFS, [SD]PORFSX, [SD]POSV, DSPOSV, [SD]POSVX, [SD]POSVXX, [SD]POTRF, [SD]POTRI and[SD]POTRS
    • Enabled Automatic Offload in MKL_?GETRFNPI functions
  • ScaLAPACK:
    • Improved performance of P?GEMM when both operands sub( A ) and sub( B ) are communicated (M, N >> K)
    • Improved performance of P?POTRF and P???EVD on suboptimal processor grids by matrix redistribution
  • Sparse BLAS:
    • Improved performance of DBSRMV for the non-transpose case when alpha=1
  • Intel MKL PARDISO:
    • Added support for block compressed sparse row (BSR) matrix storage format
    • Added optimization for matrixes with variable block structure
    • Added support for mkl_progress in Parallel Direct Sparse Solver for Clusters
    • Added cluster_sparse_solver_64 interface
  • Vector Mathematical Functions:
    • Improved performance of c/zConj, c/zDiv, c/zExp, c/zLn, c/zMul, c/zMulByConj, and c/zSqrt functions on Intel Xeon processors and Intel AVX-512 Architectures
  • Summary Statistics:
    • Introduced sorting algorithm
  • Known Issue:
    • Users may notice performance degradation of Pardiso ( SMP ) on the solving stage with 1 RHS for smaller matrix sizes (less than 10k). Issue is Fixed in the coming release Intel MKL 11.3.3

 

What's New in Intel MKL 11.3 Update 1

  • Benchmarks:
    • Added new Intel® Optimized High Performance Conjugate Gradient (HPCG) Benchmark with second generation of Intel® Xeon Phi™ coprocessor support
    • Improved MP LINPACK benchmark organization by removing sub-optimal MP LINPACK binaries from the product
    • Improved MP LINPACK performance for Intel® Advanced Vector Extension 2 (Intel® AVX2) for 64-bit Intel MKL
  • BLAS:
    • Improved BLAS Level 1 performance for Intel AVX2 and Intel® Advanced Vector Extensions 512 (Intel® AVX512)
    • Improved performance of BLAS Level 3 functions (S,D,C,Z)SYMM and (C,Z)HEMM with Intel® Threading Building Blocks (Intel® TBB) threading when left side specified and m>>n and when right side specified and n>>m
    • Improved parallel performance of ?GEMM for Intel AVX2 for 64-bit Intel MKL for matrices with moderate dimensions
    • Improved BLAS Level 3 performance for Intel AVX512
    • Improved performance of (S,D)GEMV for Intel AVX2 for 64-bit Intel MKL
    • Improved parallel performance of ?TRSM for Intel AVX2 for 64-bit Intel MKL
    • Improved ?NRM2 performance for Intel® Advanced Vector Extension (Intel® AVX) and Intel AVX2 for 32-bit and 64-bit Intel MKL
    • Fixed (S,D)GEMM issues for beta=0 cases affecting Intel AVX2 for 64-bit Intel MKL during multithreaded execution 
      For DGEMM, this affects matrices with N < 4000 and M/nthreads > 5004 
      For SGEMM, this affects matrices with N < 4000 and M/nthreads > 10008
    • Fixed (S,D)SYMM issues for beta=0 cases affecting Intel AVX2 for 64-bit Intel MKL during both multithreaded and sequential execution 
      For DSYMM, this affects matrices with M/nthreads > 5004 
      For SSYMM, this affects matrices with M/nthreads > 10008
  • LAPACK
    • Introduced an Intel TBB threading layer providing Intel MKL composability for DSYEV and DPSTRF
    • Added C language LAPACK examples
    • Significantly improved performance of (D/S/C/Z)STEDC and (D/S/C/Z)YEVD functions for middle sized matrices with about 4K rows or columns
  • Intel MKL PARDISO 
    • Added Intel TBB threading support
    • Added support for block compressed sparse row (BSR) matrix storage format
    • Added optimization for matrices with variable block structure
  • FFTs
    • Significantly improved small 2D and 3D batched FFT performance on Intel Xeon Phi coprocessor and second generation Intel Xeon processor.
  • Sparse BLAS:
    • Added Intel TBB threading support for matrix-vector multiplication for BSR and CSR matrices
    • Improved parallel performance of sparse matrix by sparse matrix multiplication for large matrices with the inspector-executor API
  • Extended Eigensolver
    • Improved diagnostics in generalized eigenproblem for matrices which are not positive definite
  • Random Number Generators
    • Improved performance of MCG59 and MCG31M1 basic generators and Gaussian ICDF distribution
  • Summary Statistics
    • Improved performance of covariance, correlation and cross product for cases of larger p dimension
  • Vector Mathematics
    • Improved performance for vsRint, vsNearbyInt, vsConj, and vdConj functions
    • Improved accuracy for the EP version of the vdDiv function, the HA version of the vsTan function, and the LA version of the vdTan function
    • Fixed incorrect setting of VML_STATUS_ACCURACYWARNING in the HA and LA complex functions vzAdd, vzSub, vzMul, vzMulByConj, and vzDiv
  • ScaLAPACK
    • Added code examples demonstrating use of ScaLAPACK functionality
  • Added mkl_version.h include file, which can be used to determine the version of Intel MKL at the time of compilation

Known Limitations:

  • CROT and ZROT functions have undefined behavior in multi-threaded mode for vector sizes greater than 2048(ZROT) and 4096(CROT)
    • Workaround: Users can either set MKL_NUM_THREADS=1 (or) MKL_DOMAIN_NUM_THREADS= "MKL_DOMAIN_BLAS=1" or link their application sequentially
  • ?GEMM and ?SYRK functions written in C source code and compiled with Intel C Compiler 16.0 using MKL_DIRECT_CALL flag may produce incorrect results or cause program crash
    • Workaround:
      • Do not pass –openmp flag for the compilation of the affected source files
      • Or, do not use MKL_DIRECT_CALL feature
  • On non-Intel platforms, expect degraded performance for the SOBOL and NIEDERREITER Random Number Generators.
  • ?GEMM might produce incorrect results in Automatic Offload mode with Intel® Manycore Platform Software Stack (Intel® MPSS) 3.5 due to a defect in Intel MPSS. Please use Intel MPSS 3.5.1 or later version instead. No Intel MPSS versions except for 3.5.0 are affected.
  • The Intel® Math Kernel Library Parallel Direct Sparse Solver for Clusters (Intel MKL Parallel Direct Sparse Solver for Clusters) example "cluster_sparse_solver_f" might work incorrectly with Intel® MPI Library 5.1.2. This issue only affects the example and does not affect the product. For correct results, edit the example makefile and add the -no_ilp64 option to the link line.

Deprecation notices:

  • The SP2DP interface is deprecated and may be removed in a future release
  • Use of MPICH 1 has been deprecated and support may be removed in a future release

 

What's New in Intel MKL 11.3

  • Introduced high bandwidth memory (MCDRAM) support for second generation of Intel® Xeon Phi™ coprocessors
  • Introduced MPI wrappers that allow users to build custom BLACS library for most MPI implementations
  • Cluster components (Cluster Sparse Solver, Cluster FFT, ScaLAPACK) are now available for OS X*
  • Extended the Intel MKL memory manager to improve scaling on large symmetric multiprocessing systems
  • Introduced an Intel® Threaded Building Blocks(Intel® TBB) threading layer providing Intel MKL composability with TBB-based applications. BLAS Level 3, LAPACK(?GETRF, ?POTRF, ?GEQRF, ?GELQF, ?(OR/UN)M(QR/QL/LQ/RQ)) and the Poisson solver are parallelized with TBB. All other Intel MKL functions are not explicitly threaded in the layer, but may benefit from listed parallelization
  • Added new service functions to provide more control for Intel MKL Automatic Offload for Intel Xeon Phi systems. These functions include mkl_mic_get_meminfo, mkl_mic_get_cpuinfo, mkl_mic_set_flags, mkl_mic_get_flags, mkl_mic_clear_status, and mkl_mic_get_status
  • Optimizations for the second generation of Intel Xeon Phi coprocessors are dispatched by default. A Call to mkl_enable_instruction routine is not required starting from this release
  • Sparse BLAS: Introduced new 2-stage (inspector-executor) APIs for Level 2 and Level 3 sparse BLAS functions. This feature provides performance benefits to some applications (e.g. iterative solvers), where a matrix structure analysis done in the first (inspector) stage allows better optimizations for operations in the subsequent (executor) stage. The Inspector-executor API provides the following features:
    • Support for 0-based and 1-based indexing, row-major and column-major ordering of matrices, and any combination of these storage schemes
    • Parallel sparse matrix format converters with significantly improved performance
    • Parallel sparse matrix transposition operation and improved performance for matrix-vector and matrix-matrix operations with transposed matrix
    • Parallel triangular sparse solver functionality for CSR and BSR formats
    • Improved performance for sparse matrix-matrix and matrix-vector operations in BSR format
  • BLAS:
    • Introduced ?GEMM_BATCH and (C/Z)GEMM3M_BATCH functions to perform multiple independent matrix-matrix multiply operations
    • Improved parallel performance of (D/S)SYMV on all Intel® Xeon® processors
    • Improved (C/D/S/Z/DZ/SC)ROT performance for Intel® Advanced Vector Extensions (Intel® AVX) architectures in 64-bit Intel MKL
    • Improved (C/Z)ROT performance for Intel® Advanced Vector Extensions 2 (Intel® AVX2) architectures in 64-bit Intel MKL
    • Improved parallel performance of ?SYRK/?HERK, ?SYR2K/?HER2K, and DGEMM for cases with large k sizes on Intel AVX2 architectures in 64-bit Intel MKL
    • Improved ?SYRK/?HERK and ?SYR2K/?HER2K performance on Intel Xeon Phi coprocessors
    • Improved parallel and serial performance of xTRSM on Intel AVX2 64-bit Intel MKL
    • Improved serial performance of STRMM for small triangular matrix (dimension less than or equal to 10) on Intel AVX2 for 64-bit Intel MKL
    • Improved BLAS level 3 functionality performance for second generation of Intel Xeon Phi coprocessors
  • LAPACK and ScaLAPACK
    • LAPACK 3.5.0 compatibility provides 70 new functions, including symmetric/hermitian LDLT factorization with rook pivoting, and CS decomposition for tall and skinny matrices with orthonormal columns
    • Improved performance of LU (?GETRF), QR(?GEQRF) and Cholesky(?POTRF) on Intel AVX2 architectures
    • Improved performance of Intel® Optimized LINPACK Benchmark shared memory (SMP) implementation for Intel AVX2
    • Significantly improved performance of LU factorization for non-square matrices on Intel AVX2 architectures
    • Added new routines for incomplete LU factorization without pivoting
    • Supporting GNU FORTRAN interfaces in ScaLAPACK
    • Improved the performance of LU factorization when CNR (conditional numerical reproducibility) is enabled, narrowing the performance gap between CNR-on and CNR-off to no more than 5%
    • Improved performance of symmetric eigensolver on Intel AVX2 architectures, for cases where only eigenvalues are computed
    • Improved performance of SVD for cases where singular vectors are computed on Intel AVX or Intel AVX2 architectures, and when M>=N and singular vectors are not needed
  • Intel MKL PARDISO
    • Significantly improved scalability on Intel Xeon Phi coprocessors
  • Random Number Generators
    • Introduced counter-based pseudorandom number generator ARS-5 based on the Intel AES-NI instruction set
    • Introduced counter-based pseudorandom number generator Philox4x32-10
  • FFTs
    • Improved 2D and 3D FFT performance on Intel AVX2 architectures
    • Extended FFTW* interface compatibility by providing Fortran MPI wrappers for FFTW3* interface, support for transposed FFT and explicit transposition, thread-safe plan creation and execution routines
  • Cluster Sparse Solver:
    • Improved workload balancing algorithm providing better performance on hybrid clusters on Intel Xeon processors and Intel Xeon Phi coprocessors
  • Documentation:
    • Introduced separate C and Fortran language versions of the Intel MKL Reference manual
    • The C version is available from <install_dir>/documentation_2016/en/mkl/common/mklman-c/index.htm
    • The Fortran version is available from <install_dir>/documentation_2016/en/mkl/common/mklman_f/index.htm
  • Deprecation Notices:
    • Fortran 77 headers are removed. Applications relying on these headers should be updated to include .fi headers instead
    • Removed Cluster support for IA-32
  • Known Limitations:   
    • Building the Intel Optimized MP LINPACK Benchmark for a customized MPI implementation on Windows* is not supported for Microsoft Visual Studio 2015 and later.     Workaround: Use an earlier version of Microsoft Visual Studio.
    •   Issue Description: If the user tries to use MSVS 2015 with our provided build.bat script to build their own xhpl.exe executable, they will see a number of unresolved external symbol errors like: libhpl_intel64.lib(HPL_pdmatgen.obj) : error LNK2001: unresolved external symbol __iob_func 
    • An older version of MSVS was used to build the libhpl_intel64.lib library we provide to link against when building the MP LINPACK benchmark for a customized MPI implementation.  It appears that these functions are now unlined in MSVS2015.
  • Product Contents

    Now Intel MKL consists in one package for both IA-32 and Intel® 64 architectures and in online installer

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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.

    The Intel MKL Extended Eigensolver functionality is based on the Feast Eigenvalue Solver 2.0 http://www.ecs.umass.edu/~polizzi/feast/

    PARDISO (PARallel DIrect SOlver)* in Intel MKL was originally developed by the Department of Computer Science at the University of Basel http://www.unibas.ch . 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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1 comment

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Zenghu C.'s picture

I got the following errors when used Intel Fortran 2016  to compile a code that worked well with Fortran 10:

Error #11018: Cannot open mkl_c.lib

Error #11018: Cannot open libguide.lib

fatal error LNK1181: cannot open input file 'mkl_c.lib'

Can you help me?

 

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