2021년 2월 17일 (수) 08:27에 Pythagoras0님의 편집

2021-02-17T08:27:53Z

Pythagoras0: /* 메타데이터 */ 새 문단

2020-12-26T12:27:04Z

메타데이터: 새 문단

Pythagoras0: /* 노트 */ 새 문단

2020-12-21T05:32:07Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q6786762 Q6786762]
===말뭉치===
# Intel® Math Kernel Library (Intel® MKL) accelerates math processing and neural network routines that increase application performance and reduce development time.<ref name="ref_cd83c954">[https://www.cloudera.com/downloads/partner/intel.html Download Intel's Math Kernel Library]</ref>
# Intel MKL includes highly vectorized and threaded Linear Algebra, Fast Fourier Transforms (FFT), Neural Network, Vector Math and Statistics functions.<ref name="ref_cd83c954" />
# 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.<ref name="ref_cd83c954" />
# The Intel Math Kernel Library (MKL) is a library of optimized, general-purpose math software.<ref name="ref_8aa15836">[https://www2.cisl.ucar.edu/resources/software/math-kernel-library-mkl Math Kernel Library (MKL)]</ref>
# the MKL module gives you access to many MKL examples.<ref name="ref_8aa15836" />
# This example shows how to use the Intel compiler ifort to compile a program with MKL dependencies.<ref name="ref_8aa15836" />
# The program in this example calls BLAS subroutine DGEMM, which is included in MKL.<ref name="ref_8aa15836" />
# Intel Math Kernel Library (Intel MKL) is a library of optimized math routines for science, engineering, and financial applications.<ref name="ref_ce94caa1">[https://en.wikipedia.org/wiki/Math_Kernel_Library Math Kernel Library]</ref>
# MKL is bundled with Intel Parallel Studio XE, Intel Cluster Studio XE, Intel C++, Fortran Studio XE products as well as canopy.<ref name="ref_ce94caa1" />
# At least two routes for hooking the MKL's internal routines to remove the discrimination have been discovered.<ref name="ref_ce94caa1" />
# Agner Fog discovered that MKL and ICC binaries also have a non-discriminating dispatcher.<ref name="ref_ce94caa1" />
# MKL 9.0 release is included with the product ( <install_dir> /Documentation/ja-JP/mklman90.pdf).<ref name="ref_341f276d">[https://scc.ustc.edu.cn/zlsc/chinagrid/intel/mkl/mkl_documentation.htm Intel® Math Kernel Library Documentation]</ref>
# Available only if the Intel® C++ Composer XE or Intel® Fortran Composer XE product that includes Intel MKL provides Japanese localization.<ref name="ref_341f276d" />
# MKL includes a wealth of routines to accelerate technical application performance on modern multicore architectures.<ref name="ref_c1303cb9">[https://www.nrel.gov/hpc/eagle-software-libraries-mkl.html Using the Intel Math Kernel Library on Eagle]</ref>
# If you are mixing an Anaconda environment with modules to build, always activate the conda environment before loading any library modules like MKL.<ref name="ref_c1303cb9" />
# With the Intel toolchain, linking against MKL is as simple as adding -mkl to the link command.<ref name="ref_c1303cb9" />
# This by default links in the threaded MKL routines.<ref name="ref_c1303cb9" />
# Intel® Math Kernel Library (Intel® MKL) helps you achieve maximum performance with a math computing library of highly optimized, extensively threaded routines.<ref name="ref_db9f5c35">[http://scc.ustc.edu.cn/zlsc/intel/2020/mkl/ps2020/get_started.htm Get Started with Intel® Math Kernel Library 2020 for Linux*]</ref>
# Select a function or routine from the Intel MKL library that is best suited for your problem.<ref name="ref_db9f5c35" />
# The release notes contain information specific to the latest release of Intel MKL including new and changed features.<ref name="ref_db9f5c35" />
# Providers become interesting when they can leverage a platform-native high performance library like Intel MKL instead of the default purely managed provider.<ref name="ref_eab99aee">[https://numerics.mathdotnet.com/MKL.html Intel Math Kernel Library (MKL)]</ref>
# However, native binaries like our MKL provider are platform specific, so we need to load them with services of the platform instead of the .Net runtime.<ref name="ref_eab99aee" />
# This means that you can, for example, place the 32 bit MKL provider binaries into C:\MKL\x86 and the 64 bit ones into C:\MKL\x64 , and then set Control.<ref name="ref_eab99aee" />
# and /x64 folders in mathnet-numerics/out/MKL: you should now find the libiomp5.dylib and MathNet.Numerics.MKL.dll libaries.<ref name="ref_eab99aee" />
# ® Math Kernel Library (Intel® MKL) (shown in the last lines of code in the figure).<ref name="ref_a20a7ffa">[https://www.sciencedirect.com/topics/computer-science/intel-math-kernel-library Intel Math Kernel Library - an overview]</ref>
# Replacing the Intel MKL routine with a simpler numerical integration routine allows us to solve both of these problems.<ref name="ref_a20a7ffa" />
# The DGEMM provided by Intel MKL is already cache-blocked, solving all of the issues experienced by our implementation.<ref name="ref_a20a7ffa" />
# For multi-process applications, we also support the ScaLAPACK, FFTW2, and FFTW3 MKL wrappers.<ref name="ref_7b24d2ca">[https://www.osc.edu/resources/available_software/software_list/mkl_intel_math_kernel_library MKL - Intel Math Kernel Library]</ref>
# You can use module spider mkl to view available modules for a given machine.<ref name="ref_7b24d2ca" />
# To load the default MKL, run the following command: module load mkl .<ref name="ref_7b24d2ca" />
# To load a particular version, use module load mkl/version .<ref name="ref_7b24d2ca" />
# Load the appropriate mkl module, and then link your code using an appropriate link line.<ref name="ref_5dbbd5bc">[https://www.ch.cam.ac.uk/computing/software/intel-math-kernel-library Intel Math Kernel Library]</ref>
# Never load the MKL modules automatically in your shell startup files.<ref name="ref_5dbbd5bc" />
# For older versions of MKL they have to be compiled.<ref name="ref_5dbbd5bc" />
# The Intel Math Kernel Library (MKL) has been installed for use on Linux systems and can be loaded using the modules environment.<ref name="ref_391b908f">[https://www.mpcdf.mpg.de/services/computing/software/libraries/intel-math-kernel-library Intel Math Kernel Library — Max Planck Computing & Data Facility]</ref>
# The MKL library is composed of highly optimized mathematical routines.<ref name="ref_391b908f" />
# The link line for binding MKL to your application depends on the MKL functions used and if parallel or sequential use is required.<ref name="ref_391b908f" />
# The Intel Math Kernel Library (MKL) is available, and we strongly recommend using it.<ref name="ref_8e935237">[https://www.nsc.liu.se/software/math-libraries/ Math libraries]</ref>
# Several versions of MKL may exist, you can see which versions are available with the "module avail" command.<ref name="ref_8e935237" />
# The MKL consists of two parts: a linear algebra package and processor specific kernels.<ref name="ref_8e935237" />
# If you want to build an application using MKL with the Intel compilers at NSC, we recommend using the flag -Nmkl (to get your application correctly tagged) and the flag -mkl=MKLTYPE .<ref name="ref_8e935237" />
# MKL's configuration settings for training and inference are influenced by these factors.<ref name="ref_3045e996">[https://docs.aws.amazon.com/ko_kr/deep-learning-containers/latest/devguide/deep-learning-containers-mkl.html AWS Deep Learning Containers Intel Math Kernel Library (MKL) Recommendations]</ref>
# This is compared to the binary without MKL optimizations and measured in terms of samples/second.<ref name="ref_3045e996" />
# In rare cases Intel MKL can have adverse effects.<ref name="ref_3045e996" />
# ® Math Kernel Library (Intel® MKL) optimizes code with minimal effort for future generations of Intel® processors.<ref name="ref_8046eb67">[https://polyhedron.com/?product=intel-math-kernel-library-mkl Intel® Math Kernel Library (MKL) – Polyhedron Software & Services Ltd. Website]</ref>
# Technical support For details about the compiler and linking options, threading, and memory management with Intel MKL , see Intel MKL User's Guide.<ref name="ref_4cfb4cbb">[http://www.jaist.ac.jp/iscenter-new/mpc/altix/altixdata/opt/intel/mkl/9.1.023/doc/Doc_index.htm Intel® Math Kernel Library 9.1 for Linux*]</ref>
# Math Kernel Library Help bookmark in the left-hand panel and then the Intel Math Kernel Library Reference Manual bookmark below it.<ref name="ref_4cfb4cbb" />
# ® Math Kernel Library (Intel® MKL) includes a wealth of math processing routines to accelerate application performance and reduce development time.<ref name="ref_fa6f06d0">[https://www.adeptscience.de/products/cpp/intel-math-kernel-library-windows-linux.html Intel Math Kernel Library]</ref>
# Intel® MKL includes highly vectorized and threaded Linear Algebra, Fast Fourier Transforms (FFT), Vector Math and Statistics functions.<ref name="ref_fa6f06d0" />
# “I’m a C++ and Fortran developer and have high praise for the Intel® Math Kernel Library.<ref name="ref_fa6f06d0" />
# ® MKL includes highly vectorized and threaded Linear Algebra, Fast Fourier Transforms (FFT), Vector Math and Statistics functions.<ref name="ref_fa6f06d0" />
# Update build scripts so that they point to the desired version of Intel MKL if you choose to keep multiple versions installed on your computer.<ref name="ref_360986ae">[http://wwwuser.gwdg.de/~parallel/mkl181_doc/Getting_Started.htm Getting Started with the Intel(R) Math Kernel Library 8.1 for Linux*]</ref>
# Note that you can have several versions of Intel MKL installed on your computer, but you will be required to remove beta versions of this software.<ref name="ref_360986ae" />
# If you want to further customize some Intel MKL features, you may use the configuration file mkl.cfg which contains several variables that can be changed.<ref name="ref_360986ae" />
# Now we need to download the MKL package.<ref name="ref_3bc6abbc">[https://create.arduino.cc/projecthub/Arduino_Genuino/intel-math-kernel-library-on-arduino-1d0be4 Intel Math Kernel Library on Arduino]</ref>
# In the example, a matrix multiplication is performed using the optimized function cblas_dgemm included in MKL.<ref name="ref_3bc6abbc" />
# The Math Kernel Library (MKL) patch will fix the Math Kernel Library used by LabVIEW Analysis VIs.<ref name="ref_ec7a907b">[https://www.ni.com/fr-fr/support/documentation/supplemental/17/archived--math-kernel-library--mkl--patch-details.html Archived: Math Kernel Library (MKL) Patch Details]</ref>
# The Intel Math Kernel Library (MKL) is composed of highly optimized mathematical functions for engineering and scientific applications requiring high performance on Intel platforms.<ref name="ref_f3e9d8c0">[https://www.nas.nasa.gov/hecc/support/kb/intel-math-kernel-library-(mkl)_90.html Intel Math Kernel Library (MKL)]</ref>
# The sequential (non-threaded) mode does not require an OpenMP runtime library, and does not respond to the environment variable OMP_NUM_THREADS or its Intel MKL equivalents.<ref name="ref_f3e9d8c0" />
# In this mode, Intel MKL runs unthreaded code.<ref name="ref_f3e9d8c0" />
# You should use the library in the sequential mode only if you have a particular reason not to use Intel MKL threading.<ref name="ref_f3e9d8c0" />
# The Intel Math Kernel Library (MKL) is a set of math libraries that contains optimized BLAS, LAPACK, FFTs, ScaLAPACK and other functions.<ref name="ref_e55abce7">[https://www.its.hku.hk/services/research/hpc/software/mkl Intel Math Kernel Library(MKL)]</ref>
# It is quite simple to compile and link a C, C++, or Fortran program with Intel MKL, especially when using the Intel compilers.<ref name="ref_e55abce7" />
# The Intel MKL comes bundled with Intel compilerand Intel MPI.<ref name="ref_e55abce7" />
# To link MKL with non-Intel compiler toolchain, additional environment variables and linking options are required.<ref name="ref_e55abce7" />
# MKL provides linear algebra routines, fast fourier transform, vector math library function, and random number generator function, for developers of engineering, science, and financial software.<ref name="ref_2c67061a">[https://web.kudpc.kyoto-u.ac.jp/manual/en/compilers/mkl Intel Math Kernel Library]</ref>
# When using MKL, you need to use the Intel compiler.<ref name="ref_2c67061a" />
# BLACS and SCALAPACK in MKL cannot be used since MKL doesn't support Cray MPI.<ref name="ref_2c67061a" />
# Intel MKL (Math Kernel Library) is a library of optimized math routines for numerical computations such as linear algebra (using BLAS, LAPACK, ScaLAPACK) and discrete Fourier Transformation.<ref name="ref_19f24c21">[https://wiki.bwhpc.de/e/Math_Kernel_Library_(MKL) Math Kernel Library (MKL)]</ref>
# With its standard interface in matrix computation and the interface of the popular fast Fourier transformation library fftw, MKL can be used to replace other libraries with minimal code changes.<ref name="ref_19f24c21" />
# The desired compiler module (exception system GNU compiler) has to be loaded before using MKL.<ref name="ref_19f24c21" />
# To see a list of all MKL environments set by the 'module load'-command use 'env | grep MKL'.<ref name="ref_19f24c21" />
# Intel’s Math Kernel Library (MKL) provides highly optimized, threaded and vectorized functions to maximize performance on each processor family.<ref name="ref_c82b4b7c">[https://docs.hpc.shef.ac.uk/en/latest/iceberg/software/libs/intel_mkl.html Intel Math Kernel Library — Sheffield HPC Documentation]</ref>
# The MKL was installed along with the Intel compilers as part of Intel Parallel Studio.<ref name="ref_c82b4b7c" />
# The purpose of this set of wrappers is to enable developers whose programs currently use FFTW to gain performance with the Intel MKL Fourier transforms without changing the program source code.<ref name="ref_5dba295e">[http://www.democritos.it/activities/IT-MC/mkl_lib-10.0.011/fftw3xmkl_notes.htm FFTW3.x to Intel(R) Math Kernel Library Wrappers]</ref>
# However, the wrappers to these functions and the fftw_cleanup_threads wrapper are empty and do nothing, as the Intel MKL DFTI implements a different mechanism of parallelization.<ref name="ref_5dba295e" />
# Then the FFTW library can be substituted by the wrapper and Intel MKL libraries.<ref name="ref_5dba295e" />
# The file fftw3_f77_mkl.h in the \interfaces\fftw3xf\wrappers subdirectory in the Intel MKL directory defines function names according to names in the Fortran module.<ref name="ref_5dba295e" />
===소스===
<references />

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-== 메타데이터 ==
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 ===위키데이터===
 * ID :  [https://www.wikidata.org/wiki/Q6786762 Q6786762]

← 이전 판		2020년 12월 26일 (토) 12:27 판
84번째 줄:		84번째 줄:
	===소스===		===소스===
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		+	== 메타데이터 ==
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		+	===위키데이터===
		+	* ID : [https://www.wikidata.org/wiki/Q6786762 Q6786762]

Math Kernel Library - 편집 역사

2021년 2월 17일 (수) 08:27에 Pythagoras0님의 편집

Pythagoras0: /* 메타데이터 */ 새 문단

Pythagoras0: /* 노트 */ 새 문단