Intel MKL库使用指南

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“Intel的官方MKL库手册，包含LAPACK等常用库的详细指南。” Intel Math Kernel Library (MKL) 是一个强大的数学计算库，主要用于优化在Intel处理器上运行的高性能计算应用。该库提供了各种数值计算和科学计算的核心算法，包括线性代数、傅立叶变换、统计计算、随机数生成以及优化算法等。LAPACK（线性代数包）是MKL中的一个重要组成部分，用于解决大型线性系统的求解、特征值问题和奇异值问题。 MKL库的主要优势在于其高度优化和并行化，它充分利用了Intel处理器的架构特性，如SSE、AVX、AVX2和AVX-512指令集，以及多核和超线程技术，从而在执行效率上远超未优化的代码。自2006年以来，MKL经历了多个版本的迭代和更新。例如： - 版本-001是9.0黄金版，标志着MKL的正式发布。 - 版本-002增加了对Intel MKL 9.1 beta的支持，引入了“开始”、“LINPACK和MP LINPACK基准测试”章节，以及对第三方接口和移除接口的支持。 - 版本-003对应9.1黄金版，扩展了现有章节，讨论了ILP64接口的更多方面，并添加了配置Eclipse CDT以链接Intel MKL的教程。 - 版本-004则介绍了10.0 Beta版，引入了分层设计模型的概念，并调整了整个手册的内容以适应这一模型。 MKL库的使用不仅限于单机环境，还包括集群环境。对于集群软件，有专门的章节说明如何配合Intel MKL进行工作，包括配置和链接指导，以确保在分布式系统上的高效运算。在实际应用中，开发人员可以利用MKL提供的接口，包括C、C++、Fortran的接口，来快速构建高性能的计算程序。MKL库还提供了一些实用工具，如性能监控工具和配置工具，帮助用户评估和优化程序性能。 Intel MKL库是一个强大的数学计算工具，适用于需要高性能计算的科学家、工程师和软件开发者。通过使用MKL，用户可以大大提高计算效率，专注于他们的核心应用逻辑，而不是底层优化。同时，随着Intel处理器技术的不断演进，MKL也会持续更新以支持最新的硬件特性，保持其在高性能计算领域的领先地位。

Getting Started 2

2-3

High-level Directory Structure in chapter 3.

Before You Begin Using Intel MKL

Before you get started using the Intel MKL, sorting out a few important basic concepts will

greatly help you get off to a good start.

The table below summarizes some important things to think of before you start using Intel

MKL.

Table 2-1 What you need to know before you get started

Target platform Identify the architecture of your target machine:

• IA-32

• Intel® 64

• IA-64 (Itanium® processor family)

Reason. Intel MKL libraries, which you should link with your application, are

located in directories corresponding to your particular architecture (see Intel® MKL

Versions). So, you should provide proper paths in your link lines (see Linking

Examples). To configure your development environment for the use with Intel MKL,

set your environment variables using the script corresponding to your architecture

(see Setting Environment Variables

Mathematical

problem

Identify all Intel MKL function domains that problems you are solving require:

• BLAS

• Sparse BLAS

• LAPACK

• PBLAS

• ScaLAPACK

• Sparse Solver routines

• Vector Mathematical Library functions

• Vector Statistical Library functions

• Fourier Transform functions (FFT)

• Cluster FFT

• PBLAS

• Trig o n ometric Transform ro u t i nes

• Poisson, Laplace, and Helmholtz Solver routines

• Optimization (Trust-Region) Solver routines

• GMP* arithmetic functions

Reason. The function domain you intend to use narrows the search in the

Reference Manual for specific routines you need. Additionally, the link line that you

use to link your application with Intel MKL cluster software depends on the function

domains you intend to employ (see Working with Intel® Math Kernel Library Cluster

Software). Coding tips may also depend on the function domain (see Tips and

Techniques to Improve Performance).

2-4

2 Intel® Math Kernel Library User’s Guide

Programming

language

Though Intel MKL provides support for both Fortran and C/C++ programming, not

all the function domains support a particular language environment, for example,

C/C++ or Fortran90/95. Identify the language interfaces that your function

domains support (see Intel® Math Kernel Library Language Interfaces Support

Reason. In case your function domain does not directly support the needed

environment, you can use mixed-language programming (see Mixed-language

programming with Intel® MKL).

For a list of language-specific interface libraries and modules and an example how

to generate them, see also Using Language-Specific Interfaces with Intel® MKL

Threading model Select among the following options how you are going to thread your application:

• Your application is already threaded

• You may want to use the Intel® threading capability, that is, Compatibility

OpenMP* run-time library (

libiomp) or Legacy OpenMP* run-time library

(

libguide), or a threading capability provided by a third-party compiler

• You do not want to thread your application.

Reason. By default, the OpenMP* software sets the number of threads that Intel

MKL uses. If you need a different number, you have to set it yourself using one of

the available mechanisms. For more information, and especially, how to avoid

conflicts in the threaded execution environment, see Using Intel® MKL Parallelism

Additionally, the compiler that you use to thread your application determines which

threading library you should link with your application (see Linking Examples

Linking model Decide which linking model is appropriate for linking your application with Intel MKL

libraries:

• Static

• Dynamic

Reason. For information on the benefits of each linking model, link command

syntax and examples, link libraries, and other linking topics, like how to save disk

space by creating a custom dynamic library, see Linking Your Application with

Intel® Math Kernel Library.

MPI used Reason: To link your application with ScaLAPACK and/or Cluster FFT, the libraries

corresponding to your particular MPI should be included in the link line (see Working

with Intel® Math Kernel Library Cluster Software).

Table 2-1 What you need to know before you get started (continued)

3-2

3 Intel® Math Kernel Library User’s Guide

Layered Model Concept

The Intel Math Kernel Library has long had a structure that is not visible to the user except

for the 32-bit version for Windows* OS. In that case, two interface libraries are provided,

and you must select one of them at run time. Both libraries are relatively small and

independent of a particular processor based on IA-32 architecture, and the major part of

Intel MKL, which is independent of the interface, is not duplicated. Thus, by means of the

interface libraries Intel MKL supports two different compiler interface standards without

greatly increasing the library size.

Starting with release 10.0, Intel MKL is extending this approach to support a richer set of

circumstances: compilers and threading.

Interfaces. On Linux systems based on IA-64 architecture, the Intel® Fortran Compiler

returns complex values differently than gnu and some other compilers. Rather than

duplicate the library for these differences, separate interface libraries are provided to

<mkl directory>/interfaces/fftw2xf

Contains wrappers for FFTW version 2.x (Fortran

interface) to call Intel MKL FFTs

<mkl directory>/interfaces/fftw3xc

Contains wrappers for FFTW version 3.x (C interface) to

call Intel MKL FFTs

<mkl directory>/interfaces/fftw3xf

Contains wrappers for FFTW version 3.x (Fortran

interface) to call Intel MKL FFTs

<mkl directory>/interfaces/lapack95

Contains Fortran 95 wrappers for LAPACK and a

makefile to build the library

<mkl directory>/lib/32

Contains static libraries and shared objects for IA-32

architecture

<mkl directory>/lib/64

Contains static libraries and shared objects for IA-64

architecture (Itanium® processor family)

<mkl directory>/lib/em64t

Contains static libraries and shared objects for Intel®

64 architecture (formerly, Intel® EM64T)

<mkl directory>/man/en_US/man3 Contains man pages for Intel MKL functions

<mkl directory>/tests

Contains source and data files for tests

<mkl directory>/tools/builder

Contains tools for creating custom dynamically linkable

libraries

<mkl directory>/tools/environment

Contains shell scripts to set environmental variables in

the user shell

<mkl directory>/tools/plugins/

com.intel.mkl.help

Contains an Eclipse* IDE plug-in with Intel MKL

Reference Manual in WebHelp format. See

mkl_documentation.htm for comments.

Table 3-1 High-level directory structure (continued)

Directory Comment

Intel® Math Kernel Library Structure 3

3-3

support compiler differences while constraining the size of the library. Similarly, LP64 can

be supported on top of ILP64 through an interface. Moreover, interface libraries are provided to

support legacy supercomputers where single precision means 64-bit arithmetic.

Threading. For efficiency reasons, Intel MKL employs function-level threading throughout

the library rather than loop-level threading. Consequently, all threading can be constrained

to a relatively small set of functions and collected into a library. All references to

compiler-specific run-time libraries are generated in these functions. By compiling them

with different compilers and providing a threading library layer Intel MKL can work in

programs threaded with Intel compilers and other supported threading compilers. A

non-threaded library version can also be obtained by turning off threading when compiling

the threading library layer because all threading is provided through OpenMP* technology.

Computation. For any given processor family (processors based on IA-32, IA-64, or Intel®

64 architecture), a single computational library is used for all interfaces and threading

layers because there is no parallelism in the computational layer.

Run-time library (RTL). The last layer provides RTL support. Not all RTLs are delivered with

Intel MKL. The only RTLs provided, except those that are relevant to the Intel MKL cluster

software, are Intel

® compiler based RTLs: Intel® Compatibility OpenMP* run-time

compiler library (libiomp) and Intel® Legacy OpenMP* run-time compiler library

(libguide). To thread using third-party threading compilers, you can employ Threading

layer libraries or use the compatibility library in the appropriate circumstances.

Layers

There are four essential parts of the library:

1. Interface layer

2. Threading layer

3. Computational layer

4. Compiler Support RTL layer (RTL layer, for brevity).

Interface Layer. This layer provides matching between compiled code of your application

and the threading and/or computational parts of the library. This layer provides:

• An LP64 interface to Intel MKL ILP64 software

(see Support for ILP64 Programming

for details)

• A means of dealing with the way different compilers return function values

• A means of mapping between single-precision names and double-precision names in applications

that employ ILP64, such as Cray-style naming.

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