犰狳中的并行化 [英] Parallelisation in Armadillo

问题描述

Armadillo C ++线性代数库文档说明了在C ++中开发库的原因之一要成为通过现代C ++编译器中的OpenMP实现并行化的简易性，但是犰狳代码不会使用OpenMP。我如何获得与Armadillo并行化的好处？这是通过使用高速LAPACK和BLAS替换实现的吗？我的平台是Linux，Intel处理器，但我怀疑这个问题有一个通用的答案。

解决方案

实际上是通过使用高速LAPACK和BLAS替换来实现的。在Ubuntu 12.04上，我使用软件包管理器安装了OpenBLAS，并从源代码构建了Armadillo库。在 examples 文件夹中创建和运行的示例，我可以使用 OPENBLAS_NUM_THREADS 环境变量控制核心数。 / p>

我创建了一个小项目 openblas-benchmark 它测量Armadillo在计算各种大小矩阵的矩阵乘积C = AxB时的性能增加，但我只能在2核机器上测试它。

性能图显示了大于512x512的矩阵的执行时间减少了近50％。注意，两个轴都是对数的; y轴上的每个网格线表示执行时间加倍。

The Armadillo C++ linear algebra library documentation states one of the reasons for developing the library in C++ to be "ease of parallelisation via OpenMP present in modern C++ compilers", but the Armadillo code does not use OpenMP. How can I gain the benefits of parallelisation with Armadillo? Is this achieved by using one of the high-speed LAPACK and BLAS replacements? My platform is Linux, Intel processor but I suspect there is a generic answer to this question.

解决方案

Okay so it appears that parallelisation is indeed achieved by using the high-speed LAPACK and BLAS replacements. On Ubuntu 12.04 I installed OpenBLAS using the package manager and built the Armadillo library from the source. The examples in the examples folder built and run and I can control the number of cores using the OPENBLAS_NUM_THREADS environment variable.

I created a small project openblas-benchmark which measures the performance increase of Armadillo when computing a matrix product C=AxB for various size matrices but I could only test it on a 2-core machine so far.

The performance plot shows nearly 50% reduction in execution time for matrices larger than 512x512. Note that both axes are logarithmic; each grid line on the y axis represents a doubling in execution time.

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