相关疑难解决方法(0)

我不确定这是否更像是一个操作系统问题,但我想我会问这里,以防任何人从Python的结尾有一些见解.

我一直在尝试使用一个CPU密集型for循环joblib,但是我发现不是将每个工作进程分配给不同的核心,我最终将它们全部分配到同一个核心而没有性能提升.

这是一个非常简单的例子......

from joblib import Parallel,delayed
import numpy as np

def testfunc(data):
    # some very boneheaded CPU work
    for nn in xrange(1000):
        for ii in data[0,:]:
            for jj in data[1,:]:
                ii*jj

def run(niter=10):
    data = (np.random.randn(2,100) for ii in xrange(niter))
    pool = Parallel(n_jobs=-1,verbose=1,pre_dispatch='all')
    results = pool(delayed(testfunc)(dd) for dd in data)

if __name__ == '__main__':
    run()

...这是我在htop脚本运行时看到的内容:

我在一台4核的笔记本电脑上运行Ubuntu 12.10(3.5.0-26).显然joblib.Parallel是为不同的工作者生成单独的进程,但有没有办法让这些进程在不同的内核上执行？

我试图在Python中实现大量的矩阵 - 矩阵乘法.最初,我假设NumPy将自动使用我的线程BLAS库,因为我是针对这些库构建的.但是,当我查看顶部或其他内容时,似乎代码根本不使用线程.

任何想法是什么错误或我可以做些什么来轻松使用BLAS性能？

我正在运行Mac OS X 10.6.8并使用Enthought Python Distribution.我想要numpy函数来利用我的核心.我遇到了类似于这篇文章的问题:python/numpy中的多线程blas但是在完成该海报的步骤之后,我仍然遇到同样的问题.这是我的numpy.show_config():

lapack_opt_info:
    libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread']
    library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib']
    define_macros = [('SCIPY_MKL_H', None)]
    include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include']
blas_opt_info:
    libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread']
    library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib']
    define_macros = [('SCIPY_MKL_H', None)]
    include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include']
lapack_mkl_info:
    libraries = ['mkl_lapack95_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread']
    library_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/lib']
    define_macros = [('SCIPY_MKL_H', None)]
    include_dirs = ['/Library/Frameworks/EPD64.framework/Versions/1.4.2/include']
blas_mkl_info:
    libraries = ['mkl_intel_lp64', 'mkl_intel_thread', 'mkl_core', 'mkl_mc', 'mkl_mc3', 'pthread']
    library_dirs = …