PyOpenCL与Parallel-Python

Question

由于PyOpenCL和Parallel Python都是专用于并行处理的Python模块，有人可以提供一个程序员为什么会使用其中一个的例子吗？

Answer 1

来自package listings：

  

pyopencl - 访问OpenCL并行计算API的Python模块

     

pp - 用于Python的并行和分布式编程工具包

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程序员使用pp创建“作业”和“作业服务器”，以在多核，多处理器和/或群集计算环境中分配工作。

OpenCL就像CUDA的超集语言（特别是应用程序编程接口（API）），不是语法，而是用法。 OpenCL是用于编程连接到计算机的许多不同类型设备的接口。这些设备包括不同的图形卡架构，其中CUDA仅适用于NVIDIA芯片组卡。对于可能只在具有NVIDA GPU的系统上运行的程序，还有pycuda。

这些模块的使用取决于所访问的硬件和要解决的问题。它们可以根据需要一起使用或单独使用。

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这是ParallelPython程序的example。

#!/usr/bin/python
# File: dynamic_ncpus.py
# Author: Vitalii Vanovschi
# Desc: This program demonstrates parallel computations with pp module 
# and dynamic cpu allocation feature.
# Program calculates the partial sum 1-1/2+1/3-1/4+1/5-1/6+... (in the limit it is ln(2))
# Parallel Python Software: http://www.parallelpython.com

import math, sys, md5, time
import pp

def part_sum(start, end):
    """Calculates partial sum"""
    sum = 0
    for x in xrange(start, end):
        if x % 2 == 0:
           sum -= 1.0 / x 
        else:
           sum += 1.0 / x 
    return sum

print """Usage: python dynamic_ncpus.py"""
print 

start = 1
end = 20000000

# Divide the task into 64 subtasks
parts = 64
step = (end - start) / parts + 1

# Create jobserver
job_server = pp.Server()

# Execute the same task with different amount of active workers and measure the time
for ncpus in (1, 2, 4, 8, 16, 1):
    job_server.set_ncpus(ncpus)
    jobs = []
    start_time = time.time()
    print "Starting ", job_server.get_ncpus(), " workers"
    for index in xrange(parts):
        starti = start+index*step
        endi = min(start+(index+1)*step, end)
        # Submit a job which will calculate partial sum 
        # part_sum - the function
        # (starti, endi) - tuple with arguments for part_sum
        # () - tuple with functions on which function part_sum depends
        # () - tuple with module names which must be imported before part_sum execution
        jobs.append(job_server.submit(part_sum, (starti, endi)))

    # Retrieve all the results and calculate their sum
    part_sum1 = sum([job() for job in jobs])
    # Print the partial sum
    print "Partial sum is", part_sum1, "| diff =", math.log(2) - part_sum1

    print "Time elapsed: ", time.time() - start_time, "s"
    print
job_server.print_stats()

# Parallel Python Software: http://www.parallelpython.com