multicpu (中文版)

Set the number of cpu and thread using ONE function:

result = multi_cpu(process_job, jobs, cpu_num, thread_num)

cpu_num: the number of cpu.
thread_num: the number of thread in one cpu.

Installation

Multicpu is conveniently available via pip:

pip install multicpu

or installable via git clone and setup.py

git clone git@github.com:cyh24/multicpu.git
sudo python setup.py install

Usage

The multicpu library enables you to utilize the benefits of multi-cpu and multi-threading with minimal concern about the implementation details.

"Talk is cheap, show me your performance."

Let's take a look at how simple it is to speed up an inefficient chunk of blocking code with minimal effort.

Non-IO-Intensive Function

def process_job(job):
    time.sleep(1)
    return job

jobs = [i for i in range(20)]

IO-Intensive Function

def process_job(job):
    count = 100000000
    while count>0:
        count -= 1
    return job

jobs = [i for i in range(20)]

Non-Thread

if __name__ == "__main__":
    result = []
    for job in jobs:
        result.append(process_job(job))

Multi-thread

from concurrent import futures

if __name__ == "__main__":
    result = []
    thread_pool = futures.ThreadPoolExecutor(max_workers=10)
    result = thread_pool.map(process_job, jobs)

multicpu

from multicpu import multi_cpu

if __name__ == "__main__":
    result = multi_cpu(process_job, jobs, 10, 1)

Performance

Function	Non-Thread	Multi-Thread(10)	multicpu(10,1)	multicpu(10,2)
IO	146.42 (s)	457.53 (s)	16.34 (s)	42.81 (s)
Non-IO	20.02 (s)	2.01 (s)	2.02 (s)	1.02 (s)

How Does it Work?

Feel free to read the source for a peek behind the scenes -- it's less than 60 lines of code.

x0day/multicpu