Mavzu: Parallel xisoblash tizimlari

# components of the distance vector
p = nodes[start:stop]
q = nodes.T
Rx = p[:, 0:1] - q[0:1]
Ry = p[:, 1:2] - q[1:2]
Rz = p[:, 2:3] - q[2:3]


# calculate function of the distance
L = np.sqrt(Rx * Rx + Ry * Ry + Rz * Rz)
D[start:stop, :] = L * L * L / 12 + L * L / 6
queue.task_done()

""" Python multiprocessing with shared memory example.

This example demonstrate workaround for the GIL problem. Workaround uses

processes instead of threads and RawArray allocated from shared memory.

See also:

[1] http://docs.python.org/2/library/multiprocessing.html
[2] http://folk.uio.no/sturlamo/python/multiprocessing-tutorial.pdf
[3] http://www.bryceboe.com/2011/01/28/the-python-multiprocessing-queue-and-large-objects/

"""

import time
import ctypes
import multiprocessing as mp
import numpy as np
import matplotlib.pyplot as plt

def generateNodes(N):

""" Generate random 3D nodes
"""
return np.random.rand(N, 3)

def spCalcDistance(nodes):

""" Single process calculation of the distance function.
"""
p = nodes
q = nodes.T
# components of the distance vector
Rx = p[:, 0:1] - q[0:1]
Ry = p[:, 1:2] - q[1:2]
Rz = p[:, 2:3] - q[2:3]
# calculate function of the distance
L = np.sqrt(Rx * Rx + Ry * Ry + Rz * Rz)
D = L * L * L / 12 + L * L / 6
return D
def mpCalcDistance_Worker(nodes, queue, arrD):
""" Worker process for the multiprocessing calculations
"""

nP = nodes.shape[0]

nQ = nodes.shape[0]

D = np.reshape(np.frombuffer(arrD), (nP, nQ))

while True:

job = queue.get()
if job == None:
break

start = job[0]

stop = job[0] + job[1]
# components of the distance vector
p = nodes[start:stop]
q = nodes.T
Rx = p[:, 0:1] - q[0:1]
Ry = p[:, 1:2] - q[1:2]
Rz = p[:, 2:3] - q[2:3]


# calculate function of the distance
L = np.sqrt(Rx * Rx + Ry * Ry + Rz * Rz)
D[start:stop, :] = L * L * L / 12 + L * L / 6
queue.task_done()
queue.task_done()

def mpCalcDistance(nodes):

""" Multiple processes calculation of the distance function.
"""


# allocate shared array
nP = nodes.shape[0]
nQ = nodes.shape[0]

arrD = mp.RawArray(ctypes.c_double, nP * nQ)

# setup jobs
#nCPU = mp.cpu_count()
nCPU = 2
nJobs = nCPU * 36
q = nP / nJobs
r = nP % nJobs
jobs = []
firstRow = 0
for i in range(nJobs):
rowsInJob = q
if (r > 0):
rowsInJob += 1
r -= 1
jobs.append((firstRow, rowsInJob))
firstRow += rowsInJob

queue = mp.JoinableQueue()

for job in jobs:
queue.put(job)
for i in range(nCPU):
queue.put(None)


# run workers
workers = []
for i in range(nCPU):
worker = mp.Process(target = mpCalcDistance_Worker,
args = (nodes, queue, arrD))
workers.append(worker)
worker.start()

queue.join()

# make array from shared memory
D = np.reshape(np.frombuffer(arrD), (nP, nQ))
return D

def compareTimes():

""" Compare execution time single processing versus multiple processing.
"""
nodes = generateNodes(3000)
t0 = time.time()
spD = spCalcDistance(nodes)
t1 = time.time()
print "single process time: {:.3f} s.".format(t1 - t0)

t0 = time.time()

mpD = mpCalcDistance(nodes)
t1 = time.time()
print "multiple processes time: {:.3f} s.".format(t1 - t0)
err = np.linalg.norm(mpD - spD)
print "calculate error: {:.2e}".format(err)
def showTimePlot():
""" Generate execution time plot single processing versus multiple processing.
"""
N = range(100, 4000, 4)
spTimes = []
mpTimes = []
rates = []
for i in N:
print i
nodes = generateNodes(i)
t0 = time.time()
spD = spCalcDistance(nodes)
t1 = time.time()
sp_tt = t1 - t0
spTimes.append(sp_tt)
t0 = time.time()
mpD = mpCalcDistance(nodes)
t1 = time.time()
mp_tt = t1 - t0
mpTimes.append(mp_tt)
rates.append(sp_tt / mp_tt)
plt.figure()
plt.plot(N, spTimes)
plt.plot(N, mpTimes)
plt.xlabel("N")
plt.ylabel("Execution time")
plt.figure()
plt.plot(N, rates)
plt.xlabel("N")
plt.ylabel("Rate")
plt.show()

def main():

compareTimes()
#showTimePlot()

if __name__ == '__main__':

main()