simple-cpp-python/script.py at master · fteicht/simple-cpp-python · GitHub

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importbuild.__simple_cpp_pythonaswow
importgetopt
importmath
importoperator
importtime
importsys
importmatplotlib.pyplotasplt
frompathosimportmultiprocessing
fromfunctoolsimportreduce
fromcollectionsimportdefaultdict
frompandasimportDataFrame

defpython_inner_loop(M):
# computes pi/8 as a series
returnreduce(operator.add, [1/ ((4*k+1) * (4*k+3)) forkinrange(M)], 0)

defpython_outer_loop(xf, N, M):
# computes the series of cos(n * theta) + sin(n * theta)
pool=multiprocessing.Pool()
v=pool.map(lambdan: math.cos((n+1) *xf(M)) +math.sin((n+1) *xf(M)), [nforninrange(N)])
returnreduce(operator.add, v, 0)

deffull_python(N, M):
returnpython_outer_loop(python_inner_loop, N, M)

defmixed_op(pool, lr, n, M):
lr[n] =pool.apply_async(lambda : python_inner_loop(M))

defmixed_get(lr, n):
returnlr[n].get()


if__name__=="__main__":

try:
options, remainder=getopt.getopt(sys.argv[1:],
"x:n:",
 ["xarg=", "narg="])
exceptgetopt.GetoptErroraserr:
# print help information and exit:
print(err) # will print something like "option -a not recognized"
usage()
sys.exit(2)

N=1000

foropt, arginoptions:
ifoptin ('-n', '--narg'):
n=int(arg)

start=time.time()
r=full_python(N, 10*N)
end=time.time()
print('full python result: '+str(r) +' in '+str(end-start) +' seconds')

start=time.time()
r=wow.full_cpp(N, 10*N)
end=time.time()
print('full c++ result: '+str(r) +' in '+str(end-start) +' seconds')

start=time.time()
r=wow.cpp_outer_loop_gil(lambdaM: python_inner_loop(M), N, 10*N)
end=time.time()
print('mixed result (GIL): '+str(r) +' in '+str(end-start) +' seconds')

start=time.time()
pool=multiprocessing.Pool()
lr= [None]*N
r=wow.cpp_outer_loop_process(lambdan, M : mixed_op(pool, lr, n, M), lambdan : mixed_get(lr, n), N, 10*N)
pool.close()
end=time.time()
print('mixed result (process): '+str(r) +' in '+str(end-start) +' seconds')

x=math.pi/8
r=0.5* ((1.0/math.tan(x/2)) -1+ (math.sin(x* (N+0.5)) -math.cos(x* (N+0.5))) /math.sin(x/2))
print('exact result: '+str(r))

# Statistical analysis
print('Statistic analysis...')

N=1000
d=defaultdict(list)
index= [100, 1000, 10000, 100000, 1000000]

forMinindex:
print("N={0}, M={1}".format(N, M))

start=time.time()
full_python(N, M)
end=time.time()
ref=end-start
d['full_python'].append(1.0)
print(' full python:', str(end-start))

start=time.time()
wow.cpp_outer_loop_gil(lambdam: python_inner_loop(m), N, M)
end=time.time()
d['mixed_gil'].append(ref/ (end-start))
print(' mixed gil:', str(end-start))

start=time.time()
pool=multiprocessing.Pool()
lr= [None]*N
wow.cpp_outer_loop_process(lambdan, m : mixed_op(pool, lr, n, m), lambdan : mixed_get(lr, n), N, M)
pool.close()
end=time.time()
d['mixed_process'].append(ref/ (end-start))
print(' mixed process:', str(end-start))

start=time.time()
wow.full_cpp(N, M)
end=time.time()
d['full_cpp'].append(ref/ (end-start))
print(' full cpp:', str(end-start))

df=DataFrame(data=d, index=index)
df.plot.bar(rot=0, logy=True)
plt.xlabel('M')
plt.ylabel('(log) speedup compared to full python')
plt.axhline(y=1, linewidth=3, color='gray', linestyle='dashed')
plt.savefig('comparison_M.png', transparent=True)
plt.show()

M=10000
d=defaultdict(list)
index= [10, 100, 1000, 10000, 100000]

forNinindex:
print("N={0}, M={1}".format(N, M))

start=time.time()
full_python(N, M)
end=time.time()
ref=end-start
d['full_python'].append(1.0)
print(' full python:', str(end-start))

start=time.time()
wow.cpp_outer_loop_gil(lambdam: python_inner_loop(m), N, M)
end=time.time()
d['mixed_gil'].append(ref/ (end-start))
print(' mixed gil:', str(end-start))

start=time.time()
pool=multiprocessing.Pool()
lr= [None]*N
wow.cpp_outer_loop_process(lambdan, m : mixed_op(pool, lr, n, m), lambdan : mixed_get(lr, n), N, M)
pool.close()
end=time.time()
d['mixed_process'].append(ref/ (end-start))
print(' mixed process:', str(end-start))

start=time.time()
wow.full_cpp(N, M)
end=time.time()
d['full_cpp'].append(ref/ (end-start))
print(' full cpp:', str(end-start))

df=DataFrame(data=d, index=index)
df.plot.bar(rot=0, logy=True)
plt.xlabel('N')
plt.ylabel('(log) speedup compared to full python')
plt.axhline(y=1, linewidth=3, color='gray', linestyle='dashed')
plt.savefig('comparison_N.png', transparent=True)
plt.show()