Python: parallel code running slower than sequential version
-1
$begingroup$
I have a sequential code where I am counting unique events occurring at a timestamp given the data on time intervals. The sequential code I have prepared is:
a=list of timestamps of size 100.
number=
for i in range(100):
indices=numpy.argwhere((a[i] >= dataset[:,0]) & (a[i] <= dataset[:,1]))[:,0]
number.append(len(set(dataset[indices,2])))
Since the actual size of a is large, it is expected to take large number of days to complete. Therefore, I created a parallel version of the code:
num_cores = multiprocessing.cpu_count()
inputs = range(100)
def processInput(i):
indices=numpy.argwhere((a[i] >= dataset[:,0]) & (a[i] <= dataset[:,1]))[:,0]
return(len(set(dataset[indices,2])))
results = Parallel(n_jobs=num_cores)(delayed(processInput)(i) for i in inputs)
Surprisingly, the sequential version on 100 elements is taking 2 minutes to complete and the parallel version takes about 9 minutes. Why
python performance
asked 50 mins ago
shaifali Guptashaifali Gupta
12
New contributor
shaifali Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
-1
$begingroup$
I have a sequential code where I am counting unique events occurring at a timestamp given the data on time intervals. The sequential code I have prepared is:
a=list of timestamps of size 100.
number=
for i in range(100):
indices=numpy.argwhere((a[i] >= dataset[:,0]) & (a[i] <= dataset[:,1]))[:,0]
number.append(len(set(dataset[indices,2])))
Since the actual size of a is large, it is expected to take large number of days to complete. Therefore, I created a parallel version of the code:
num_cores = multiprocessing.cpu_count()
inputs = range(100)
def processInput(i):
indices=numpy.argwhere((a[i] >= dataset[:,0]) & (a[i] <= dataset[:,1]))[:,0]
return(len(set(dataset[indices,2])))
results = Parallel(n_jobs=num_cores)(delayed(processInput)(i) for i in inputs)
Surprisingly, the sequential version on 100 elements is taking 2 minutes to complete and the parallel version takes about 9 minutes. Why
python performance
asked 50 mins ago
shaifali Guptashaifali Gupta
12
New contributor
shaifali Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
This code looks a lot like your previous question. However, here you are specifically asking why code performs the way it does, rather than asking for suggestions on how to improve the code, so your question is off-topic for Code Review, and should be asked on Stack Overflow.
$endgroup$
– 200_success
15 mins ago
add a comment |
-1
-1
-1
$begingroup$
I have a sequential code where I am counting unique events occurring at a timestamp given the data on time intervals. The sequential code I have prepared is:
a=list of timestamps of size 100.
number=
for i in range(100):
indices=numpy.argwhere((a[i] >= dataset[:,0]) & (a[i] <= dataset[:,1]))[:,0]
number.append(len(set(dataset[indices,2])))
Since the actual size of a is large, it is expected to take large number of days to complete. Therefore, I created a parallel version of the code:
num_cores = multiprocessing.cpu_count()
inputs = range(100)
def processInput(i):
indices=numpy.argwhere((a[i] >= dataset[:,0]) & (a[i] <= dataset[:,1]))[:,0]
return(len(set(dataset[indices,2])))
results = Parallel(n_jobs=num_cores)(delayed(processInput)(i) for i in inputs)
Surprisingly, the sequential version on 100 elements is taking 2 minutes to complete and the parallel version takes about 9 minutes. Why
python performance
asked 50 mins ago
shaifali Guptashaifali Gupta
12
New contributor
shaifali Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a sequential code where I am counting unique events occurring at a timestamp given the data on time intervals. The sequential code I have prepared is:
a=list of timestamps of size 100.
number=
for i in range(100):
indices=numpy.argwhere((a[i] >= dataset[:,0]) & (a[i] <= dataset[:,1]))[:,0]
number.append(len(set(dataset[indices,2])))
Since the actual size of a is large, it is expected to take large number of days to complete. Therefore, I created a parallel version of the code:
num_cores = multiprocessing.cpu_count()
inputs = range(100)
def processInput(i):
indices=numpy.argwhere((a[i] >= dataset[:,0]) & (a[i] <= dataset[:,1]))[:,0]
return(len(set(dataset[indices,2])))
results = Parallel(n_jobs=num_cores)(delayed(processInput)(i) for i in inputs)
Surprisingly, the sequential version on 100 elements is taking 2 minutes to complete and the parallel version takes about 9 minutes. Why
python performance
python performance
asked 50 mins ago
shaifali Guptashaifali Gupta
12
New contributor
shaifali Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 50 mins ago
shaifali Guptashaifali Gupta
12
New contributor
shaifali Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 50 mins ago
shaifali Guptashaifali Gupta
12
New contributor
shaifali Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 50 mins ago
shaifali Guptashaifali Gupta
12
asked 50 mins ago
shaifali Guptashaifali Gupta
12
12
New contributor
shaifali Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
shaifali Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
shaifali Gupta is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
This code looks a lot like your previous question. However, here you are specifically asking why code performs the way it does, rather than asking for suggestions on how to improve the code, so your question is off-topic for Code Review, and should be asked on Stack Overflow.
$endgroup$
– 200_success
15 mins ago
add a comment |
$begingroup$
This code looks a lot like your previous question. However, here you are specifically asking why code performs the way it does, rather than asking for suggestions on how to improve the code, so your question is off-topic for Code Review, and should be asked on Stack Overflow.
$endgroup$
– 200_success
15 mins ago
$begingroup$
This code looks a lot like your previous question. However, here you are specifically asking why code performs the way it does, rather than asking for suggestions on how to improve the code, so your question is off-topic for Code Review, and should be asked on Stack Overflow.
$endgroup$
– 200_success
15 mins ago
$begingroup$
This code looks a lot like your previous question. However, here you are specifically asking why code performs the way it does, rather than asking for suggestions on how to improve the code, so your question is off-topic for Code Review, and should be asked on Stack Overflow.
$endgroup$
– 200_success
15 mins ago
add a comment |
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$begingroup$
This code looks a lot like your previous question. However, here you are specifically asking why code performs the way it does, rather than asking for suggestions on how to improve the code, so your question is off-topic for Code Review, and should be asked on Stack Overflow.
$endgroup$
– 200_success
15 mins ago