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Hi, I just found the Jupyter session is seriously slower than the SageNB one.

This was the snippet to reproduce:

# intersection profiling with Python session
from sage.all import *

def run(num):
    cube = (polytopes.cube() * 37 / 45).change_ring(QQ) 

    for i in range(num):
        hspace = Polyhedron(ieqs=[[1/2, 1/(i+1), 0, 0]]).change_ring(QQ)
        intersection = hspace.intersection(cube)

%timeit run(100)

The timing in SageNB is 1 loop, best of 5: 186 ms per loop, while that in Jupyter is 1 loop, best of 5: 7.07 s per loop. With this snippet I think no variable was cached. I also cprofiled a stand-alone .py file with the same lines (expect the %timeit line), whose timing is close to the Jupyter one. Any idea what could cause this huge performance difference?

Hi, I just found the Jupyter session is seriously slower than the SageNB one.

This was the snippet to reproduce:

# intersection profiling with Python session
from sage.all import *

def run(num):
    cube = (polytopes.cube() * 37 / 45).change_ring(QQ) 

    for i in range(num):
        hspace = Polyhedron(ieqs=[[1/2, 1/(i+1), 0, 0]]).change_ring(QQ)
        intersection = hspace.intersection(cube)

%timeit run(100)

The timing in SageNB is 1 loop, best of 5: 186 ms per loop, while that in Jupyter is 1 loop, best of 5: 7.07 s per loop. With this snippet I think no variable was cached. I also cprofiled a stand-alone .py file with the same lines (expect the %timeit line), whose timing is close to the Jupyter one. Any idea what could cause this huge performance difference?

Hi, I just found the Jupyter session Python kernel is seriously slower than the SageNB SageMath one.

This was the snippet to reproduce:

# intersection profiling with Python session
from sage.all import *

def run(num):
    cube = (polytopes.cube() * 37 / 45).change_ring(QQ) 

    for i in range(num):
        hspace = Polyhedron(ieqs=[[1/2, 1/(i+1), 0, 0]]).change_ring(QQ)
        intersection = hspace.intersection(cube)

%timeit run(100)

The timing in SageNB for SageMath kernel is 1 loop, best of 5: 186 ms per loop, while that in Jupyter for Python is 1 loop, best of 5: 7.07 s per loop. With this snippet I think no variable was cached. I also cprofiled a stand-alone .py file with the same lines (expect the %timeit line), whose timing is close to the Jupyter one. Python kernel. Any idea what could cause this huge performance difference?

Hi, I just found the Python kernel is seriously slower than the SageMath one.

This was the snippet to reproduce:

# intersection profiling with Python session
from sage.all import *

def run(num):
    cube = (polytopes.cube() * 37 / 45).change_ring(QQ) 

    for i in range(num):
        hspace = Polyhedron(ieqs=[[1/2, 1/(i+1), 0, 0]]).change_ring(QQ)
        intersection = hspace.intersection(cube)

%timeit run(100)

The timing for SageMath kernel is 1 loop, best of 5: 186 ms per loop, while that for Python is 1 loop, best of 5: 7.07 s per loop. With this snippet I think no variable was cached. I also cprofiled a stand-alone .py file with the same lines (expect the %timeit line), whose timing is close to the Python kernel. Any idea what could cause this huge performance difference?

Image which kernel to use