Inspired by a little experiment we did to see if there is room to
improve to ECL's bignum performance, we ran a little experiment
computing fibonacci numbers (we wanted to test addition because it was
mainly ECL's memory management that was under consideration)
with the following defs:
def fibo(n):
a=1
b=1
c=0
for i in range(n):
c=a
a=a+b
b=c
return b
def test(n,m):
for i in range(m):
_=fibo(n)
sage: timeit("test(4000,4000)")
5 loops, best of 3: 6.99 s per loop
sage: time test(4000,4000)
CPU times: user 7.10 s, sys: 0.00 s, total: 7.10 s
Wall time: 7.11 s
sage: time test(4000,4000)
CPU times: user 7.24 s, sys: 0.00 s, total: 7.24 s
Wall time: 7.24 s
sage: time test(4000,4000)
CPU times: user 7.38 s, sys: 0.00 s, total: 7.38 s
Wall time: 7.39 s
sage: time test(4000,4000)
CPU times: user 7.10 s, sys: 0.00 s, total: 7.10 s
Wall time: 7.11 s
sage: time test(4000,4000)
CPU times: user 7.05 s, sys: 0.00 s, total: 7.05 s
Wall time: 7.06 s
In ECL, this took 14.8 sec (uncompiled) and 8.8 sec (compiled). Of
course, this particular programming exercise was just to see how fast
one can shove information into GMP and ECL at this point definitely
doesn't claim to be particularly optimized for that particular task,
but as you can see, straightforward python and sage do a good job.
For comparison, Magma takes about 8.4 secs, as does the code above
when I initialize a=int(1) etc.
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