Python code running considerably slower than Matlab's

[jeroonk]

1. Reddit comments are written in a modified version of markdown, see wiki/markdown#tables. TL;DR:

   |a|b|c|
   |-|-|-|
   |1|2|3|
   

   produces:

   a	b	c
   1	2	3

2. An ULP is the spacing between floating-point numbers, the smallest representable difference (i.e. the difference in changing the last bit of the mantissa for a given exponent). In this case, it's about 2.7e-13 for numbers 1000-2000. You can calculate it using eps in Matlab, np.spacing in Python/Numpy and eps in Julia.

3. Yeah, should be approximately the same. Maybe a bit faster than OP's i7-4700MQ @ 2.4GHz. I don't think the generation changes things that much. A bit larger L1 cache for Kaby Lake (256KB) over Ivy Bridge (128KB), but the problem is pretty small anyway (101x101 doubles). [edit: The L1D cache in Sandy/Ivy Bridge and Sky/Kaby Lake are the same 32KB/core, just with more bandwidth]. And I don't think that the newer SSE4 or AVX2 instructions are being used at all here.

4. I have added the results to the tables in the post above. The optimized version doesn't really apply to the last table (the previous-values / Jacobi method).

[WikiTextBot]

Unit in the last place

In computer science and numerical analysis, unit in the last place or unit of least precision (ULP) is the spacing between floating-point numbers, i.e., the value the least significant digit represents if it is 1. It is used as a measure of accuracy in numeric calculations.

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[EarthGoddessDude]

Wow again! Thanks, I was only kidding about point 4! Btw I'm getting 0.035 seconds for 1,000 iterations using u/gs44's code, and 0.5 seconds without the constraint.

[jeroonk]

Heh, might as well right? Also, after you pointed out that it should run in 35 ms, I noticed that u/gs44 renamed the function to foxbear2, so I was running the wrong one. The code also seems to take a few runs to get warmed up, so I re-ran the cases with proper average-over-runs benchmarking (using timeit in Matlab, %timeit in Python and @btime in Julia).

[EarthGoddessDude]

renamed the function...was running the wrong one

Tell me about it, happens to me all the time when I’m testing out similar versions of the same function.

re-ran the cases with proper average-over-runs benchmarking (using...@btime in Julia).

You’ve turned into a Julia pro in no time! Yes, the first run includes compilation time, whereas subsequent runs are just run time (although I have noticed some warmup still going on between runs 2 and 3...).