Fixed #22 Propose new pyfftw_sdp

[NimaSarajpoor]

This PR addresses issue #22. The proposed pyfftw-based sdp will first be added under challenger_sdp.py so that we can compare it against the existing pyfftw_sdp. Eventually, once we are certain that there is no other concern, we will move it to pyfftw_sdp.

[gitnotebooks]

Review these changes at https://app.gitnotebooks.com/stumpy-dev/sliding_dot_product/pull/25

[NimaSarajpoor]

timing.sh:

#!/bin/bash

rm -rf sdp/__pycache__
./timing.py -timeout 5.0 -pmin 2 -pmax 24 -pdiff 100 pyfftw challenger > timing.csv
rm -rf sdp/__pycache__

The performance of challenger relative to existing pyffte_sdp

Observation
The proposed challenger is approximately 1.25x-2x faster than pyfftw_sdp for large T, where len(T) >= 2^15.

[NimaSarajpoor]

@seanlaw
If there is no particular concern on the logic, please let me know and I can move the script to pyfftw_sdp.

[seanlaw]

@NimaSarajpoor Please go ahead!

[NimaSarajpoor]

The script for pyfftw_sdp has been updated to reflect the recent proposal. Given this change to our baseline performance, it is worth it to re-evaluate the performance of other modules relative to this new baseline. The following figure illustrates the performance of pocketfft_r2c_c2r (blue plot) and scipy_oaconvolve (orange plot) benchmarked against the new baseline: pyfftw_sdp.

#!/bin/bash

rm -rf sdp/__pycache__
./timing.py -timeout 5.0 -pmin 6 -pmax 24 pyfftw pocketfft_r2c_c2r scipy_oaconvolve > timing.csv
rm -rf sdp/__pycache__

Observations

• In all cases, pyfftw_sdp outperforms pocketfft_r2c_c2r_sdp
• For len(Q)<2^15, scipy_oaconvolve_sdp outperforms (single-threaded) pyfftw_sdp when len(T) >> len(Q).

Conclusion
For len(Q) >= 2^6, our focus can be on the following two modules:

• scipy_oaconvolve_sdp
• pyfftw_sdp

I think it should be worth it to check the performance of multi-threading pyfftw_sdp and see if that outperforms scipy_oaconvolve_sdp.

[NimaSarajpoor]

I think it is worth evaluating the performance of multi-threaded pyfftw_sdp to see whether it can outperform scipy_oaconvolve_sdp.

The baseline implementation of pyfftw_sdp is single-threaded. In what follows, I explore multi-threaded variants of pyfftw_sdp, where the number of threads (self.threads, see the line below) is set to 2, 4, and 8:

sliding_dot_product/sdp/pyfftw_sdp.py

Line 11 in d8f0423

self.threads = 1

The benchmark was run using the following timing.sh script:

#!/bin/bash

rm -rf sdp/__pycache__
./timing.py -timeout 5.0 -pmin 6 -pmax 24 pyfftw challenger_2threads challenger_4threads challenger_8threads scipy_oaconvolve > timing.csv
rm -rf sdp/__pycache__

Results

Observations

• Multi-threaded pyfftw_sdp does not consistently outperform the single-threaded baseline. In fact, for shorter input arrays, the single-threaded version often performs better.
• pyfftw_sdp with 4 or 8 threads outperforms scipy_oaconvolve.

Side note
It is important to clarify a key assumption underlying this comparison: the FFT planning time in pyfftw is excluded. This assumes that the FFT is applied repeatedly to inputs of the same size, in which case the one-time planning cost (which can also be computed in advance) can reasonably be ignored.
Under this assumption, single-threaded / multi-threaded pyfftw_sdp seems to be the solution we are looking for. However, if the application requires FFTs on new, unforeseen large input sizes, then scipy_oaconvolve may be the better choice, as it does not have to spend time for the "planning" phase.

[NimaSarajpoor]

@seanlaw
Do you think this PR is ready to be merged?

[seanlaw]

Do you think this PR is ready to be merged?

Please give me some time to review it more thoroughly

[seanlaw]

@NimaSarajpoor I basically have one primary suggestion for you to consider but I am not married to it (see below)

[seanlaw]

@NimaSarajpoor I've left some comments for you to consider

[NimaSarajpoor]

@seanlaw
I've addressed the comments. I've also improved the comments and docstrings in pyfftw_sdp.py. Additionally, improved the comment in the new test function that was added recently to test.py.

[seanlaw]

@NimaSarajpoor I think this is ready to be merged. I left a comment for you to consider but feel free to ignore if you disagree as I do not feel strongly about it

[NimaSarajpoor]

@seanlaw
I think this PR is ready. I discovered a small opportunity for improvement and left a comment. Wanted to know your input before I merge this PR.

[NimaSarajpoor]

Thank you @seanlaw for all the comments and feedbacks.