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Fixed#938: Speed up njit-decorated function for sliding dot product [WIP] #939
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Codecov Report
All modified and coverable lines are covered by tests ✅
Comparison is base (
857063c) 98.93% compared to head (2c5dd4d) 98.93%.
Report is 4 commits behind head on main.
Additional details and impacted files
@@ Coverage Diff @@ ## main #939 +/- ## ======================================= Coverage 98.93% 98.93% ======================================= Files 84 84 Lines 14292 14292 ======================================= Hits 14140 14140 Misses 152 152
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@seanlaw
I think the previous notebook got too messy and there were different versions there. I did not remove it (I just kept it for our future reference). Instead, I just pushed a new notebook (see c2a9b43). My goal is to add functions step by step based on the discussions we have had so far on this PR and #938.
For now I just resolved the previously- provided comments (by you and me) that were regarding the initial notebook. I will consider them in this new notebook though.
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Line #20. x[:] = scipy.fft.fft(x) # we will implement our fft shortly!
We will later work on implementing this fft function using 6-step / 8-step algorithm. We then change this line and the caller function accordingly.
Reply via ReviewNB
This PR addresses the issue raised in #938. We want to create a new function for
sliding_dot_productthat:(1) has a performance that is close to the performance of
core.sliding_dot_product(2) can be called by a njit-decorated function
Currently, the existing alternative to
core.sliding_dot_productiscore._sliding_dot_product; however, in contrast tocore.sliding_dot_product, its performance is NOT independent ofm, the length of query.As suggested by @seanlaw, we are going to try OTFFT to implement fft and ifft. Then, we use them to create a new function for
sliding_dot_product.