Optimize series.rolling.sum()

[densmirn]

Previous implementation results:

name	nthreads	type	size	median
Series.rolling.sum	4	Python	10000000	0.616
Series.rolling.sum	4	SDC	10000000	4.544

Optimized implementation results:

name	nthreads	type	size	median
Series.rolling.sum	4	Python	10000000	0.552
Series.rolling.sum	4	SDC	10000000	0.053

The optimized implementation executes faster up to ~85 times than previous one and faster up to ~10 times than Python. There is no scalability due to prange isn't used at all because variable nfinite (number of finite values) is common for all threads.

[AlexanderKalistratov]

Does it helped? Can't wait to know the result 😄

BTW you are not going to write all this monstrous code for every rolling function, don't you?

[AlexanderKalistratov]

I'm expecting to see generic implementation for the most of series methods something like this:

windows= [WindowKind(window_size)]
foriinrange(1, len(chunks)):
windows.append(WindowKind(window_size))
foriinprange(len(chunks)):
chunk=chunks[i]
window=windows[i]
prelude_start=max(0, chunk.start-window_size)
prelude_stop=max(0, chunk.start)
forjinrange(interlude_start, interlude_stop):
window.add(data, j)
forjinrange(chunk.start, chunk.stop)
window.add(data, j)
result[j] =window.get_result()

This is a pseudocode. You need to think about exact details

accidental approval

[pep8speaks]

Hello @densmirn! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2020-02-20 05:35:37 UTC

[densmirn]

Current performance:

name	nthreads	type	size	median
Series.rolling.sum	1	Python	200000	1.219
Series.rolling.sum	1	SDC	200000	0.905
Series.rolling.sum	4	Python	200000	1.23
Series.rolling.sum	4	SDC	200000	0.409

Python 1 / SDC 4 = 2,98
The scalability was enabled.

[densmirn]

Current performance:

name	nthreads	type	size	median
Series.rolling.sum	1	Python	800000	3.903
Series.rolling.sum	1	SDC	800000	0.517
Series.rolling.sum	4	Python	800000	3.947
Series.rolling.sum	4	SDC	800000	0.254

Python 1 / SDC 1 = 7.549
Python 1 / SDC 4 = 15,366

Remeasured linear implementation b2a4d9d:

name	nthreads	type	size	median
Series.rolling.sum	1	Python	800000	4.01
Series.rolling.sum	1	SDC	800000	0.401

SDC_LINEAR 1 / SDC_PARALLEL 1 = 0.776
SDC_LINEAR 1 / SDC_PARALLEL 4 = 1.579

I think it's a victory.

[AlexanderKalistratov]

Please consider the following option:

@sdc_register_jitabledefresult_or_nan(nfinite, minp, result):
ifnfinite<minp:
returnnumpy.nanreturnresultdefgen_sdc_pandas_series_rolling_impl(pop, put, init_result=numpy.nan):
"""Generate series rolling methods implementations based on pop/put funcs"""defimpl(self):
win=self._windowminp=self._min_periodsinput_series=self._datainput_arr=input_series._datalength=len(input_arr)
output_arr=numpy.empty(length, dtype=float64)
chunks=parallel_chunks(length)
foriinprange(len(chunks)):
chunk=chunks[i]
nfinite=0result=init_resultprelude_start=max(0, chunk.start-win+1)
prelude_stop=min(chunk.start, prelude_start+win)
interlude_start=prelude_stopinterlude_stop=min(prelude_start+win, chunk.stop)
foridxinrange(prelude_start, prelude_stop):
value=input_arr[idx]
nfinite, result=put(value, nfinite, result)
foridxinrange(interlude_start, interlude_stop):
value=input_arr[idx]
nfinite, result=put(value, nfinite, result)
output_arr[idx] =result_or_nan(nfinite, minp, result)
foridxinrange(interlude_stop, chunk.stop):
put_value=input_arr[idx]
pop_value=input_arr[idx-win]
nfinite, result=put(put_value, nfinite, result)
nfinite, result=pop(pop_value, nfinite, result)
output_arr[idx] =result_or_nan(nfinite, minp, result)
returnpandas.Series(output_arr, input_series._index,
name=input_series._name)
returnimpl

It's not the most elegant one, but it could give us some performance (due to elimination of condition in loop and extra counter). If it doesn't, your solution is preferable.

Also, I've changed order of put and pop (firstly put, then pop). It shouldn't affect sum, but could be useful for min and max - if we have added new min/max - we don't need to recalculate result

[densmirn]

I didn't get visible result, but I like the code. So let me apply the patch.

[AlexanderKalistratov]

Also please keep in mind, that for some functions you need to keep more than one result (e.g. variance)