Ran through roughly 40k detectorVisits of the HSC RC2 dataset though the w_2021_39 version of calibrate.py and this ticket's updated matcher with reduced pre-computation and memory usage.
I wrapped a timer around the solveAndFit loop in AstrometryTask and found these headline numbers in seconds:
w_2021_39:
Mean: 3.389, STD: 4.112
Percentiles: 5%, 25%, 50%, 75%, 95%
1.148, 1.546, 2.107, 3.513, 10.473
Min: 0.699, Max: 178.790
this ticket:
Mean: 3.451, STD: 4.542
Percentiles: 5%, 25%, 50%, 75%, 95%
1.045, 1.422, 2.003, 3.560, 11.313
Min: 0.630, Max: 201.265
Overall, not a significantly different change in timing and up till 75% the timing is faster or about just as fast.
The question remaining for this ticket is if this is worth merging now and reap the benefit of a smaller memory footprint with the slightly slower performance for the mean and outlier data or wait for a ticket to rewrite one of the inner loops in C++/pybind11 to gain the slight speed hit back.
Ran through roughly 40k detectorVisits of the HSC RC2 dataset though the w_2021_39 version of calibrate.py and this ticket's updated matcher with reduced pre-computation and memory usage.
I wrapped a timer around the solveAndFit loop in AstrometryTask and found these headline numbers in seconds:
w_2021_39:
Mean: 3.389, STD: 4.112
Percentiles: 5%, 25%, 50%, 75%, 95%
1.148, 1.546, 2.107, 3.513, 10.473
Min: 0.699, Max: 178.790
this ticket:
Mean: 3.451, STD: 4.542
Percentiles: 5%, 25%, 50%, 75%, 95%
1.045, 1.422, 2.003, 3.560, 11.313
Min: 0.630, Max: 201.265
Overall, not a significantly different change in timing and up till 75% the timing is faster or about just as fast.
The question remaining for this ticket is if this is worth merging now and reap the benefit of a smaller memory footprint with the slightly slower performance for the mean and outlier data or wait for a ticket to rewrite one of the inner loops in C++/pybind11 to gain the slight speed hit back.