Parallelise dials.rs_mapper
#2238
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Working towards each process having its own grid and counts arrays
I checked that the output map is identical between this branch and main for the dials.data insulin dataset. On the branch this was split over 5 blocks and took about 3 seconds compared with 5 seconds on main. Memory requirements on the branch were a bit less than double that on main (810 MB compared with 450 MB). For the thaumatin dataset, the branch took 7s compared to 24s on main, with the job split over 16 processes. Memory required went up to 1.7 GB from 520 MB.
Codecov Report
@@ Coverage Diff @@
## main #2238 +/- ##
==========================================
- Coverage 80.99% 80.46% -0.54%
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Files 588 587 -1
Lines 66876 66870 -6
Branches 9440 8888 -552
==========================================
- Hits 54169 53806 -363
- Misses 10614 10999 +385
+ Partials 2093 2065 -28 |
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Perform the
rs_mappercalculation in parallel. The resulting map files are identical. Testing using the thaumatin dataset ondials.datashowed that with 16 parallel processes, the program completed 3.4 times faster (7s compared to 24s). With the default grid size the memory requirements increased by a similar factor, but are still modest (1.7 GB compared to 520 MB).The PR also introduces
loggingto replaceprints, though there is still not yet much console output.