[REVIEW] Improve memory scaling for low average vertex degree graphs & many GPUs #1823
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…ate thrust::tuple_cat
seunghwak
added
Graph Prims
improvement
seunghwak
changed the title
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rerun tests |
seunghwak
changed the title
seunghwak
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3 - Ready for Review
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2 - In Progress
DO NOT MERGE
Codecov Report
@@ Coverage Diff @@
## branch-21.10 #1823 +/- ##
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+ Coverage 59.85% 69.57% +9.72%
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Files 77 139 +62
Lines 3547 8645 +5098
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+ Hits 2123 6015 +3892
- Misses 1424 2630 +1206 Continue to review full report at Codecov.
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rerun tests |
ChuckHastings
approved these changes
Sep 21, 2021
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@gpucibot merge |
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Assuming sqrt(P) is an integer, matrix row/column property array sizes in each GPU scale as V/sqrt(P) while the storage requirement for graph edges scale as E/P. If E/V is small and P is large, the O(V/sqrt(P)) part will dominate the memory requirement and analyzing N times larger graphs will require N^2 times more GPUs; this is unacceptable. However, in this case, at most E/P elements of the V/sqrt(P) sized array will be accessed, so no need to store the whole V/sqrt(P) values. Instead, we can store row/column properties in (key, value) pairs limiting the memory requirement to be the minimum of V/sqrt(P) and E/P.
This PR supports storing matrix row/column properties in (key, value) pairs if the percentage of actually accessed elements is lower than the threshold value (the code has been tested only up to 8 GPUs, and there was no clear benefit at this scale; currently the threshold value is set to 0 and (key, value) pair support is never enabled, but the threshold value will be adjusted later after large scale testing).