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Handling larger clusters #13
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Following table shows how much time each benchmark spends on extent.py.
After migrating extent.py to Cython and replacing tuples with C arrays, these benchmark spend less than 1% on extent.py (not committed yet). |
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When we have arrays with more than a few hundred tiles, I've noticed that our performance drops significantly; this is almost certainly due to the various extent operations needed to compute tiles. We can move the extent code to Cython which would give us a big speedup.
Also, the vast majority of arrays have tiles that are all the same shape; we can leverage this to avoid scanning a tile list, and instead use the tile shape to find the target tile, e.g.
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