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Add a benchmark for dask.array.block comparing it to numpy and straight copy #18
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Is this ready to merge? |
self.da_block = da.block(self.block) | ||
self.da_concatenate = da.concatenate(self.arr_list) | ||
if mode.startswith('block optimized'): | ||
self.da_block.dask, _ = fuse_linear(self.da_block.dask) |
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@jakirkham is there a better way to optimize the graph here?
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This is a pretty typical strategy for optimizing. Does a few different things for Dask Arrays in particular.
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Thanks. I would like to get a
- Typical dask
user
(i.e. no optimization called) - Call to block with recommended optimizations to the graph.
- Calling persist with the above combos.
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Calling persist
will trigger the standard Dask Array graph optimizations in either case.
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Got it, I'll remove that "mode".
@mrocklin I guess it wasn't as slow as I remember :S.
Dask is doing better than numpy, at least for now 👯♀️