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View Code? Open in Web Editor NEWDatabase approach for generating near-roofline Einstein Summation kernels.
Home Page: https://feinsum.readthedocs.io/
License: MIT License
Database approach for generating near-roofline Einstein Summation kernels.
Home Page: https://feinsum.readthedocs.io/
License: MIT License
This should allow us to handle operands that depend on multiple uses.
cl.Context does NOT define the device on which the transformation will be run.
The following array packages come to mind:
This would be really significant for memory-bound batched einsums as this opens up the possibility of sharing operands via lower latency address spaces.
Suggested by @inducer.
With the additional assumption that all the operands are non-negative, the transformations (both algebraic and loop) would be sound (I think) to translate from their Einstein summation equivalents.
We should implement our own search-space exploration techniques.
Causing compilation to trigger multiple times i.e. ~40% of the time spent in a run is redundant.
Checks like:
transform_id
exist under tuning/impls/
.Assuming the code generator follows a naming scheme for specifying transformations is brittle.
https://github.com/illinois-ceesd/mirgecom/blob/main/.rtd-env-py3.yml provides a good example.
Easier debugging + significantly improves the legibility of the DB.
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