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(Adding the task dependencies for my own reminder.)
Wait for the Halide 16.0 release.
Refactor the Halide::BoundaryConditions calls to use the new APIs;
Similarly, refactor Generator::* related code to use Halide 16.0 APIs;
In algorithms/ladmm.py, ensure all Numpy matrices are Fortran order by default; this avoids the frequent C-order to F-order typecasting overhead in the (L-)ADMM iterations;
Similarly, ensure Halide-accelerated linear operators, e.g. A_mask.cpython.so writes to the output buffers in F-order, not some orphan buffers that are immediately destroyed. This should solve the convergence failure bugs whenever implem='Halide' is defined.
(Adding the task dependencies for my own reminder.)
Halide::BoundaryConditions
calls to use the new APIs;Generator::*
related code to use Halide 16.0 APIs;algorithms/ladmm.py
, ensure all Numpy matrices are Fortran order by default; this avoids the frequent C-order to F-order typecasting overhead in the (L-)ADMM iterations;A_mask.cpython.so
writes to the output buffers in F-order, not some orphan buffers that are immediately destroyed. This should solve the convergence failure bugs wheneverimplem='Halide'
is defined.anderson2021_test_apps_autoscheduler
halide/Halide#7606).C++20
; this should cut the compile time in half thanks to newC++ Concepts
feature;ladmm-iter-gen.cpp
with the broadcast operatorHalide::_
.Li2018
autoscheduler withAnderson2021
: the latter utilizes the GPU cache and shared memory in the SM far better.References:
halide/Halide#6856
halide/Halide#7459
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