Dagger.jl Fast, Smart Parallelism

DaggerGPU 0.1.6 Release Notes

Summary of key changes:

  • Package extensions and ROCm/AMD revamp (#18)

While only consisting of a single PR, this release is a big step towards making DaggerGPU suitable for more use cases. First and foremost, this release adds proper support for computing with AMDGPU.jl, by making use of the new per-task synchronization API, and by updating to the latest version of KernelAbstractions.jl.

Additionally, DaggerGPU now uses package extensions on Julia 1.9+, which makes it possible to not have to load all of the GPU backends just to use DaggerGPU with a single backend. We still keep Requires around for 1.7 and 1.8 support, but I would expect that to go away in the not-too-distant future.

Along the way, I also updated the tests to be a bit more thorough and correct across all the backends, which should help ensure good coverage of our supported backends.

That's it for this release, but I expect to be doing some work focused on KernelAbstractions support in the near future, to make it easier to port GPU kernel code to Dagger directly without extra boilerplate.

CC BY-SA 4.0 Julian P Samaroo. Last modified: June 29, 2023.
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