JuliaGPU / JuliaGPU/AMDGPU.jl

Support packed FP16 operations

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#335 3 comments 0 reactions 0 assignees View on GitHub

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codegen enhancement intrinsics performance
Dominant language
Julia
Stars
344
Forks
79
Avg merge
2d 23h
Merged PRs (30d)
27

Description

Currently, the intrinsics we expose for Float16 inputs accept scalar inputs, however, there exists many math intrinsics that take Tuple{Float16,Float16}, with the intention to be able to use packed math instructions most effectively. While we certainly can expose these intrinsics directly to the user, we should also consider if there is some optimization available (either through LLVM, or manually implemented here) to fuse non-packed F16 operations into packed 2F16 operations.

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First steps

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  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

No file, test, or entry point is named. Start by locating the Float16 intrinsic definitions and the handling of Tuple{Float16,Float16}, then inspect the relevant LLVM optimization path; the scope is resolved when the project has a defined way to support or evaluate fusion into packed 2F16 operations.

Written by the indexing model from the issue text.

Assessment

Tech stack
julia
Domain
backend, performance
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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