Julia JIT support for device APIs
Nobody has claimed this yet.
- Dominant language
- Cython
- Stars
- 601
- Forks
- 46
- PR merge metrics
- No merged PRs in 30d
Description
As header-only libraries based on CUTLASS, these only work in C++.
It would be nice to have e.g. the new DGEMM via IMMA (https://github.com/NVIDIA/CUDALibrarySamples/tree/master/MathDx/cuBLASDx/16_dgemm_emulation) inside Julia kernels.
Unfortunately, Warp/numba can only JIT compile a subset of python types, and not the (more general) Julia types that we require (e.g. differential equation solvers).
A potential way forward would be to compile libmathdx to PTX, link it in via LLVM, and get full performance via LTO. However, these often contain NVVM IR, which LLVM cannot handle.
Related discussion: https://discourse.julialang.org/t/using-cublasdx-in-julia/125527
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked cuBLASDx DGEMM emulation sample and the related Julia discussion. The issue names no repository files, tests, or concrete entry point, and does not define an acceptance test for linking libmathdx, PTX, or NVVM IR into Julia kernels.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, julia, python
- Domain
- compilers, hpc
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100