The last couple of weeks were pretty productive, despite the heavy academic load of the semester. For this release, I wanted to focus mainly on ClangIR. It feels natural to follow the momentum I've built on this project; ultimately, it is the area I feel most comfortable with and the one that has pushed me to learn the most about compilers.
I completed two main PRs: one focused on backporting work done in October to the incubator, and the other focused on implementing missing CUDA features within CIR.
Backporting AddressSpaces from Upstream - ClangIR
Link: https://github.com/llvm/clangir/pull/1986
At the time of writing, I am still addressing some feedback on this PR. The main idea, which I blogged about previously, is to model how different offload programming languages handle memory address representations on various hardware. This matters because utilizing specific memory locations can drastically reduce latency and offer orders of magnitude in performance improvements—this is certainly true for shared memory located within a GPU workgroup/wave.
The attribute was modeled on top of pointer types. Initially, the implementation was geared towards representing target address spaces. The main challenge of backporting this to the incubator was managing the compatibility with language-specific address spaces as well. In theory, you could attach either a target or a language-specific attribute to a pointer. I am currently implementing an interface to make both generic, but it is proving difficult due to the intrinsic complexities of TableGen and MLIR definitions.
Adding Support for Stream-Per-Thread in CUDA - ClangIR
Link: https://github.com/llvm/clangir/pull/1997
One of the perks of contributing to LLVM is learning from the "gold standard" implementations of C-like languages within Clang's ecosystem.
In GPU programming, streams act as a command buffer between the host and the device. Operations sent to a stream are executed in the exact order they are received. In older CUDA versions, the default stream was shared across all host threads, meaning massive queues could cause false serialization and performance overhead. (See: Nvidia Blog on CUDA 7 Streams).
While older CUDA versions didn't support stream-per-thread, it has become the norm in modern versions.
In reflection... You'll find that the Clang code generation pipeline holds an embedded runtime for CUDA, OpenCL, and HIP. The main takeaway is that many features have a clear baseline in ClangIR compared to the original CodeGen implementation. I didn't need extensive domain-specific CUDA knowledge to support this; it is essentially a driver flag that instructs the compiler to alter the default stream semantics during code generation.
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