[Mlir-commits] [mlir] [mlir] Document GPU dialect layering to capture discussions from a PR (PR #95812)

Guray Ozen llvmlistbot at llvm.org
Tue Jun 18 09:59:53 PDT 2024


================
@@ -12,8 +12,36 @@ manipulations to launch a GPU kernel and provide a simple path towards GPU
 execution from MLIR. It may be targeted, for example, by DSLs using MLIR. The
 dialect uses `gpu` as its canonical prefix.
 
+This dialect also abstracts away primitives commonly available in GPU code, such
+as with `gpu.thread_id` (an operation that returns the ID of threads within
+a thread block/workgroup along a given dimension). While the compilation
+pipelines documented below expect such code to live inside a `gpu.module` and
+`gpu.func`, these intrinsic wrappers may be used outside of this context.
+
+Intrinsic-wrapping operations should not expect that they have a parent of type
+`gpu.func`. However, operations that deal in compiling and launching GPU functions,
+like `gpu.launch_func` or `gpu.binary` may assume that the dialect's full layering
+is being used.
+
 [TOC]
 
+## GPU address spaces
+
+The GPU dialect exposes the `gpu.address_space` attribute, which currently has
+three values: `global`, `workgroup`, and `private`.
+
+These address spaces represent the types of buffer commonly seen in GPU compilation:.
+`global` memory is memory that resides in the GPU's global memory and is commonly
+used for function arguments. `workgroup` memory is a limited, per-workgroup resource:
+all threads in a workgroup/thread block access the same values in `worgroup` memory,
+but cannot access the `workgroup` memory of other workgroups. Finally, `private`
+memory is used to represent `alloca`-like buffers that are private to a sigle thread.
----------------
grypp wrote:

The private memory part remains the same. I deleted 1) global memory is commonly kernel functions, because there are multiple ways to allocate global memory (e.g. malloc in device) 2) workgroup memory cannot be accesses from other workgroups. This is not true anymore for NV GPUs. 

https://github.com/llvm/llvm-project/pull/95812


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