Note: this dialect is more likely to change than others in the near future; use with caution.
This dialect provides middle-level abstractions for launching GPU kernels following a programming model similar to that of CUDA or OpenCL. It provides abstractions for kernel invocations (and may eventually provide those for device management) that are not present at the lower level (e.g., as LLVM IR intrinsics for GPUs). Its goal is to abstract away device- and driver-specific 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.
Memory buffers are defined at the function level, either in “gpu.launch” or in “gpu.func” ops. This encoding makes it clear where the memory belongs and makes the lifetime of the memory visible. The memory is only accessible while the kernel is launched/the function is currently invoked. The latter is more strict than actual GPU implementations but using static memory at the function level is just for convenience. It is also always possible to pass pointers to the workgroup memory into other functions, provided they expect the correct memory space.
The buffers are considered live throughout the execution of the GPU function body. The absence of memory attribution syntax means that the function does not require special buffers. Rationale: although the underlying models declare memory buffers at the module level, we chose to do it at the function level to provide some structuring for the lifetime of those buffers; this avoids the incentive to use the buffers for communicating between different kernels or launches of the same kernel, which should be done through function arguments instead; we chose not to use
alloca-style approach that would require more complex lifetime analysis following the principles of MLIR that promote structure and representing analysis results in the IR.