awesome-tensor-compilers

XLA is domain-specific compiler for linear algebra. Triton generates and compiles an intermediate representation for tiled computation. This IR allows more general functions and also claims higher performance. obligatory reference to the family of work: https://github.com/merrymercy/awesome-tensor-compilers

Compiling from high-level lang to GPU is a huge problem, and we greatly appreciate efforts to solve it. If I understand correctly, this (CM) allows for C-style fine-level control over a GPU device as though it were a CPU. However, it does not appear to address data transit (critical for performance). Compilation and operator fusing to minimize transit is possibly more important. See Graphcore Poplar, Tensorflow XLA, Arrayfire, Pytorch Glow, etc. Further, this obviously only applies to Intel GPUs, so investing time in utilizing low-level control is possibly a hardware dead-end. Dream world for programmers is one where data transit and hardware architecture are taken into account without living inside a proprietary DSL Conversely, it is obviously against hardware manufacturers' interests to create this. Is MLIR / LLVM going to solve this? T