Research

We present LoopStack, a domain-specific compiler stack for tensor operations, composed of a front-end, LoopTool, and an efficient optimizing code generator, LoopNest. LoopStack is designed to produce highly efficient but also predictable code. Such a design allows both experts, and more importantly, ML–based approaches to find good schedules (algorithms). LoopStack is extensible and supports various processors and accelerators while incorporating HPC optimizations often missing from other machine learning compiler back-ends. To show the quality of the generated code we designed a rudimentary AI to search for schedules and compare the speed of generated code with the most optimized, hand-tuned libraries. Further, we show that for a large collection of schedules LoopNest’s compilation is orders of magnitude faster than LLVM, while resulting in equal or improved run time performance.