Compiling for Vector Extensions With Stream-Based Specialization

To overcome the current performance wall, data streaming and data-flow computing paradigms have been gradually making their way into the general-purpose domain. However, the proliferation of such paradigms is often hindered by the lack of compilation support, as their execution model is usually incompatible with the internal static single-assignment form used in modern compilers. Accordingly, we propose a new compilation flow that leverages the LLVM infrastructure to automatically extract and encode the memory access pattern and computation data-flow graph with streaming representations. The proposed compilation flow is used to generate code for the recently presented Unlimited Vector Extension, which tackles the shortcomings of vector-length agnostic single-instruction multiple-data extensions by deploying a data streaming paradigm with implicit memory access and loop control. We show that our proposed tool is capable of detecting, representing, and vectorizing a much wider range of loop patterns than existing solutions while providing significant performance gains.