Performance Left on the Table: An Evaluation of Compiler Autovectorization for RISC-V
Neil Adit, Adrian Sampson
Abstract
Next-generation length-agnostic vector instruction set architecture (ISA) designs, the RISC-V vector extension, and ARM’s scalable vector extension enable software portability across hardware implementations with different vector engines. While traditional, fixed-length single-instruction–multiple-data ISA instructions, such as Intel AVX and ARM Neon, enjoy mature compiler support for automatic vectorization, compiler support is still emerging for these length-agnostic ISAs. This work studies the compiler shortcomings that constitute the gap in autovectorization capabilities between length-agnostic and fixed-length architectures. We examine LLVM’s support for both the RISC-V vector extension and traditional vector ISAs. We study a set of synthetic scalar loops to compare the breadth of support in the two settings, and we examine a real benchmark suite to compare autovectorized to hand-vectorized RISC-V code. We use both studies to distill a set of recommendations for engineering improvements and future research in compilers and programming models for length-agnostic vector programming.