Quantitative Methods for Continuously Improving RISC V Compilers Philipp Tomsich, VRULL

Quantitative Methods for Continuously Improving RISCV Compilers Philipp Tomsich, VRULL Up until now, most commercial implementations of RISCV have focused on embedded use cases; with the emergence of RISCV as an alternative to the ARM and Intel ecosystems, higherend applications that include storage and the datacenter are moving into focus. This also shifts the perspective on target workloads and representative benchmarks. We show how quantitative methods can be used to assess the quality of code generation and identify and prioritize potential improvements based on hotblock analysis, dynamic instruction count metrics, and instruction histograms. The difference applicability to small benchmarks (such as Coremark, where this method identifies both a superoptimisation opportunity for the CRC functions and highlights the absence of conditionalmove instructions in the RISCV instructions set) and large benchmarks (such as SPEC, which helps to improve and mature the tools across the board) are demonstrate