Efficient Support of the Scan Vector Model for RISC-V Vector Extension

Abstract

RISC-V vector extension (RVV) provides wide vector registers, which is applicable for workloads with high data-level parallelism such as machine learning or cloud computing. However, it is not easy for developers to fully utilize the underlying performance of a new architecture. Hence, abstractions such as primitives or software frameworks could be employed to ease this burden. Scan, also known as all-prefix-sum, is a common building block for many parallel algorithms. Blelloch presented an algorithmic model called the scan vector model, which uses scan operations as primitives, and demonstrates that a broad range of applications and algorithms can be implemented by them. In our work, we present an efficient support of the scan vector model for RVV. With this support, parallel algorithms can be developed upon those primitives without knowing the details of RVV while gaining the performance that RVV provides. In addition, we provide an optimization scheme related to the length multiplier feature of RVV, which can further improve the utilization of the vector register files. The experiment shows that our support of scan and segmented scan for RVV can achieve 2.85x and 4.29x speedup, respectively, compared to the sequential implementation. With further optimization using the length multiplier of RVV, we can improve the previous result to 21.93x and 15.09x speedup.