Microarchitectural Co-Optimization for Sustained Throughput of RISC-V Multi-Lane Chaining Vector Processors

TL;DR

This paper analyzes and optimizes microarchitectural inefficiencies in RISC-V vector processors, achieving significant throughput improvements without hardware changes by targeted microarchitectural enhancements.

Contribution

It introduces a microarchitectural optimization framework for RISC-V vector processors that significantly improves sustained throughput by addressing key bottlenecks.

Findings

Achieved a 1.33x speedup over baseline Ara processor.

Closed 12.2% of the performance gap to the theoretical bound.

Speedups of approximately 2.41x, 1.60x, 1.52x, and 1.42x on key kernels.

Abstract

Modern RISC vector processors rely on the synergy of multi-lane parallelism and chaining to achieve high sustained throughput, yet their achieved performance often falls substantially short of the theoretical performance bound due to microarchitectural inefficiencies. In this work, we take the open-source RVV processor Ara as the target platform and analyze the sources of its sustained-throughput loss and optimize the design accordingly. We first establish an ideal multi-lane chaining execution model as a microarchitectural reference for the ideal steady-state progression of the vector backend. Based on this model, we attribute Ara's key bottlenecks to inefficiencies along three critical execution paths: memory-side inefficiencies in data supply and transaction issuance, control-side inefficiencies caused by conservative dependence management and issue control, and operand-delivery inefficiencies caused by access conflicts and result-propagation overhead. To address these bottlenecks, we propose a coordinated set of microarchitectural optimizations. Experimental results show that, without increasing raw memory bandwidth or changing the main processor configuration, Ara-Opt achieves a geometric-mean speedup of 1.33x over baseline Ara. Under roofline-based normalization, the geometric-mean gap-closed ratio reaches 12.2%. In particular, scal, axpy, ger, and gemm achieve speedups of approximately 2.41x, 1.60x, 1.52x, and 1.42x, with corresponding gap-closed ratios of 93.7%, 88.9%, 78.3%, and 59.3%, respectively. These results show that the proposed method can effectively recover sustained-throughput capability lost to microarchitectural inefficiencies in Ara under essentially unchanged hardware resource constraints, and move the implementation points of regular streaming and high-throughput workloads significantly closer to the theoretical performance bound.