Task Mapping and Scheduling on RISC-V MIMD Processor With Vector Accelerator Using Model-Based Parallelization

Abstract

In this paper, we propose a model-based workflow to generate parallel code on a multiple instruction stream, multiple data stream (MIMD) processor with vector accelerator (MIMDV) from a Simulink model. Solving data- and task-parallelism is crucial during this process. For data parallelism, a RISC-V Simulink library written in vector codes is prepared for blocks with sufficient vector or matrix calculations. Moreover, large inputs can be divided, which means that tasks can be executed simultaneously using multiple cores. For task parallelism, integer linear programming (ILP) is designed to deploy tasks on scalar processing elements (SPEs) and a vector processing element (VPE) of MIMDV. The use of a vector library for a task and the number of SPEs a task uses are determined. To reduce the overhead, synchronization is realized by barrier wait, and execution is divided into multiple time intervals called layer. We propose a novel one-step ILP that accurately minimizes the parallel time of such a situation. Furthermore, we propose a two-step ILP to achieve reasonable performance in practical time. One step is SPE mapping, and the other is layer scheduling. We tested our methods using random task graphs and real-world applications on DR1000C, a type of RISC-V MIMD processor with a vector accelerator.

Keywords

• Task mapping and scheduling
• model-based development
• multi-core processor
• RISC-V
• MIMD
• vectorization