发表机构
Graduate School of Science and Engineering, Saitama University; eSOL Company Ltd.（埼玉大学理工学研究科; eSOL有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种从Simulink到OpenCL的工作流保持重定向方法，利用GPU Coder提取CUDA代码并转换，在Kalray MPPA Coolidge2上验证了可行性。
AI 中文摘要
本文解决了基于模型的开发（MBD）与面向信息物理系统（CPS）的先进众核执行之间的软件可移植性差距。我们提出了一种面向基于Simulink的CPS应用、采用候选级数据并行的工作流保持重定向方法，将其映射到基于OpenCL的众核处理器。该工具链并非为新平台手动重写模型，而是利用MathWorks GPU Coder提取数据并行的CUDA代码，随后通过自定义框架将其转换为OpenCL主机和设备代码。转换过程涵盖语法重写、API仿真以及平台特定的参数打包。我们将此工作流部署于Kalray MPPA Coolidge2上计算密集型的Frenet框架轨迹规划器。结果表明，针对所评估的CPS工作负载和平台，工作流保持的重定向流水线具有可行性。
英文摘要
This paper addresses the software portability gap between Model-Based Development (MBD) and advanced many-core execution for Cyber-Physical Systems (CPS). We present a workflow-preserving retargeting approach for Simulink-based CPS applications with candidate-wise data parallelism to OpenCL-based many-core processors. Rather than manually rewriting models for new platforms, our toolchain uses MathWorks GPU Coder to extract data-parallel CUDA code, which is then translated into OpenCL host and device code via a custom framework. The conversion handles syntax rewriting, API emulation, and platform-specific argument packing. We deployed this workflow for a computationally intensive Frenet-frame trajectory planner on the Kalray MPPA Coolidge2. The results demonstrate the feasibility of a workflow-preserving retargeting pipeline for the evaluated CPS workload and platform.
Comments16 pages, 7 figures, and 3 tables. This is the preprint submitted to SEAA 2026. A revised short-paper version was published in LNCS 16863
Journal refSoftware Engineering and Advanced Applications (SEAA 2026), LNCS 16863, Springer, 2027, pp. 76-85
DOI：10.1007/978-3-032-36590-3_6
