🤖 AI Summary
This study addresses the software portability gap between model-driven development for cyber-physical systems and multicore execution platforms. To bridge this divide, we propose a workflow-preserving automated translation framework that converts Simulink models into OpenCL code. Specifically, the framework leverages GPU Coder to extract CUDA implementations and employs a custom pipeline to translate them into OpenCL host and device code, enabling cross-platform adaptation of syntax, APIs, and parameter packing. This approach transforms data-parallel models into multicore executables without requiring manual rewriting. As a key contribution, we validate the feasibility and effectiveness of the proposed retargeting pipeline by successfully deploying a Frenet trajectory planner on the Kalray MPPA multicore platform, demonstrating its practical applicability for high-performance embedded systems.
📝 Abstract
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.