Accelerating multi-objective combinatorial optimization via GPUs for cyber-physical systems design

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Cyber-physical systems (CPS) are transforming how we interact with the physical world. They are complex systems that combine machinery, computers, software, data, and human users, such as self-driving cars, airplanes, medical devices, factory automation, and smart renewable energy grids. The design of such systems is increasingly challenging: it involves a multitude of decisions, such as selecting among different sensor and actuator technologies, deciding on different system functions and processing algorithms, deploying software among different computing nodes, and deciding on the communication and power supply topology, all under a multitude of design constraints and objectives such as cost, mass, computing performance, energy consumption, reliability, and safety. Such decision making under constraints and optimization objectives can be formalized as multi-objective combinatorial optimization (MOCO). MOCO enables computer-aided design methods that can dramatically accelerate and improve the efficacy of the overall design process for CPS. However, MOCO is a computationally hard problem and current MOCO solutions have limited scalability.The overarching objective of this project is to accelerate and improve the efficacy of CPS design by improving the scalability of MOCO using Graphic Processor Units (GPU). The research will target a range of constraint-based and metaheuristic MOCO algorithms suitable for design optimization of automotive electric and electronic systems and avionic systems and accelerate them using the GPU platform. The project will improve design of the next generation of intelligent cars and airplanes, open new application areas for GPUs in the context of CPS design, and train ten highly-qualified personnel in algorithm design, software development and CPS engineering---areas that are vital to Canadian knowledge-based economy.