CSR: Small: Collaborative Research: Real-Time Computing Infrastructure for Integrated CPU-GPU SoC Platforms

University Of Kansas Center For Research Inc, Lawrence KS

Investigators

Abstract

Autonomous cars and drones demand high computational performance to process massive amount of real-time data while also keeping their size, weight, power and cost to an acceptable level. Graphics processing unit (GPU) is specially designed hardware to efficiently process such large data. Therefore, it is increasingly being integrated in new generations of computer chips. Unfortunately, such integrated chips often exhibit unpredictable timing behaviors due to unregulated use of shared hardware resources that can prevent timely execution of critical tasks. This project will create a new real-time computing infrastructure for GPU integrated computer chips to provide predictable timing and high-performance. The project will create new resource management algorithms, task models, real-time synchronization protocols, and schedulability analysis methodologies for GPU integrated computing platforms that significantly improve time predictability and efficiency, and reduce analysis pessimism, compared to the state-of-the art. The project has three research objectives. The first objective is to develop software mechanisms to bound worst-case memory interference to controllable limits with minimal programmer intervention. The second objective is to maximize system resource utilization without sacrificing timing predictability of critical real-time tasks. The third objective is to develop modeling and analysis methodologies for the proposed computing infrastructure. The project has several direct economic and societal impacts. This research will greatly improve temporal predictability and efficiency of GPU integrated computing platforms, which are used for safety-critical cyber-physical systems---particularly in automotive and aviation industries. Considering the market size of automotive industry and the high certification cost in aviation industry, the expected improvements of the project can potentially translate to multi-billion-dollar savings. The research outcomes will be disseminated via public code repositories and integrated into graduate and undergraduate courses. In particular, autonomous car and drone testbeds will be used to increase student engagement in the classes. Research artifacts, such as source code of modified Linux kernel, user-level library, and tools will be publicly available via open-source repositories at https://github.com/CSL-KU/igpu-rm for the duration of the project and beyond. Research findings will be reported via scientific journals and conferences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.