There is a technology trend to consolidate multiple applications onto a shared hardware platform to reduce the size, weight, power, and cost of real-time systems, such as self-driving vehicles and autonomous robots. Furthermore, modern platforms consist of Central Processing Units (CPUs) and Graphics Processing Units (GPUs) with an increasing number of processing cores that share resources. Moreover, many current applications, such as artificial intelligence applications, have high computation needs and must execute in parallel to satisfy their real-time constraints. These technology trends demand that real-time systems be able to schedule real-time applications upon the shared multiple parallel resources efficiently.
This research will investigate new parallel real-time scheduling frameworks for modern platforms with multiple resources. The scheduling problem is classified into two categories: staged-resources scheduling for alternating usage of different types of resources (e.g., alternatively executing on CPUs and GPUs), and vectorized-resources scheduling for simultaneously using multiple types of resources (e.g., running on processing units that share the last-level cache). The project will establish new parallel real-time task models for the two categories of resource usages. Based on the models, novel real-time schedulers and their corresponding analyses will be developed to achieve the goal of efficient utilization of multiple resources.
The project will advance the understanding of parallel scheduling in real-time systems and serves as the initial steps of the challenge of efficient parallel real-time systems upon powerful and complex modern platforms. This project can have industrial impact on a wide range of today's artificial intelligence-based real-time systems to improve their responsiveness, efficiency, and scalability. The project includes enriching outreach activities and diversity programs to promote Science, Technology, Engineering and Mathematics (STEM) educational activity and broaden participation in computing and engineering.
Research products generated as part of this project will be retained, managed, and disseminated through resources available at the New Jersey Institute of Technology. The products will be preserved with the goal of storing them for at least three years after the completion of the project or the publication of the corresponding articles, whichever is later. The URL to the project repository is https://git.njit.edu/njit-prt.
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.
|Effective start/end date||6/1/20 → 5/31/23|
- National Science Foundation: $174,998.00