Mixed-Criticality Real-Time Computing with GPUs
Abstract
This project is directed at developing a framework for supporting real-time mixed-criticality workloads on multicore platforms augmented with graphics processing units (GPUs) as hardware accelerators. The particular workloads of interest are drawn from emerging safety-critical embedded systems where autonomous functionality is required. Examples of such systems that are of relevance to the U.S. military include unmanned airplanes and helicopters, battlefield robots, unmanned ground vehicles, and various autonomous weapons systems. While many of these systems require a remote human operator todayÑand thus should more properly be called semiautonomousÑ next-generation systems are envisioned that will be truly autonomous. In many settings, fielding truly autonomous systems will require significant computing power that must be provided within strict size, weight, and power (SWaP) limits. Multicore+GPU platforms are capable of meeting these requirements, as long as a judicious resource-allocation framework is employed that can ensure that critical timing constraints are met without under-utilizing hardware resources. The development of such a framework is the main objective of this project. The proposed framework will leverage the fact that, even in safety critical systems, not all system components are highly critical. Thus, less-pessimistic provisioning assumptions can be applied to lower-criticality components to ensure efficient platform utilization while still guaranteeing the timing requirements of all components. In validating timing constraints, analysis assumptions can be applied to each component that are appropriate for the criticality level to which it is assigned. The key outcomes of this project will be (i) new criticality-aware resource-allocation methods and associated analysis techniques for checking timing constraints on multicore+GPU platforms, and (ii) prototype implementations of these methods that will be made available to the research community as open-source software. The analysis to be obtained will extend recent work on mixed-criticality schedulability analysis to be applicable on multicore platforms augmented with GPUs. The prototype systems to be developed will extend a state-of-the-art real-time GPU management framework developed by the PIÕs group called GPUSync, which is implemented in LITMUSRT, a real-time Linux extension developed by the PIÕs group under prior ARO support. Two new GPUSync-related prototypes will be developed, one implemented in LITMUSRT that directly extends the existing GPUSync framework, and a second implemented mostly in user space that is less fully featured but requires only minimal operating system (OS) modifications. The latter prototype is motivated by the fact that certified OSs must be used in many satefy-critical domains. In practice, user space GPU management might be preferable to avoid costs associated with certifying extensive OS modifications. Print Form
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 06, 2018
- Source ID
- W911NF1710294
Entities
People
- James C Anderson
Organizations
- Army Contracting Command
- United States Army
- University of North Carolina at Chapel Hill