Real-Time Systems with Radiation-Hardened Processors: A GPU-based Framework to Explore Tradeoffs

Radiation-hardened processors are designed to be resilient against soft errors but such processors are slower than Commercial OffThe-Shelf (COTS) processors as well significantly costlier. In order to mitigate the high costs, software techniques such as task re-executions must be deployed together with adequately hardened processors to provide reliability. This leads to a huge design space comprising of the hardening level of the processors and the number of re-executions of each task in the system. Each configuration in this design space represents a tradeoff between processor load, reliability and costs. The reliability comes at the price of higher costs due to higher levels of hardening and performance degradation due to hardening or due to re-executions. Thus, the tradeoffs between performance, reliability and costs must be carefully studied. Pertinent questions that arise in such a design scenario are — (i) how many times a task must be re-executed and (ii) what should be hardening level? — such that the system reliability is satisfied. In order to evaluate such tradeoffs efficiently, in this thesis, we propose novel framework that harnesses the computational power of Graphics Processing Units (GPUs). Our framework is based on a system failure probability analysis that connects the probability of failure of tasks to the overall system reliability. Based on characteristics of this probabilistic analysis as well as real-time deadlines, we derive bounds on the design space to prune infeasible solutions. Finally, we illustrate the benefits of our proposed framework with several experiments.