An evaluation of lazy fault detection based on Adaptive Redundant Multithreading

The challenge of resilience for High Performance Computing applications is significant for future extreme scale systems. These systems will experience unprecedented rates of faults and errors as they will be constructed from massive numbers of components that are inherently less reliable than those available today. While the use of redundant computing can provide detection and possible correction of errors, its system-wide use in future extreme-scale HPC systems will incur considerable overheads to application performance. In this paper, we present a framework that provides application level fault detection based on redundant multithreading. In previous work, we demonstrated an adaptive approach based on a language level directive. The computation contained in the programmer directive is executed by duplicate threads. In concert with a runtime system, the redundant multithreading is enabled opportunistically to provide fault detection at more reasonable overheads to application performance. The lazy fault detection approach presented in this work seeks to further optimize the use of redundancy by prioritizing the application's primary computation over the fault detection. Our approach relaxes the requirement that the redundant threads synchronize and compare results immediately. We show that lazy error detection is feasible and yields lower time to solution over adaptive RMT for a range of scientific computational kernels. We also explore a thread-to-core assignment strategy that seeks to reduce the interference between the redundant threads.