Enabling Resilience in Asynchronous Many-Task Programming Models.

Resilience is an imminent issue for next-generation platforms due to projected increases in soft/transient failures as part of the inherent trade-offs among performance, energy, and costs in system design. In this paper, we introduce a comprehensive approach to enabling application-level resilience in Asynchronous Many-Task (AMT) programming models with a focus on remedying Silent Data Corruption (SDC) that can often go undetected by the hardware and OS. Our approach makes it possible for the application programmer to declaratively express resilience attributes with minimal code changes, and to delegate the complexity of efficiently supporting resilience to our runtime system. We have created a prototype implementation of our approach as an extension to the Habanero C/C++ library (HClib), where different resilience techniques including task replay, task replication, algorithm-based fault tolerance (ABFT), and checkpointing are available. Our experimental results show that task replay incurs lower overhead than task replication when an appropriate error checking function is provided. Further, task replay matches the low overhead of ABFT. Our results also demonstrate the ability to combine different resilience schemes. To evaluate the effectiveness of our resilience mechanisms in the presence of errors, we injected synthetic errors at different error rates (1.0%, and 10.0%) and found modest increase in execution times. In summary, the results show that our approach supports efficient and scalable recovery, and that our approach can be used to influence the design of future AMT programming models and runtime systems that aim to integrate first-class support for user-level resilience.