DINO: Divergent Node Cloning for Sustained Redundancy in HPC

Abstract

Soft faults like silent data corruption and hard faults like hardware failures may cause a high performance computing (HPC) job of thousands of processes to nearly cease to make progress due to recovery overheads. Redundant computing has been proposed as a solution at extreme scale by allocating two or more processes to perform the same task. However, current redundant computing approaches do not repair failed replicas. Thus, SDC-free execution is not guaranteed after a replica failure and the job may finish with incorrect results. Replicas are logically equivalent, yet may have divergent runtime states during job execution, which complicates on-the-fly repairs for forward recovery. In this work, we present a redundant execution environment that quickly repairs hard failures via Divergent Node cloning (DINO) at the MPI task level. DINO contributes a novel task cloning service integrated into the MPI runtime system that solves the problem of consolidating divergent states among replicas on-the-fly. Experimental results indicate that DINO can recover from failures nearly instantaneously, thus retaining the redundancy level throughout job execution. The cloning overhead, depending on the process image size and its transfer rate, ranges from 5.60 to 90.48 seconds. To the best of our knowledge, the design and implementation for repairing failed replicas in redundant MPI computing is unprecedented.