James John Elliott

Combining Partial Redundancy and Checkpointing for HPC

Todays largest High Performance Computing (HPC) systems exceed one Petaflops (1015 floating point operations per second) and exascale systems are projected within seven years. But reliability is becoming one of the major challenges faced by exascale computing. With billion-core parallelism, the mean time to failure is projected to be in the range of minutes or hours instead of days. Failures are becoming the norm rather than the exception during execution of HPC applications. Current fault tolerance techniques in HPC focus on reactive ways to mitigate faults, namely via checkpoint and restart (C/R). Apart from storage overheads, C/R-based fault recovery comes at an additional cost in terms of application performance because normal execution is disrupted when checkpoints are taken. Studies have shown that applications running at a large scale spend more than 50% of their total time saving checkpoints, restarting and redoing lost work. Redundancy is another fault tolerance technique, which employs redundant processes performing the same task. If a process fails, a replica of it can take over its execution. Thus, redundant copies can decrease the overall failure rate. The downside of redundancy is that extra resources are required and there is an additional overhead on communication and synchronization. This work contributes a model and analyzes the benefit of C/R in coordination with redundancy at different degrees to minimize the total wallclock time and resources utilization of HPC applications. We further conduct experiments with an implementation of redundancy within the MPI layer on a cluster. Our experimental results confirm the benefit of dual and triple redundancy - but not for partial redundancy - and show a close fit to the model. At ≈ 80, 000 processes, dual redundancy requires twice the number of processing resources for an application but allows two jobs of 128 hours wallclock time to finish within the time of just one job without redundancy. For narrow ranges of processor counts, partial redundancy results in the lowest time. Once the count exceeds ≈ 770, 000, triple redundancy has the lowest overall cost. Thus, redundancy allows one to trade-off additional resource requirements against wallclock time, which provides a tuning knob for users to adapt to resource availabilities.

Blue Gene/L Log Analysis and Time to Interrupt Estimation

System- and application-level failures could be characterized by analyzing relevant log files. The resulting data might then be used in numerous studies on and future developments for the mission-critical and large scale computational architecture, including fields such as failure prediction, reliability modeling, performance modeling and power awareness. In this paper, system logs covering a six month period of the Blue Gene/L supercomputer were obtained and subsequently analyzed. Temporal filtering was applied to remove duplicated log messages. Optimistic and pessimistic perspectives were exerted on filtered log information to observe failure behavior within the system. Further, various time to repair factors were applied to obtain application time to interrupt, which will be exploited in further resilience modeling research.