No abstract

Abstract-As the capability and component count of systems increase, the MTBF decreases. Typically, applications tolerate failures with checkpoint/restart to a parallel file system (PFS). While simple, this approach can suffer from contention for PFS resources. Multi-level checkpointing is a promising solution. However, while multi-level checkpointing is successful on today s machines, it is not expected to be sufficient for exascale class machines, which are predicted to have orders of magnitude larger memory sizes and failure rates. Our solution combines the benefits of non-blocking and multi-level checkpointing. In this paper, we present the design of our system and model its performance. Our experiments show that our system can improve efficiency by 1.1 to 2.0× on future machines. Additionally, applications using our checkpointing system can achieve high efficiency even when using a PFS with lower bandwidth.

No abstract

Load balance is critical for performance in large parallel applications. An imbalance on today's fastest supercomputers can force hundreds of thousands of cores to idle, and on future exascale machines this cost will increase by over a factor of a thousand. Improving load balance requires a detailed understanding of the amount of computational load per process and an application's simulated domain, but no existing metrics sufficiently account for both factors. Current load balance mechanisms are often integrated into applications and make implicit assumptions about the load. Some strategies place the burden of providing accurate load information, including the decision on when to balance, on the application. Existing application-independent mechanisms simply measure the application load without any knowledge of application elements, which limits them to identifying imbalance without correcting it.Our novel load model couples abstract application information with scalable measurements to derive accurate and actionable load metrics. Using these metrics, we develop a cost model for correcting load imbalance. Our model enables comparisons of the effectiveness of load balancing algorithms in any specific imbalance scenario. Our model correctly selects the algorithm that achieves the lowest runtime in up to 96% of the cases, and can achieve a 19% gain over selecting a single balancing algorithm for all cases.

No abstract