Current checkpointing techniques employed to overcome faults for HPC applications result in inferior application performance after restart from a checkpoint for a number of applications. This is due to a lack of page and core affinity awareness of the checkpoint/restart (C/R) mechanism, i.e., application tasks originally pinned to cores may be restarted on different cores, and in case of non-uniform memory architectures (NUMA), quite common today, memory pages associated with tasks on a NUMA node may be associated with a different NUMA node after restart. This work contributes a novel design technique for C/R mechanisms to preserve task-to-core maps and NUMA node specific page affinities across restarts. Experimental results with BLCR, a C/R mechanism, enhanced with affinity awareness demonstrate significant performance benefits of 37%-73% for the NAS Parallel Benchmark codes and 6-12% for NAMD with negligible overheads instead of up to nearly four times longer an execution times without affinity-aware restarts on 16 cores.

Adaptive Resource-Centric Computing (ARCC) enables a simultaneous mix of high-throughput parallel, real-time, and interactive applications through automatic discovery of the correct mix of resource assignments necessary to achieve application requirements. This approach, embodied in the Tessellation manycore operating system, distributes resources to QoS domains called cells. Tessellation separates global decisions about the allocation of resources to cells from application-specific scheduling of resources within cells. We examine the implementation of ARCC in the Tessellation OS, highlight Tessellation's ability to provide predictable performance, and investigate the performance of Tessellation services within cells.

Live migration is a widely used technique for resource consolidation and fault tolerance. KVM and Xen use iterative pre-copy approaches which work well in practice for commercial applications. In this paper, we study pre-copy live migration of MPI and OpenMP scientific applications running on KVM and present a detailed performance analysis of the migration process. We show that due to a high rate of memory changes, the current KVM rate control and target downtime heuristics do not cope well with HPC applications: statically choosing rate limits and downtimes is infeasible and current mechanisms sometimes provide suboptimal performance. We present a novel on-line algorithm able to provide minimal downtime and minimal impact on end-to-end application performance. At the core of this algorithm is controlling migration based on the application memory rate of change.