Resource-Elasticity Support for Distributed Memory HPC Applications

Computer simulations are alternatives to the scientific method in domains where physical experiments are unfeasible or impossible. When the amount of memory and processing speed required is large, simulations are executed in distributed memory High Performance Computing (HPC) systems. These systems are usually shared among its users. A resource manager with a batch scheduler is used to fairly and efficiently share the resources of these systems among its users. Current large HPC systems have thousands of compute nodes connected over a high-performance network. Users submit batch job descriptions where the number of resources required by their simulations are specified. Batch job descriptions are queued and scheduled based on priorities and submission times. The parallel efficiency of a simulation depends on the number of resources allocated to it. It is challenging for users to specify allocation sizes that produce adequate parallel efficiencies. A resource allocation can be too small and the parallel efficiency of the application may be adequate, but its performance may not be scaled to its maximum potential. A resource allocation can be too large and therefore the parallel efficiency of the application may be degraded due to synchronization overheads. Unfortunately, in current systems these resource allocations cannot be adapted once the applications of a job start. A resource manager and MPI library combination that adds resource-elasticity support for HPC applications is proposed in this work. The resource manager is extended with operations to adapt the resources of running applications in jobs; in addition, new scheduling techniques are added to it. The MPI library has been extended with operations that enable resource adaptations as changes in the number of processes in world communicators. The goal is to optimize system-wide efficiency metrics through adjustments to the resource allocations of running applications. Resource allocations are adjusted continuously based on performance feedback from running applications.