Hardware overprovisioned systems have recently been proposed as a viable alternative for a power-efficient design of next-generation supercomputers. A key challenge for such systems is to determine the degree of overprovisioning, which refers to the number of extra nodes that need to be installed under a given power constraint. In this paper, we first show that the degree of overprovisioning depends on dynamic parameters, such as the job mix as well as the global power constraint, and that static decisions can result in limited system throughput. We then study an exhaustive combination of adaptive resource management strategies that span three job scheduling algorithms, four power capping techniques, and three node boot-up mechanisms to understand the trade-off space involved. We then draw conclusions about how these strategies can adaptively control the degree of overprovisioning and analyze their impact on job throughput and power utilization.

Limited power budgets will be one of the biggest challenges for deploying future exascale supercomputers. One of the promising ways to deal with this challenge is hardware over provisioning, that is, installing more hardware resources than can be fully powered under a given power limit coupled with software mechanisms to steer the limited power to where it is needed most. Prior research has demonstrated the viability of this approach, but could only rely on small-scale simulations of the software stack. While such research is useful to understand the boundaries of performance benefits that can be achieved, it does not cover any deployment or operational concerns of using overprovisioning on production systems. This paper is the first to present an extensible power-aware resource management framework for production-sized overprovisioned systems based on the widely established SLURM resource manager. Our framework provides flexible plugin interfaces and APIs for power management that can be easily extended to implement site-specific strategies and for comparison of different power management techniques. We demonstrate our framework on a 965-node HA8000 production system at Kyushu University. Our results indicate that it is indeed possible to safely overprovision hardware in production. We also find that the power consumption of idle nodes, which depends on the degree of overprovisioning, can become a bottleneck. Using real-world data, we then draw conclusions about the impact of the total number of nodes provided in an overprovisioned environment.

Current trends for high-performance systems are leading us towards hardware over-provisioning where it is no longer possible to run each component at peak power without exceeding a system or facility wide power bound. In such scenarios, the power consumed by individual components must be artificially limited to guarantee system operation under a given power bound. In this paper, we present the design of a power scheduler capable of enforcing such a bound using dynamic system-wide power reallocation in an application-agnostic manner. Our scheduler is expected to achieve better job runtimes than a naive power scheduling approach without requiring a priori knowledge of application power behavior.

Current trends for high-performance systems are leading towards hardware overprovisioning where it is no longer possible to run all components at peak power without exceeding a system- or facility-wide power bound. The standard practice of static power scheduling is likely to lead to inefficiencies with over- and under-provisioning of power to components at runtime. In this paper we investigate the performance and scalability of an application agnostic runtime power scheduler (POWsched) that is capable of enforcing a system-wide power limit. Our experimental results show POWsched is robust, has negligible overhead, and can take advantage of opportunities to shift wasted power to more power-intensive applications, improving overall workload runtime by as much as 14% without job scheduler integration or application specific profiling. In addition, we conduct scalability studies to determine POWsched's overhead for large node counts. Lastly, we contribute a model and simulator (POWsim) for investigating dynamic power scheduling behavior and enforcement at scale.