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Abstract

Building future generation supercomputers while constraining their power consumption is one of the biggest challenges faced by the HPC community. For example, US Department of Energy has set a goal of 20 MW for an exascale (1018 flops) supercomputer. To realize this goal, a lot of research is being done to revolutionize hardware design to build power efficient computers and network interconnects. In this work, we propose a software-based online resource management system that leverages hardware facilitated capability to constrain the power consumption of each node in order to optimally allocate power and nodes to a job. Our scheme uses this hardware capability in conjunction with an adaptive runtime system that can dynamically change the resource configuration of a running job allowing our resource manager to re-optimize allocation decisions to running jobs as new jobs arrive, or a running job terminates. We also propose a performance modeling scheme that estimates the essential power characteristics of a job at any scale. The proposed online resource manager uses these performance characteristics for making scheduling and resource allocation decisions that maximize the job throughput of the supercomputer under a given power budget. We demonstrate the benefits of our approach by using a mix of jobs with different power response characteristics. We show that with a power budget of 4:75 MW, we can obtain up to 5:2X improvement in job throughput when compared with the SLURM scheduling policy that is power-unaware. We corroborate our results with real experiments on a relatively small scale cluster, in which we obtain a 1:7X improvement.

Maximizing Throughput of Overprovisioned HPC Data Centers Under a Strict Power Budget

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Abstract

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