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Large scale parallel machines are subject to system-wide power budgets (or caps). As these machines grow in capacity, they can concurrently execute dependent applications that were previously processed serially. Such application coupling saves IO and time as the applications now communicate at runtime instead of through disk. Such coupled applications are predicted to be a major workload for future exascale supercomputers; e.g., scientific simulations will execute concurrently with in situ analysis. While support for system-wide power caps has been widely studied, prior work does not consider the impact on coupled applications. We study techniques for maximizing coupled application performance under a system-wide power cap and implement them on a 26-node cluster. We compare to SLURM, a state-of-the-art job scheduler that considers power, but not coupling. The proposed techniques increase mean performance over SLURM by 7-14%. Unlike existing approaches, the proposed techniques also recognize when it is not possible to increase performance and, instead, reduce energy, achieving 18% energy reduction for a 5% performance loss. Finally, the dynamic techniques are resilient to tail behavior and system noise, improving performance in noisy environments by 30-36%.

Performance & Energy Tradeoffs for Dependent Distributed Applications Under System-wide Power Caps