Abstract-The throughput of supercomputers depends not only on efficient job scheduling but also on the type of jobs that form the workload. Malleable jobs are most favorable for a cluster as they can dynamically adapt to a changing allocation of resources. The batch system can expand or shrink a running malleable job to improve system utilization, throughput, and response times. In the past, however, the rigid nature of commonly used programming models like MPI made writing malleable applications a daunting task, which is why it remained largely unrealized. This is now changing. To improve fault tolerance, load imbalance, and energy efficiency in emerging exascale systems, more adaptive programming paradigms such as Charm++ enter the scene. Although they offer better support for malleability, current batch systems still lack management facilities for malleable jobs and are therefore incapable of leveraging their potential. In this paper, we present an extension of the Torque/Maui batch system for malleability. We propose a novel malleable job scheduling strategy and show the first batch system capable of efficiently managing rigid, malleable, and evolving jobs together. We demonstrate that our strategy achieves consistently superior performance in comparison to every other state-of-the-art malleable job scheduling strategy under varying dynamics of the workload.

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Identifying scalability bottlenecks in parallel applications is a vital but also laborious and expensive task. Empirical performance models have proven to be helpful to find such limitations, though they require a set of experiments in order to gain valuable insights. Therefore, the experiment design determines the quality and cost of the models. Extra-P is an empirical modeling tool that uses small-scale experiments to assess the scalability of applications. Its current version requires an exponential number of experiments per model parameter. This makes the creation of empirical performance models very expensive, and in some situations even impractical. In this paper, we propose a novel parameter-value selection heuristic, which functions as a guideline for the experiment design, leveraging sparse performance-modeling, a technique that only needs a polynomial number of experiments per model parameter. Using synthetic analysis and data from three different case studies, we show that our solution reduces the average modeling costs by about 85% while retaining 92% of the model accuracy.

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