High-performance computing (HPC) relies increasingly on heterogeneous hardware and especially on the combination of central and graphical processing units. The task-based method has demonstrated promising potential for parallelizing applications on such computing nodes. With this approach, the scheduling strategy becomes a critical layer that describes where and when the ready-tasks should be executed among the processing units. In this study, we describe a heuristic-based approach that assigns priorities to each task type. We rely on a fitness score for each task/worker combination for generating priorities and use these for configuring the Heteroprio scheduler automatically within the StarPU runtime system. We evaluate our method’s theoretical performance on emulated executions and its real-case performance on multiple different HPC applications. We show that our approach is usually equivalent or faster than expert-defined priorities.
This paper presents a new solution to address the challenge of increasing memory usage in high-performance computing simulations of Lattice-Bolzmann or Finite-Volume schemes. Our approach utilises a lossy compression scheme based on the Discrete Wavelet Transform (DWT) to achieve high compression ratios while preserving the accuracy of the simulation. Our evaluation on two different FV/LBM schemes demonstrates that the approach can reduce memory usage by several orders of magnitude.Résumé. Ce papier présente une nouvelle solution pour faire face à l'augmentation de l'utilisation de la mémoire dans les simulations haute performance basées sur les méthodes Lattice-Bolzmann ou Volumes Finis. Notre approche utilise un schéma de compression avec perte basé sur la transformée par ondelettes discrète (DWT) pour obtenir des taux de compression élevés tout en préservant la précision de la simulation. Notre évaluation sur deux différents schémas VF/LBM démontre que l'approche peut réduire l'utilisation de la mémoire de plusieurs ordres de grandeur.
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