The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to more ยป perform scalable data analysis and simulation via an intuitive, cross-platform client user interface. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now with the rise of multimodal acquisition systems and the associated processing capability the era of multidimensional, informationally dense data sets has arrived. Our study reveals interesting insights about how platform configuration affects the performance and energy efficiency of HPC workflows under power = , We also validate our model and present model-driven studies for a wide range of real-system scenarios. In order to resolve these issues in power-constrained HPC, in this paper, we propose a reliability-aware model to determine the aforementioned platform configurations for HPC workflows. In addition, given a power limit, it is unclear what the optimal scales and power capping levels are for various workflows, especially when taking reliability into account. However, it remains unclear how to choose the appropriate power limits for various HPC workflows and how to distribute the power limit of a workflow between simulation and analysis. Enforcing power limits is emerging as a practical trend for power-constrained HPC facilities. As computing capability keeps growing, power consumption is becoming critical to HPC facilities. Approaching the era of exascale, online analysis is gaining popularity due to the savings of I/O to persistent storage. In high-performance computing (HPC) workflows, data analytics is typically utilized to gain insights from scientific simulations.
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