These job-value functions are used by the value-based scheduling algorithms to maximize the system productivity where system productivity is the accumulation of job-value for the completed jobs. To measure the productivity of an HPC job, researchers have proposed to assign a monotonically decreasing time-dependent value function, called job-value, to that job. High performance computing (HPC) systems are confronting the challenge of improving their productivity under a system-wide power constraint in the exascale era. In this work we address the underlying HPC needs for characterization in the material science community, elaborate how BEAM's design and infrastructure tackle those needs, and present a small sub-set of user cases where scientists utilized BEAM across a broad range of analytical techniques and analysis modes. This framework delivers authenticated, “push-button” execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing the converged compute-and-data infrastructure at Oak Ridge National Laboratory's (ORNL) Compute and Data Environment for Science (CADES) and HPC environments like Titan at the Oak Ridge Leadership Computing Facility (OLCF). 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 perform scalable more » 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. In this work we address the underlying HPC needs for characterization in the material science community, elaborate how BEAM s design and infrastructure tackle those needs, and present a small sub-set of user cases where scientists utilized BEAM across a broad range of analytical techniques and analysis modes. This framework delivers authenticated, push-button execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing the converged compute-and-data infrastructure at Oak Ridge National Laboratory s (ORNL) Compute and Data Environment for Science (CADES) and HPC environments like Titan at the Oak Ridge Leadership Computing Facility (OLCF). 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 » perform scalable data analysis and simulation via an intuitive, cross-platform client user interface. 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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