Titlebook: High Performance Computing; ISC High Performance Heike Jagode,Hartwig Anzt,Piotr Luszczek Conference proceedings 2021 Springer Nature Switz

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Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flowsments. For combustion, the number of reactions can be significant (over 100) and due to the very large CPU requirements of chemical reactions (over 99%) a large number of flow and combustion problems are presently beyond the capabilities of even the largest supercomputers..Motivated by this, novel D

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Turbomachinery Blade Surrogate Modeling Using Deep Learninging. In this paper we present the feasibility of convolutional neural network (CNN) techniques for aerodynamic performance evaluation. CNN approach will enable designer to fully utilize the ability of computers and statistics to interrogate and interpolate the nonlinear relationship between shapes a

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A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision?Treesmodel based on gradient boosted decision trees is presented. The model is trained to learn the boundary layer of a turbulent channel flow so that it can be used to make predictions for significantly different flows where the equilibrium assumptions are valid. The methodology of building the model is

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Nonlinear Mode Decomposition and Reduced-Order Modeling for Three-Dimensional Cylinder Flow by Distrach used to decompose flow fields into physically important flow structures known as modes. In this study, convolutional neural network-based mode decomposition was applied to the three-dimensional flow field. However, because this process is costly in terms of calculation and memory usage for even

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Toward a Workflow for Identifying Jobs with Similar I/O Behavior Utilizing Time Series Analysisg systems that capture the behavior of the executed jobs. While it is easy to utilize statistics to rank jobs based on the utilization of computing, storage, and network, it is tricky to find patterns in 100,000 jobs, i.e., is there a class of jobs that aren’t performing well. Similarly, when suppor

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H3: An Application-Level, Low-Overhead Object Storespecially tailored for use in “converged” Cloud-HPC environments, where HPC applications expect from the underlying storage services to meet strict latency requirements—even for high-level object operations. By embedding the object store in the application, thus avoiding the REST layer, we show that