Titlebook: High Performance Computing; 34th International C Michèle Weiland,Guido Juckeland,Ponnuswamy Sadayap Conference proceedings 2019 Springer Na

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Learning Neural Representations for Predicting GPU Performancetypes of GPUs available, finding the best hardware accelerator for each application is a challenge. For that matter, it is time consuming and tedious to execute every application on every GPU system to learn the correlation between application properties and hardware characteristics. To address this

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Petaflop Seismic Simulations in the Public Cloudrt for virtualization closed performance gaps, compared to on-premises, bare-metal systems. This development is driven by offloaded hypervisors and full CPU virtualization. Today’s cloud service providers, such as Amazon or Google, offer the ability to assemble application-tailored clusters to maxim

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MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cyclesrder to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-state operating points and road and/or track performance data. As the parameter space of control variables, design variable constraint

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PerfMemPlus: A Tool for Automatic Discovery of Memory Performance Problemstools play an important role in performance optimization. Today’s processors offer feature-rich performance monitoring units with support for instruction sampling. But existing tools only partially use this data. Previously, performance counters were used to measure the memory bandwidth. But the att