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Found 6 papers, 1 papers with code
Predicting batch queue job wait times for informed scheduling of urgent HPC workloads
We demonstrate that our techniques deliver the most accurate predictions across our machines of interest, with the result of this work being the ability to predict job start times within one minute of the actual start time for around 65\% of jobs on ARCHER2 and 4-cabinet, and 76\% of jobs on Cirrus.
Compact Native Code Generation for Dynamic Languages on Micro-core Architectures
Micro-core architectures combine many simple, low memory, low power-consuming CPU cores onto a single chip.
Code Generation
Programming Languages
Distributed, Parallel, and Cluster Computing
Using machine learning to reduce ensembles of geological models for oil and gas exploration
Exploration using borehole drilling is a key activity in determining the most appropriate locations for the petroleum industry to develop oil fields.
Machine Learning for Gas and Oil Exploration
Drilling boreholes for gas and oil extraction is an expensive process and profitability strongly depends on characteristics of the subsurface.
Machine learning on Crays to optimise petrophysical workflows in oil and gas exploration
With a general aim of decreasing the petrophysical interpretation time down from over 7 days to 7 minutes, in this paper we describe the use of mathematical models that have been trained using raw well log data, for completing each of the four stages of a petrophysical interpretation workflow, along with initial data cleaning.
Supercomputing with MPI meets the Common Workflow Language standards: an experience report
Use of standards-based workflows is still somewhat unusual by high-performance computing users.
Distributed, Parallel, and Cluster Computing
