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Semi-supervised learning for shale image segmentation with fast normalized cut loss
In analyzing the geological processes, the segmentation of scanning electron microscopy (SEM) images of geological samples is crucial …
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Accelerating High-order Stencils on GPUs
Finite-difference methods based on high-order stencils are commonly used for modeling of seismic wave propagation, weather forecasting, …
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An Automated Tool for Analysis and Tuning of GPU-Accelerated Code in HPC Applications
The US Department of Energy’s fastest supercomputers and forthcoming exascale systems employ Graphics Processing Units (GPUs) to …
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Paw-Net: Stacking Ensemble Deep Learning for Segmenting Scanning Electron Microscopy Images of Fine-grained Shale Samples
Segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological analyses, as it needs to …
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Measurement and Analysis of GPU-accelerated Applications with HPCToolkit
To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been …
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Quadboost: A Scalable Concurrent Quadtree
Building concurrent spatial trees is more complicated than binary search trees since a space hierarchy should be preserved during …
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