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Data Analysis Approach for Large Data Volumes in a Connected Community CSMD CSED ORNL

Data Analysis Approach for Large Data Volumes in a Connected Community

June 1, 2021

Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs CSMD ORNL

Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs

June 1, 2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures CSMD ORNL Computer Science and Mathematics

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

June 1, 2021

MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction CSMD Computer Science and Mathematics Division ORNL

MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction

June 1, 2021

Fast and Scalable Sparse Triangular Solver for Multi-GPU Based HPC Architectures CSMD Computer Science and Mathematics Division ORNL

Fast and Scalable Sparse Triangular Solver for Multi-GPU Based HPC Architectures

June 1, 2021

TSM2X: High-performance tall-and-skinny matrix–matrix multiplication on GPUs, Journal of Parallel and Distributed Computing Computer Science and Mathematics Division CSMD ORNL

TSM2X: High-performance tall-and-skinny matrix–matrix multiplication on GPUs, Journal of Parallel and Distributed Computing

June 1, 2021

Machine Learning-Based Autotuning for Parallel Particle Advection Computer Science and Mathematics Division CSMD ORNL

Machine Learning-Based Autotuning for Parallel Particle Advection

June 1, 2021

Execution times for the HyLiPoD algorithm for different seeding distributions (color) and levels of concurrency (glyph size) Computer Science and Mathematics Division CSMD ORNL

HyLiPoD: Parallel Particle Advection Via a Hybrid of Lifeline Scheduling and Parallelization-Over-Data

June 1, 2021

Fides: A General-Purpose Data Model Library for Streaming Data Computer Science and Mathematics Division CSMD ORNL

Fides: A General-Purpose Data Model Library for Streaming Data

June 1, 2021

model of neuromorphic computing for real-world autonomous racing

ORNL researchers demonstrate neuromorphic computing capabilities via scaled autonomous racing.

June 1, 2021

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