high performance computing on graphics processing units: hgpu.org

hgpu.org » Applications » Earth and Space Sciences

New High Performance GPGPU Code Transformation Framework Applied to Large Production Weather Prediction Code

Michel Muller, Takayuki Aoki

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Tags: CUDA, Earth and Space Sciences, Fortran, nVidia, OpenACC, Tesla P100, Weather prediction, Weather Prediction Model

March 3, 2018 by hgpu

Deep and Shallow convections in Atmosphere Models on Intel Xeon Phi Coprocessor Systems

Srinivasan Ramesh, Sathish Vadhiyar, Ravi Nanjundiah, PN Vinayachandran

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Tags: Earth and Space Sciences, Intel Xeon Phi, OpenMP

November 5, 2017 by hgpu

Hybrid Fortran: High Productivity GPU Porting Framework Applied to Japanese Weather Prediction Model

Michel Muller, Takayuki Aoki

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Tags: Earth and Space Sciences, Fortran, nVidia, OpenACC, OpenMP, Package, Tesla K20, Tesla P100, Weather prediction, Weather Prediction Model

October 29, 2017 by hgpu

Parallelization and Performance of the NIM Weather Model on CPU, GPU and MIC Processors

Mark Govett, Jim Rosinski, Jacques Middlecoff, Tom Henderson, Jin Lee, Alexander MacDonald, Ning Wang, Paul Madden, Julie Schramm, Antonio Duarte

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Tags: Earth and Space Sciences, Intel Xeon Phi, nVidia, OpenACC, Tesla K80, Weather prediction, Weather Prediction Model

July 19, 2017 by hgpu

Fast GPU-Based Seismogram Simulation from Microseismic Events in Marine Environments Using Heterogeneous Velocity Models

Saptarshi Das, Xi Chen, Michael P. Hobson

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Tags: CUDA, Differential equations, Earth and Space Sciences, Geophysics, Heterogeneous systems, nVidia, Partial differential equations, PDEs, Seismology, Spectral methods, Tesla C2075, Tesla K20, Visualization, Wave equation

May 18, 2017 by hgpu

GPU accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model

Michail Alvanos, Theodoros Christoudias

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Tags: Chemistry, Code generation, CUDA, Earth and Space Sciences, Fortran, nVidia, Package, Tesla M2070

May 2, 2017 by hgpu

FPGA-based Tsunami Simulation: Performance Comparison with GPUs, and Roofline Model for Scalability Analysis

Kohei Nagasu, Kentaro Sano, Fumiya Kono, Naohito Nakasato

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Tags: AMD Radeon R9 280X, ATI, Earth and Space Sciences, FPGA, nVidia, OpenCL, Performance, Tesla K20

April 3, 2017 by hgpu

Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

Monica Denham, Karina Laneri, Juan Manuel Morales

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Tags: Biology, CUDA, Earth and Space Sciences, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX Titan, Package, Tesla C2070, Tesla K40

January 16, 2017 by hgpu

Parallelization and Performance of the NIM Weather Model for CPU, GPU and MIC Processors

Mark Govett, Jim Rosinski, Jacques Middlecoff, Tom Henderson, Jin Lee, Alexander MacDonald, Paul Madden, Julie Schramm, Antonio Duarte

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Tags: CUDA, Earth and Space Sciences, Fortran, Intel Xeon Phi, nVidia, OpenACC, Tesla K80, Weather prediction, Weather Prediction Model

December 6, 2016 by hgpu

Performance Portability of the Aeras Atmosphere Model to Next Generation Architectures using Kokkos

Jerry Watkins, Irina Tezaur

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Tags: CUDA, Earth and Space Sciences, MPI, nVidia, OpenMP, performance portability, Physics, Tesla K20, Tesla K80

November 8, 2016 by hgpu

A Data Parallel Algorithm for Seismic Raytracing

Allen D. Malony, Stephanie McCumsey, Joseph Byrnes, Craig Rasmusen, Soren Rasmusen, Erik Keever, Doug Toomey

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Tags: Algorithms, Earth and Space Sciences, Geoscience, nVidia, OpenCL, OpenMP, Raytracing, Seismology, Tesla K80, Tesla M2070

July 26, 2016 by hgpu

THOR: A New and Flexible Global Circulation Model to Explore Planetary Atmospheres

Joao M. Mendonca, Simon L. Grimm, Luc Grosheintz, Kevin Heng

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Tags: Astrophysics, CUDA, Earth and Planetary Astrophysics, Earth and Space Sciences, nVidia, Tesla K20

July 20, 2016 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Applications

New High Performance GPGPU Code Transformation Framework Applied to Large Production Weather Prediction Code

Deep and Shallow convections in Atmosphere Models on Intel Xeon Phi Coprocessor Systems

Hybrid Fortran: High Productivity GPU Porting Framework Applied to Japanese Weather Prediction Model

Parallelization and Performance of the NIM Weather Model on CPU, GPU and MIC Processors

Fast GPU-Based Seismogram Simulation from Microseismic Events in Marine Environments Using Heterogeneous Velocity Models

GPU accelerated atmospheric chemical kinetics in the ECHAM/MESSy (EMAC) Earth system model

FPGA-based Tsunami Simulation: Performance Comparison with GPUs, and Roofline Model for Scalability Analysis

Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

Parallelization and Performance of the NIM Weather Model for CPU, GPU and MIC Processors

Performance Portability of the Aeras Atmosphere Model to Next Generation Architectures using Kokkos

A Data Parallel Algorithm for Seismic Raytracing

THOR: A New and Flexible Global Circulation Model to Explore Planetary Atmospheres

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