high performance computing on graphics processing units: hgpu.org

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Trapping of giant-planet cores – I. vortex aided trapping at the outer dead zone edge

Zs. Regaly, Zs. Sandor, P. Csomos, S. Ataiee

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Tags: Astrophysics, CUDA, Numerical simulation, nVidia, Package, Solar and Stellar Astrophysics, Tesla C2050

May 27, 2013 by hgpu

Point Spread Function Estimation of Solar Surface Images with a Cooperative Particle Swarm Optimization on GPUs

Peter Frank Perroni

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Tags: Algorithms, Astrophysics, Image processing, nVidia, OpenCL, Package, Particle swarm optimization, Tesla C2050, Thesis

May 16, 2013 by hgpu

Gauge fixing in lattice QCD with multi-GPUs

Mario Schrock, Hannes Vogt

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Tags: Algorithms, CUDA, High Energy Physics - Lattice, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia Quadro FX 4000, Package, Physics, QCD, Tesla C2070

May 16, 2013 by hgpu

Simulating the universe with GPU-accelerated supercomputers: n-body methods, tests, and examples

Benjamin D. Wibking

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Tags: Astrophysics, Cosmology, CUDA, Gravitation, N-body simulation, nVidia, Package, Thesis

May 7, 2013 by hgpu

Computer Simulation of Saturn’s Ring Structure

Cole Kendrick

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Tags: Astrophysics, ATI, ATI Radeon HD 5850, Gravitation, MPI, N-body simulation, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Tesla S2050

April 21, 2013 by hgpu

A Performance Comparison of Different Graphics Processing Units Running Direct N-Body Simulations

R. Capuzzo-Dolcettaa, M. Spera

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Tags: Astrophysics, ATI, ATI Radeon HD 6970, ATI Radeon HD 7870, ATI Radeon HD 7970, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenCL, Package, Tesla C1060, Tesla C2050, Tesla K20

April 9, 2013 by hgpu

GRay: a Massively Parallel GPU-Based Code for Ray Tracing in Relativistic Spacetimes

Chi-Kwan Chan, Dimitrios Psaltis, Feryal Ozel

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GT 650 M, Package, Raytracing, Tesla M2090

March 23, 2013 by hgpu

Chrono: a parallel multi-physics library for rigid-body, flexible-body, and fluid dynamics

H. Mazhar, T. Heyn, A. Pazouki, D. Melanz, A. Seidl, A. Bartholomew, A. Tasora, D. Negrut

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Tags: CUDA, Fluid dynamics, nVidia, Package, Physics, Visualization

February 21, 2013 by hgpu

Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods

Christopher Cooper, Lorena A. Barba

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Tags: Bioinformatics, Biophysics, Boltzmann equation, Computational Physics, Computer science, CUDA, Electrostatics, nVidia, Package, Physics, Poisson Boltzmann, Python, Tesla C2075

January 31, 2013 by hgpu

A short guide to CUDA C: For physicists with multi-core graphics cards

David Plotzki

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Tags: Computational Physics, Computer science, CUDA, nVidia, nVidia Quadro NVS 3100 M, Package, Physics, Tesla C1060, Tutorial

October 19, 2012 by hgpu

Efficient deconvolution methods for astronomical imaging: algorithms and IDL-GPU codes

M. Prato, R. Cavicchioli, L. Zanni, P. Boccacci, M. Bertero

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Tags: Algorithms, Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, Numerical Analysis, nVidia, nVidia GeForce GTX 280, Package

October 9, 2012 by hgpu

CUTE solutions for two-point correlation functions from large cosmological datasets

David Alonso

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia Quadro NVS 4200 M, Package, Tesla C2070

October 8, 2012 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

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

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high performance computing on graphics processing units: hgpu.org

Packages

Trapping of giant-planet cores – I. vortex aided trapping at the outer dead zone edge

Point Spread Function Estimation of Solar Surface Images with a Cooperative Particle Swarm Optimization on GPUs

Gauge fixing in lattice QCD with multi-GPUs

Simulating the universe with GPU-accelerated supercomputers: n-body methods, tests, and examples

Computer Simulation of Saturn’s Ring Structure

A Performance Comparison of Different Graphics Processing Units Running Direct N-Body Simulations

GRay: a Massively Parallel GPU-Based Code for Ray Tracing in Relativistic Spacetimes

Chrono: a parallel multi-physics library for rigid-body, flexible-body, and fluid dynamics

Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods

A short guide to CUDA C: For physicists with multi-core graphics cards

Efficient deconvolution methods for astronomical imaging: algorithms and IDL-GPU codes

CUTE solutions for two-point correlation functions from large cosmological datasets

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

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.

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

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