high performance computing on graphics processing units: hgpu.org

A new ray-tracing scheme for 3D diffuse radiation transfer on highly parallel architectures

Satoshi Tanaka, Kohji Yoshikawa, Takashi Okamoto, Kenji Hasegawa

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, Numerical simulation, nVidia, Raytracing, Tesla M2090

October 8, 2014 by hgpu

A fast GPU-based Monte Carlo simulation of proton transport with detailed modeling of non-elastic interactions

H. Wan Chan Tseung, J. Ma, C. Beltran

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Tags: CUDA, Medical Physics, Monte Carlo simulation, nVidia, nVidia GeForce GTX 680, Physics, Tesla K20

October 3, 2014 by hgpu

Explicit Integration with GPU Acceleration for Large Kinetic Networks

Benjamin Brock, Andrew Belt, Jay Jay Billings, Mike Guidry

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Tags: Algorithms, Computational Physics, CUDA, nVidia, Physics, Solar and Stellar Astrophysics, Tesla K20

September 23, 2014 by hgpu

Computational Gravitational Dynamics with Modern Numerical Accelerators

Simon Portegies Zwart, Jeroen Bedorf

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Tags: Algorithms, Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, Stellar dynamics, Tesla K20

September 23, 2014 by hgpu

GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA

J. Spiechowicz, M. Kostur, L. Machura

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Tags: Computational Physics, CUDA, Differential equations, Monte Carlo integration, Monte Carlo simulation, nVidia, nVidia GeForce GTX 680, Physics, Stochastic simulation

September 19, 2014 by hgpu

Using hybrid GPU/CPU kernel splitting to accelerate spherical convolutions

P. M. Sutter, Benjamin D. Wandelt, Franz Elsner

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Tags: Astrophysics, Cosmology, CUDA, nVidia, nVidia GeForce GTX 480

September 17, 2014 by hgpu

Analysis of GPU-based convolution for acoustic wave propagation modeling with finite differences: Fortran to CUDA-C step-by-step

Makoto Sadahiro

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Tags: Acoustics, CUDA, Finite difference, Fortran, nVidia, Physics, Tesla K20, Thesis

September 13, 2014 by hgpu

Research on double negative materials by using FDTD method based on GPUs

Chen Shen, Xian-liang Wu

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Tags: CUDA, FDTD, Finite difference, Finite-difference time-domain, Materials Science, nVidia, nVidia GeForce GT 440, Physics

September 5, 2014 by hgpu

HISQ inverter on Intel Xeon Phi and NVIDIA GPUs

O. Kaczmarek, C. Schmidt, P. Steinbrecher, Swagato Mukherjee, M. Wagner

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Tags: Conjugate gradient solver, High Energy Physics - Lattice, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan, Physics, QCD, Sparse matrix, Tesla K20, Tesla K40

September 5, 2014 by hgpu

CUDA method for the FDTD simulation by GPU

Wonhak Son

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Tags: CUDA, FDTD, Finite-difference time-domain, MPI, nVidia, nVidia GeForce GTX Titan, Physics, Thesis

September 4, 2014 by hgpu

A Framework for Lattice QCD Calculations on GPUs

F. T. Winter, M. A. Clark, R. G. Edwards, B. Joo

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Tags: Algorithms, Code generation, Computational Physics, CUDA, High Energy Physics - Lattice, Mathematical Software, nVidia, Physics, PTX, QCD, Tesla K20

August 27, 2014 by hgpu

Algorithms for Solving Non-Stationary Heat Conduction Problem for Design of a Technical Device

Alexander Ayriyan, Jan Busa Jr., Eugeny E. Donets, Hovik Grigorian, Jan Pribis

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Tags: Algorithms, Computational Physics, Differential equations, Finite difference, nVidia, nVidia GeForce GTX 470, OpenCL, Partial differential equations, PDEs, Physics

August 27, 2014 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

A new ray-tracing scheme for 3D diffuse radiation transfer on highly parallel architectures

A fast GPU-based Monte Carlo simulation of proton transport with detailed modeling of non-elastic interactions

Explicit Integration with GPU Acceleration for Large Kinetic Networks

Computational Gravitational Dynamics with Modern Numerical Accelerators

GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA

Using hybrid GPU/CPU kernel splitting to accelerate spherical convolutions

Analysis of GPU-based convolution for acoustic wave propagation modeling with finite differences: Fortran to CUDA-C step-by-step

Research on double negative materials by using FDTD method based on GPUs

HISQ inverter on Intel Xeon Phi and NVIDIA GPUs

CUDA method for the FDTD simulation by GPU

A Framework for Lattice QCD Calculations on GPUs

Algorithms for Solving Non-Stationary Heat Conduction Problem for Design of a Technical Device

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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