Computational Physics | Page 13

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From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

Marek Blazewicz, Ian Hinder, David M. Koppelman, Steven R. Brandt, Milosz Ciznicki, Michal Kierzynka, Frank Loffler, Erik Schnetter, Jian Tao

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Tags: Code generation, Computational Physics, CUDA, Differential equations, Finite difference, MPI, nVidia, OpenMP, Partial differential equations, PDEs, Physics, Tesla M2050

July 25, 2013 by hgpu

PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

L.Yu. Barash, L.N. Shchur

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Tags: Algorithms, Computational Physics, Computer science, CUDA, Mathematical Software, nVidia, Package, Pseudo-random number generators, Tesla C2050

July 25, 2013 by hgpu

Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods

Xavier Andrade, Alan Aspuru-Guzik

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Tags: ATI, ATI Radeon HD 7970, Computational Physics, Condensed matter, Data parallelism, nVidia, OpenCL, Package, Physics, Tesla K20

June 14, 2013 by hgpu

A flexible algorithm for calculating pair interactions on SIMD architectures

Szilard Pall, Berk Hess

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Tags: Algorithms, Computational Physics, CUDA, Molecular dynamics, nVidia, Physics

June 10, 2013 by hgpu

Using GPU Simulation to Accurately Fit to the Power-Law Distribution

Efstratios Rappos, Stephan Robert

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Tags: Computational Physics, CUDA, Mathematics, nVidia, nVidia GeForce GTX 590, Statistics

May 30, 2013 by hgpu

Making the case of GPUs in courses on computational physics

Knut Skogstrand Gjerden

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Tags: Computational Physics, CUBLAS, CUDA, Education, FFT, Mathematical Software, nVidia, nVidia GeForce GTX 320 M, Physics, Tesla C2050

May 17, 2013 by hgpu

Fast computation of MadGraph amplitudes on graphics processing unit (GPU)

K. Hagiwara, J. Kanzaki, Q. Li, N. Okamura, T. Stelzer

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Tags: Computational Physics, CUDA, Fortran, Monte Carlo integration, nVidia, Physics, Pseudo-random number generators, QCD, Tesla C2075

May 6, 2013 by hgpu

Feasibility Analysis of Low Cost Graphical Processing Units for Electromagnetic Field Simulations by Finite Difference Time Domain Method

A. V. Choudhari, N. A. Pande, M. R. Gupta

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Tags: Computational Physics, Computer science, CUDA, Electrodynamics, FDTD, Finite difference, Finite-difference time-domain, Maxwell's equations, nVidia, nVidia GeForce GT 520

May 3, 2013 by hgpu

GPU accelerated Trotter-Suzuki solver for quantum spin dynamics

Axel D. Dente, Carlos S. Bederian, Pablo R. Zangara, Horacio M. Pastawski

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Tags: Algorithms, Computational Physics, CUDA, nVidia, Physics, Quantum Physics, Spin Systems, Statistical Mechanics, Tesla C2070

May 2, 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

Reaction-diffusion model Monte Carlo simulations on the GPU

Raoul D. Schram

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Tags: Computational Physics, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, OpenCL, Physics, Statistical Mechanics

January 28, 2013 by hgpu

Programming CUDA and OpenCL: A Case Study Using Modern C++ Libraries

Denis Demidov, Karsten Ahnert, Karl Rupp, Peter Gottschling

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Tags: ATI, ATI Radeon HD 7970, Computational Physics, Computer science, CUDA, Differential equations, Mathematical Software, nVidia, ODEs, OpenCL, Ordinary differential equations, Package, Tesla C2070

January 2, 2013 by hgpu

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

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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

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high performance computing on graphics processing units: hgpu.org

From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation

PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods

A flexible algorithm for calculating pair interactions on SIMD architectures

Using GPU Simulation to Accurately Fit to the Power-Law Distribution

Making the case of GPUs in courses on computational physics

Fast computation of MadGraph amplitudes on graphics processing unit (GPU)

Feasibility Analysis of Low Cost Graphical Processing Units for Electromagnetic Field Simulations by Finite Difference Time Domain Method

GPU accelerated Trotter-Suzuki solver for quantum spin dynamics

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

Reaction-diffusion model Monte Carlo simulations on the GPU

Programming CUDA and OpenCL: A Case Study Using Modern C++ Libraries

Recent source codes

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

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

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

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

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