high performance computing on graphics processing units: hgpu.org

hgpu.org » Computational Physics

GPU peer-to-peer techniques applied to a cluster interconnect

Roberto Ammendola, Massimo Bernaschi, Andrea Biagioni, Mauro Bisson, Massimiliano Fatica, Ottorino Frezza, Francesca Lo Cicero, Alessandro Lonardo, Enrico Mastrostefano, Pier Stanislao Paolucci, Davide Rossetti, Francesco Simula, Laura Tosoratto, Piero Vicini

View

Tags: Computational Physics, CUDA, FPGA, MPI, nVidia, Physics, Tesla S2075

August 1, 2013 by hgpu

Graphics Processing Unit acceleration of the Random Phase Approximation in the projector augmented wave method

Jun Yan, Lin Li, Christopher O'Grady

View

Tags: Computational Physics, Condensed matter, CUDA, Materials Science, nVidia, Package, Physics, Tesla C2075

July 31, 2013 by hgpu

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

View

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

View

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

View

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

View

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

View

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

View

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

View

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

View

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

View

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

View

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

January 31, 2013 by hgpu

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

See all packages

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us:

contact@hpgu.org