high performance computing on graphics processing units: hgpu.org

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Fast End-to-End Multi-Conjugate AO Simulations Using Graphical Processing Units and the MAOS Simulation Code

Lianqi Wang, Brent Ellerbroek

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Tags: CUDA, nVidia, nVidia GeForce GTX 580, Optics, Package, Physics

July 25, 2012 by hgpu

Sparse Approximate Inverse Preconditioners for Iterative Solvers on GPUs

Mykola Lukash, Karl Rupp, Siegfried Selberherr

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Tags: Computer science, nVidia, nVidia GeForce GTX 580, OpenCL, Sparse linear iterative solvers

July 22, 2012 by hgpu

Temporal Blending for Adaptive SPH

Jens Orthmann, Andreas Kolb

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Tags: 3D Graphics and Realism, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce GTX 580, Particle simulation

July 18, 2012 by hgpu

Multi-level Parallelization of Advanced Video Coding on Hybrid CPU/GPU Platform

Svetislav Momcilovic, Nuno Roma, Leonel Sousa

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Tags: Algorithms, CUDA, H.264/AVC, Image processing, nVidia, nVidia GeForce GTX 580, Task scheduling, Video encoding

July 10, 2012 by hgpu

Performance models for CUDA streams on NVIDIA GeForce series

Juan Gomez-Luna, Jose Maria Gonzalez-Linares, Jose Ignacio Benavides, Nicolas Guil

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 9800 GX2, nVidia GeForce GTX 260, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Performance

July 9, 2012 by hgpu

Interactive BRDF Estimation for Mixed-Reality Applications

Martin Knecht, Georg Tanzmeister, Christoph Traxler, Michael Wimmer

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Tags: Augmented reality, Computer science, DirectX, nVidia, nVidia GeForce GTX 580, Rendering

July 8, 2012 by hgpu

GPU-Optimized Molecular Dynamics Simulations

Tyson J. Lipscomb

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Tags: Biology, Biomolecules, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Optimization, Pseudo-random number generators, Review, RNA, Tesla C2070, Thesis

July 8, 2012 by hgpu

Halo Gathering Scalability for Large Scale Multi-dimensional Sznajd Opinion Models Using Data Parallelism with GPUs

K.A. Hawick, D.P. Playne

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Tags: Computer science, CUDA, Data parallelism, Numerical simulation, nVidia, nVidia GeForce GTX 580, Phase transition

July 2, 2012 by hgpu

Explicit Shallow Water Simulations on GPUs: Guidelines and Best Practices

Andre R. Brodtkorb, Martin L. Saetra

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580

June 27, 2012 by hgpu

Multi-GPU Island-Based Genetic Algorithm for Solving the Knapsack Problem

Jiri Jaros

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Tags: Algorithms, Computer science, CUDA, Genetic programming, GPU cluster, MPI, nVidia, nVidia GeForce GTX 580, Package

June 26, 2012 by hgpu

Compiling a high-level language for GPUs: (via language support for architectures and compilers)

Christophe Dubach, Perry Cheng, Rodric Rabbah, David F. Bacon, Stephen J. Fink

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Tags: ATI, ATI Radeon HD 5970, Compilers, Computer science, CUDA, Heterogeneous systems, Java, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 580, OpenCL, Programming Languages

June 26, 2012 by hgpu

GPU-based Cloud Computing for Comparing the Structure of Protein Binding Sites

Matthias Leinweber, Lars Baumgartner, Marco Mernberger, Thomas Fober, Eyke Hullermeier, Gerhard Klebe, Bernd Freisleben

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Tags: Biology, Java, Macromolecule, nVidia, nVidia GeForce GTX 580, OpenCL, Tesla M2050

June 26, 2012 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Fast End-to-End Multi-Conjugate AO Simulations Using Graphical Processing Units and the MAOS Simulation Code

Sparse Approximate Inverse Preconditioners for Iterative Solvers on GPUs

Temporal Blending for Adaptive SPH

Multi-level Parallelization of Advanced Video Coding on Hybrid CPU/GPU Platform

Performance models for CUDA streams on NVIDIA GeForce series

Interactive BRDF Estimation for Mixed-Reality Applications

GPU-Optimized Molecular Dynamics Simulations

Halo Gathering Scalability for Large Scale Multi-dimensional Sznajd Opinion Models Using Data Parallelism with GPUs

Explicit Shallow Water Simulations on GPUs: Guidelines and Best Practices

Multi-GPU Island-Based Genetic Algorithm for Solving the Knapsack Problem

Compiling a high-level language for GPUs: (via language support for architectures and compilers)

GPU-based Cloud Computing for Comparing the Structure of Protein Binding Sites

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)