high performance computing on graphics processing units: hgpu.org

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Parallel implementation of Multi-dimensional Ensemble Empirical Mode Decomposition

Li-Wen Chang, Men-Tzung Lo, Nasser Anssari, Ke-Hsin Hsu, Norden E. Huang, Wen-mei W. Hwu

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Tags: CUDA, nVidia, Signal processing, Tesla C2050

July 22, 2011 by hgpu

GPGPU-Accelerated Parallel and Fast Simulation of Thousand-Core Platforms

Christian Pinto, Shivani Raghav, Andrea Marongiu, Martino Ruggiero, David Atienza, Luca Benini

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Tags: Computer science, CUDA, Design space exploration, nVidia, nVidia GeForce GTX 480

July 22, 2011 by hgpu

A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads

Shuai Che, Jeremy W. Sheaffer, Michael Boyer, Lukasz G. Szafaryn, Liang Wang, Kevin Skadron

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Tags: Benchmarking, CMP, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480

July 22, 2011 by hgpu

Real time image reconstruction using GPUs for a surgical PET imaging probe system

S.S. Huh, Li Han, W.L. Rogers, N.H. Clinthorne

Tags: CUDA, Image reconstruction, Medicine, Monte Carlo simulation, nVidia, nVidia GeForce 9800 GTX, PET, Tomography

July 22, 2011 by hgpu

AES Encryption Implementation on CUDA GPU and Its Analysis

Keisuke Iwai, Takakazu Kurokawa, Naoki Nisikawa

Tags: AES, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Security

July 22, 2011 by hgpu

Large Scale Simulations of the Euler Equations on GPU Clusters

Manfred Liebmann, Craig C. Douglas, Gundolf Haase, Zoltan Horvath

Tags: CUDA, Fluid dynamics, GPU cluster, nVidia, nVidia GeForce GTX 295

July 22, 2011 by hgpu

Mapping the Arnold web with a GPU-supercomputer

A. Seibert, S. Denisov, Alexey V. Ponomarev, P. Hanggi

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Tags: Chaotic Dynamics, Computational Physics, CUDA, Nonlinear Sciences, nVidia, Physics, Tesla M2050

July 22, 2011 by hgpu

Accelerating Radio Astronomy Cross-Correlation with Graphics Processing Units

M. A. Clark, P. C. La Plante, L. J. Greenhill

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 480, Tesla C1060

July 22, 2011 by hgpu

A Comparison of xPU Platforms Exemplified with Ray Tracing Algorithms

Rafael Huff, Thomas Gierlinger, Arjan Kuijper, Andre Stork, Dieter Fellner

Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, OpenCL, Raytracing

July 22, 2011 by hgpu

A self-organization based optical flow estimator with GPU implementation (thesis)

Manish Shiralkar

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 480, nVidia Quadro FX 5600, nVidia Quadro NVS 140 M, Optical flow, Self-organizing map, Tesla T10, Thesis

July 22, 2011 by hgpu

A self-organization based optical flow estimator with GPU implementation

Manish Shiralkar, Robert Schalkoff

Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 480, nVidia Quadro FX 5600, nVidia Quadro NVS 140 M, Optical flow, Self-organizing map, Tesla T10, Thesis

July 22, 2011 by hgpu

Parallelizing the Cellular Potts Model on graphics processing units

Jose J. Tapia, Roshan M. D'Souza

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Tags: Biology, Computational biology, CUDA, nVidia, Tesla C1060

July 22, 2011 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Parallel implementation of Multi-dimensional Ensemble Empirical Mode Decomposition

GPGPU-Accelerated Parallel and Fast Simulation of Thousand-Core Platforms

A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads

Real time image reconstruction using GPUs for a surgical PET imaging probe system

AES Encryption Implementation on CUDA GPU and Its Analysis

Large Scale Simulations of the Euler Equations on GPU Clusters

Mapping the Arnold web with a GPU-supercomputer

Accelerating Radio Astronomy Cross-Correlation with Graphics Processing Units

A Comparison of xPU Platforms Exemplified with Ray Tracing Algorithms

A self-organization based optical flow estimator with GPU implementation (thesis)

A self-organization based optical flow estimator with GPU implementation

Parallelizing the Cellular Potts Model on graphics processing units

Recent source codes

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

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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