high performance computing on graphics processing units: hgpu.org

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Utilizing massive parallelism in decoding of modern error-correcting codes for accelerating communication systems simulations

Eugen Ruzicky, Markus Rupp, Peter Farkas, Atilio Gameiro

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Tags: Algorithms, Computer science, CUDA, Error recovery, nVidia, nVidia GeForce GTX 680

November 13, 2013 by hgpu

Tiled QR Decomposition and Its Optimization on CPU and GPU Computing System

Dongjin Kim, Kyu-Ho Park

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Matrix decomposition, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680

November 8, 2013 by hgpu

Parallelizing Alternating Direction Implicit Solver on GPUs

Zhangping Wei, Byunghyun Jang, Yaoxin Zhang, Yafei Jia

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Performance

November 6, 2013 by hgpu

GPU Accelerated Blood Flow Computation using the Lattice Boltzmann Method

Cosmin Nita, Lucian Mihai Itu, Constantin Suciu, Constantin Suciu

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Tags: CUDA, Fluid dynamics, Lattice Boltzmann model, Medicine, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 650, nVidia GeForce GTX 680

October 30, 2013 by hgpu

Morphological Proximity Priors: Spatial Relationships for Semantic Segmentation

Julia Bergbauer, Claudia Nieuwenhuis, Mohamed Souiai, Daniel Cremers

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 680

October 29, 2013 by hgpu

A multi-Teraflop Constituency Parser using GPUs

John Canny, David Hall, Dan Klein

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Source codes

Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 680, Package, Tesla K10

October 24, 2013 by hgpu

High-Performance GPGPU Programming with OCaml

Mathias Bourgoin, Emmanuel Chailloux, Jean-Luc Lamotte

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Tags: ATI, ATI Radeon HD 6950, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 680, OpenCL, Tesla C2070

October 15, 2013 by hgpu

Coupling a Generalized DEM and an SPH Models Under a Heterogeneous Massively Parallel Framework

Ricardo Canelas, Jose M. Dominguez, Rui M. L. Ferreira

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Tags: CUDA, Fluid dynamics, Heterogeneous systems, MPI, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia GeForce GTX 680

October 12, 2013 by hgpu

Automatic run-time mapping of polyhedral computations to heterogeneous devices with memory-size restrictions

Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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Tags: Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 680

October 12, 2013 by hgpu

Parallel Hyperspectral Unmixing on GPUs

Jose M. P. Nascimento, Jose M. Bioucas-Dias, Jose M. Rodriguez Alves, Vitor Silva, Antonio Plaza

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Tags: Algorithms, Image processing, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 680, OpenCL, Tesla C2050

October 2, 2013 by hgpu

A graphics processor-based intranuclear cascade and evaporation simulation

H. Wan Chan Tseung, C. Beltran

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Tags: Computational Physics, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 680, Physics

October 2, 2013 by hgpu

GPU-Accelerated Real-Time Surveillance De-Weathering

Niklas Pettersson

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Tags: Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 680, Thesis

October 1, 2013 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

Utilizing massive parallelism in decoding of modern error-correcting codes for accelerating communication systems simulations

Tiled QR Decomposition and Its Optimization on CPU and GPU Computing System

Parallelizing Alternating Direction Implicit Solver on GPUs

GPU Accelerated Blood Flow Computation using the Lattice Boltzmann Method

Morphological Proximity Priors: Spatial Relationships for Semantic Segmentation

A multi-Teraflop Constituency Parser using GPUs

High-Performance GPGPU Programming with OCaml

Coupling a Generalized DEM and an SPH Models Under a Heterogeneous Massively Parallel Framework

Automatic run-time mapping of polyhedral computations to heterogeneous devices with memory-size restrictions

Parallel Hyperspectral Unmixing on GPUs

A graphics processor-based intranuclear cascade and evaporation simulation

GPU-Accelerated Real-Time Surveillance De-Weathering

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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