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CUDA implementation of Wagener’s 2D convex hull PRAM algorithm

Colm O. Dunlaing

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Tags: Computer science, CUDA, nVidia

March 23, 2012 by hgpu

Advanced Programming Platform for efficient use of Data Parallel Hardware

Luis Cabellos

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Tags: Computer science, Distributed computing, nVidia, OpenCL, Tesla C1060

March 23, 2012 by hgpu

Impact of asynchronism on GPU accelerated parallel iterative computations

Sylvain Contassot-Vivier, Thomas Jost, Stephane Vialle

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, Partial differential equations, PDEs, Performance

March 22, 2012 by hgpu

GPU-based parallel collision detection for fast motion planning

Jia Pan, Dinesh Manocha

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Tags: Clustering, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 480

March 22, 2012 by hgpu

Multi-target vectorization with MTPS C++ generic library

Wilfried Kirschenmann, Laurent Plagne, Stephane Vialle

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia Quadro FX 5800

March 22, 2012 by hgpu

High Speed Compressed Sensing Reconstruction in Dynamic Parallel MRI Using Augmented Lagrangian and Parallel Processing

Cagdas Bilen, Yao Wang, Ivan Selesnick

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Tags: Algorithms, Compression, Computer science, Data Structures and Algorithms, Magnetic resonance imaging, MRI, nVidia, nVidia Quadro FX 4800, Sensing

March 22, 2012 by hgpu

Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

Weizhi Xu, Zhiyong Liu, Dongrui Fan, Shuai Jiao, Xiaochun Ye, Fenglong Song, Chenggang Yan

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 295, Performance, Sparse matrix

March 21, 2012 by hgpu

Parallel Two-Stage Least Squares algorithms for Simultaneous Equations Models on GPU

Carla Ramiro Sanchez, Jose J. Lopez-Espin, Domingo Gimenez, Antonio M. Vidal

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C2070

March 21, 2012 by hgpu

Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware

Ahmed H. El Zein, Alistair P. Rendell

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 295, Performance, Sparse matrix, Tesla S2050

March 19, 2012 by hgpu

Spatial Join with R-Tree on Graphics Processing Units

Tongjai Yampaka, Prabhas Chongstitvatana

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GT 440

March 19, 2012 by hgpu

Analysis of the Performance of the Fish School Search Algorithm Running in Graphic Processing Units

Anthony J. C. C. Lins, Carmelo J. A. Bastos-Filho, Debora N. O. Nascimento, Marcos A. C. Oliveira Junior, Fernando B. de Lima-Neto

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Search

March 19, 2012 by hgpu

Parallelization of Particle Filter Algorithms

Matthew A. Goodrum, Michael J. Trotter, Alla Aksel, Scott T. Acton, Kevin Skadron

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Video tracking

March 19, 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

CUDA implementation of Wagener’s 2D convex hull PRAM algorithm

Advanced Programming Platform for efficient use of Data Parallel Hardware

Impact of asynchronism on GPU accelerated parallel iterative computations

GPU-based parallel collision detection for fast motion planning

Multi-target vectorization with MTPS C++ generic library

High Speed Compressed Sensing Reconstruction in Dynamic Parallel MRI Using Augmented Lagrangian and Parallel Processing

Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

Parallel Two-Stage Least Squares algorithms for Simultaneous Equations Models on GPU

Generating optimal CUDA sparse matrix-vector product implementations for evolving GPU hardware

Spatial Join with R-Tree on Graphics Processing Units

Analysis of the Performance of the Fish School Search Algorithm Running in Graphic Processing Units

Parallelization of Particle Filter Algorithms

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

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