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Clustering billions of data points using GPUs

Ren Wu, Bin Zhang, Meichun Hsu

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Tags: Algorithms, Clustering, Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 280

November 5, 2010 by hgpu

On Dynamic Load Balancing on Graphics Processors

Daniel Cederman, Philippas Tsigas

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 9600 GT, Optimization, Performance

November 5, 2010 by hgpu

MPI within a GPU

Bobby Dalton Young

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Tags: Computer science, CUDA, Data parallelism, MPI, nVidia, nVidia GeForce 8800 GTS, Thesis, Virtualization

November 5, 2010 by hgpu

An experimental approach to performance measurement of heterogeneous parallel applications using CUDA

Allen D. Malony,Scott Biersdorff,Wyatt Spear,Shangkar Mayanglambam

Tags: Computer science, CUDA, nVidia, Performance

November 5, 2010 by hgpu

Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping

E. Z. Zhang, Y. Jiang, G. Ziyu, X. Shen

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Tags: Compilers, Computer science, CUDA, nVidia, Performance, Programming Languages, Tesla C1060

November 5, 2010 by hgpu

GPU-Accelerated Nearest Neighbor Search for 3D Registration

Deyuan Qiu,Stefan May,Andreas Nuchter

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Tags: Computer science, Computer vision, CUDA, Nearest neighbour, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295

November 5, 2010 by hgpu

Debugging GPU stream programs through automatic dataflow recording and visualization

Qiming Hou,Kun Zhou,Baining Guo

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Programming techniques

November 5, 2010 by hgpu

Accelerating MATLAB Image Processing Toolbox functions on GPUs

Jingfei Kong, Martin Dimitrov, Yi Yang, Janaka Liyanage, Lin Cao, Jacob Staples, Mike Mantor, Huiyang Zhou

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Tags: ATI, ATI Radeon HD 5870, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, OpenCL, RV870

November 5, 2010 by hgpu

Accelerating advanced MRI reconstructions on GPUs

Samuel S. Stone, Justin P. Haldar, Stephanie C. Tsao, Wen-Mei W. Hwu, Zhi-Pei Liang, Bradley P. Sutton

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Tags: CUDA, Image processing, Medicine, nVidia, nVidia Quadro FX 5600

November 5, 2010 by hgpu

Efficient computation of sum-products on GPUs through software-managed cache

Mark Silberstein,Assaf Schuster,Dan Geiger,Anjul Patney,John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Programming techniques

November 5, 2010 by hgpu

Iterative induced dipoles computation for molecular mechanics on GPUs

Frederico Pratas,Ricardo A. Mata,Leonel Sousa

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Tags: Chemistry, Computational chemistry, Computer science, CUDA, Molecular dynamics, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285

November 5, 2010 by hgpu

The Scalable Heterogeneous Computing (SHOC) benchmark suite

Anthony Danalis,Gabriel Marin,Collin McCurdy,Jeremy S. Meredith,Philip C. Roth,Kyle Spafford,Vinod Tipparaju,Jeffrey S. Vetter

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Source codes

Tags: ATI, ATI Radeon HD 5770, ATI Radeon HD 5870, ATI Stream, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTS, nVidia GeForce 8800 GTX, nVidia GeForce 9400 M, OpenCL, Package, Performance, Tesla C1060

November 5, 2010 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

Programming

Clustering billions of data points using GPUs

On Dynamic Load Balancing on Graphics Processors

MPI within a GPU

An experimental approach to performance measurement of heterogeneous parallel applications using CUDA

Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping

GPU-Accelerated Nearest Neighbor Search for 3D Registration

Debugging GPU stream programs through automatic dataflow recording and visualization

Accelerating MATLAB Image Processing Toolbox functions on GPUs

Accelerating advanced MRI reconstructions on GPUs

Efficient computation of sum-products on GPUs through software-managed cache

Iterative induced dipoles computation for molecular mechanics on GPUs

The Scalable Heterogeneous Computing (SHOC) benchmark suite

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)