high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce GTX 480

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Yongpeng Zhang, Frank Mueller

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 5, 2014 by hgpu

Improved Implementation of Simulation for Membrane Computing on the Graphic Processing Unit

Ali Maroosi, Ravie Chandren Muniyandi, Elankovan A. Sundararajan, Abdullah Mohd Zin

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Tags: Computer science, CUDA, Membrane computing, nVidia, nVidia GeForce GTX 480

February 5, 2014 by hgpu

Scheduling on Manycore and Heterogeneous Graphics Processors

Stanley Tzeng

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL, Thesis

January 28, 2014 by hgpu

k+-buffer: Fragment Synchronized k-buffer

Andreas A. Vasilakis, Ioannis Fudos

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 480, OpenGL, Package, Real-time graphics, Rendering, Visualization

January 14, 2014 by hgpu

A Framework for Productive, Efficient and Portable Parallel Computing

Ekaterina I. Gonina

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Tags: Algorithms, Code generation, Computer science, CUDA, MapReduce, nVidia, nVidia GeForce GTX 480, Package, Python, Thesis

January 12, 2014 by hgpu

A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units

Andrea Manconi, Alessandro Orro, Emanuele Manca, Giuliano Armano, Luciano Milanesi

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 480, Package, Smith-Waterman algorithm, Tesla K20

January 12, 2014 by hgpu

Warp-Level Divergence in GPUs: Characterization, Impact, and Mitigation

Ping Xiang, Yi Yang, Huiyang Zhou

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 480, Performance

January 12, 2014 by hgpu

Adhoc On-Demand Distance Vector Protocol For Energy Efficiency

Abhilash C S, Abhishek Varshney, Dilip S, Navik Yogesh Laljibhai, Pradyot H Adavi

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 480, Security

January 3, 2014 by hgpu

Accelerating Simulation Codes through the GeMTC Framework

Digvijay Singh Gahlot, Scott Krieder, Ioan Raicu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Performance

January 3, 2014 by hgpu

Fast Parallel Image Registration on CPU and GPU for Diagnostic Classification of Alzheimer’s Disease

Denis P. Shamonin, Esther E. Bron, Boudewijn P. Lelieveldt, Marion Smits, Stefan Klein, Marius Staring

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Tags: Bioinformatics, Image processing, Image registration, Medicine, MRI, Neuroscience, nVidia, nVidia GeForce GTX 480, OpenCL

January 2, 2014 by hgpu

Achieving TeraCUPS on Longest Common Subsequence Problem using GPGPUs

Adnan Ozsoy, Arun Chauhan, Martin Swany

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 690, Sequence alignment, Sequence matching, Tesla C2075, Tesla M2090

January 2, 2014 by hgpu

Optimizing LZSS Compression on GPGPUs

Adnan Ozsoy, Martin Swany, Arun Chauhan

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, String matching, Tesla C2075

December 29, 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

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Improved Implementation of Simulation for Membrane Computing on the Graphic Processing Unit

Scheduling on Manycore and Heterogeneous Graphics Processors

k+-buffer: Fragment Synchronized k-buffer

A Framework for Productive, Efficient and Portable Parallel Computing

A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units

Warp-Level Divergence in GPUs: Characterization, Impact, and Mitigation

Adhoc On-Demand Distance Vector Protocol For Energy Efficiency

Accelerating Simulation Codes through the GeMTC Framework

Fast Parallel Image Registration on CPU and GPU for Diagnostic Classification of Alzheimer’s Disease

Achieving TeraCUPS on Longest Common Subsequence Problem using GPGPUs

Optimizing LZSS Compression on GPGPUs

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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