high performance computing on graphics processing units: hgpu.org

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To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms

Vajira Thambawita, Roshan Ragel, Dhammika Elkaduwe

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GT 740 M, Performance, String matching

December 30, 2014 by hgpu

Accelerating Correlation Power Analysis Using Graphics Processing Units

Hasindu Gamaarachchi, Roshan Ragel, Darshana Jayasinghe

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Tags: AES, Algorithms, Computer science, CUDA, nVidia, Performance, Security, Tesla C2075

December 26, 2014 by hgpu

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan

December 26, 2014 by hgpu

Computationally Efficient Implementation of a Hamming Code Decoder using a Graphics Processing Unit

Shohidul Islam, Cheol-Hong Kim, Jong-Myon Kim

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 560, Signal processing

December 26, 2014 by hgpu

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, Yann LeCun

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Tags: Algorithms, CNN, Computer science, CUDA, FFT, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Package, Tesla K40

December 26, 2014 by hgpu

A Domain-specific Language to Facilitate Software Defined Radio Parallel Executable Patterns Deployment on Heterogeneous Architectures

Lerato J. Mohapi, Simon Winberg, Michael Inggs

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Tags: Algorithms, CUDA, DSP, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, Signal processing

December 22, 2014 by hgpu

Fast Solving of Influence Diagrams for Multiagent Planning on GPU-enabled Architectures

Fadel Adoe, Yingke Chen, Prashant Doshi

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Tags: Algorithms, Bayesian, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Package

December 22, 2014 by hgpu

A Review on Parallelization of Node based Game Tree Search Algorithms on GPU

Rutuja U. Gosavi, Payal S. Kulkarni

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia

December 20, 2014 by hgpu

A Parallel Recursive Approach for Solving All Pairs Shortest Path Problem on GPU using OpenCL

Manish Pandey, Sanjay Sharma

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Tags: Algorithms, ATI, ATI Radeon HD 6450, ATI Radeon HD 6850, Computer science, nVidia, nVidia GeForce GT 630 M, OpenCL, Path problems

December 20, 2014 by hgpu

SignalPU: A programming model for DSP applications on parallel and heterogeneous clusters

Farouk Mansouri, Sylvain Huet, Dominique Houzet

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Tags: Algorithms, CUDA, DSP, Heterogeneous systems, nVidia, nVidia GeForce GT 610, nVidia GeForce GTX Titan, nVidia Quadro 4000, Signal processing

December 20, 2014 by hgpu

Multicore Scheduling of Parallel Real-Time Tasks with Multiple Parallelization Options

Jihye Kwon, Kang-Wook Kim, Sangyoun Paik, Jihwa Lee, Chang-Gun Lee

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Tags: Algorithms, Computer science, OpenCL, Task scheduling

December 18, 2014 by hgpu

Efficient GPU Implementation for Single Block Orthogonal Dictionary Learning

Paul Irofti

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Tags: Algorithms, ATI, ATI FirePro V8800, Computer science, Neural and Evolutionary Computing, OpenCL

December 18, 2014 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

To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms

Accelerating Correlation Power Analysis Using Graphics Processing Units

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Computationally Efficient Implementation of a Hamming Code Decoder using a Graphics Processing Unit

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

A Domain-specific Language to Facilitate Software Defined Radio Parallel Executable Patterns Deployment on Heterogeneous Architectures

Fast Solving of Influence Diagrams for Multiagent Planning on GPU-enabled Architectures

A Review on Parallelization of Node based Game Tree Search Algorithms on GPU

A Parallel Recursive Approach for Solving All Pairs Shortest Path Problem on GPU using OpenCL

SignalPU: A programming model for DSP applications on parallel and heterogeneous clusters

Multicore Scheduling of Parallel Real-Time Tasks with Multiple Parallelization Options

Efficient GPU Implementation for Single Block Orthogonal Dictionary Learning

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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