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A Framework for Automated Generation of Specialized Function Variants

Nicholas Chaimov

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Tags: Cloud, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, Optimization, Tesla C2070, Tesla S1070, Thesis

November 6, 2012 by hgpu

Production Level CFD Code Acceleration for Hybrid Many-Core Architectures

Austen C. Duffy, Dana P. Hammond, Eric J. Nielsen

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Tags: CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX 480

November 1, 2012 by hgpu

DL: A data layout transformation system for heterogeneous computing

I-Jui Sung, Geng Daniel Liu, Wen-Mei W. Hwu

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Source codes

Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, Package

November 1, 2012 by hgpu

Efficient Pattern-Based Time Series Classification on GPU

Kai-Wei Chang, Biplab Deka, Wen-Mei W. Hwu, Dan Roth

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480

October 31, 2012 by hgpu

Investigating Host-Device communication in a GPU-based H.264 encoder

Kristoffer Egil Bonarjee

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Tags: CUDA, H.264/AVC, Image processing, nVidia, nVidia GeForce GTX 480, Prefetch, Thesis, Video encoding

October 28, 2012 by hgpu

Stereo Matching using Multi-Resolution Images on CUDA

Sudhakar Sah, Nikhil Jotwani

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Tags: CUDA, FPGA, Image processing, nVidia, nVidia GeForce GTX 480

October 22, 2012 by hgpu

High-Performance Computing Algorithms for Constructing Inverted Files on Emerging Multicore Processors

Zheng Wei

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Information Retrieval, List ranking, nVidia, nVidia GeForce GTX 480, Tesla C1060, Tesla C2050, Thesis

October 16, 2012 by hgpu

Particle Filters on Multi-Core Processors

Mehdi Chitchian, Alexander S. van Amesfoort, Andrea Simonetto, Tamas Keviczky, Henk J. Sips

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Tags: ATI, ATI Radeon HD 6970, Bayesian, Computer science, Computer vision, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL, Particle filtering

October 16, 2012 by hgpu

CUDA Based Multi Objective Parallel Genetic Algorithms: Adapting Evolutionary Algorithms for Document Searches

Jason P. Duran, Sathish AP Kumar

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Text mining

October 15, 2012 by hgpu

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

Ion Lungu, Dana-Mihaela Petrosanu, Alexandru Pirjan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Optimization, Performance

October 14, 2012 by hgpu

.NET High Performance Computing

Hsuan-Hsiu Ou

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 440, nVidia GeForce GTX 480, Task scheduling, Thesis

October 13, 2012 by hgpu

Visualization of large multidimensional data sets by using multi-core CPU, GPU and MPI cluster

Piotr Pawliczek, Witold Dzwinel, David A. Yuen

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Tags: Algorithms, Computer science, CUDA, Data mining, MPI, Multidimensional scaling, nVidia, nVidia GeForce 8500 GT, nVidia GeForce 8800 Ultra, nVidia GeForce 9500 GT, nVidia GeForce 9800 GT, nVidia GeForce GT 330 M, nVidia GeForce GTX 260, nVidia GeForce GTX 460, nVidia GeForce GTX 480, Tesla M2050, Visualization

October 6, 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

* * *

high performance computing on graphics processing units: hgpu.org

Production Level CFD Code Acceleration for Hybrid Many-Core Architectures

DL: A data layout transformation system for heterogeneous computing

Efficient Pattern-Based Time Series Classification on GPU

Investigating Host-Device communication in a GPU-based H.264 encoder

Stereo Matching using Multi-Resolution Images on CUDA

Particle Filters on Multi-Core Processors

CUDA Based Multi Objective Parallel Genetic Algorithms: Adapting Evolutionary Algorithms for Document Searches

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

.NET High Performance Computing

Visualization of large multidimensional data sets by using multi-core CPU, GPU and MPI cluster

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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