high performance computing on graphics processing units: hgpu.org

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Efficient Parallelization of Natural Language Applications using GPUs

Chao-Yue Lai

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Optimization, Thesis

May 11, 2012 by hgpu

Enhancing data parallelism for Ant Colony Optimization on GPUs

Jose M. Cecilia, Jose M. Garcia, Andy Nisbet, Martyn Amos, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, Optimization, Tesla C2050

May 10, 2012 by hgpu

Exploration of Optimization Options for Increasing Performance of a GPU Implementation of a Three-Dimensional Bilateral Filter

E. Wes Bethel

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Tags: Algorithms, CUDA, Filtering, Image processing, nVidia, nVidia GeForce GTX 280, Optimization, Performance

May 9, 2012 by hgpu

Parallelization of calculations using GPU in optimization approach for macromodels construction

Petro Stakhiv, Iryna Strubytska, Yuriy Kozak

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Tags: CUDA, Mathematics, nVidia, nVidia GeForce GTS 250, Optimization

May 7, 2012 by hgpu

Implementation and Optimization of Image Processing Algorithms on Embedded GPU

Nitin Singhal, Jin Woo Yoo, Ho Yeol Choi, In Kyu Park

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Tags: Algorithms, ARM, Computer vision, Image processing, OpenGL, Optimization, Rendering

May 7, 2012 by hgpu

Effects of Compiler Optimizations in OpenMP to CUDA Translation

Amit Sabne, Putt Sakdhnagool, Rudolf Eigenmann

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia Quadro FX 5600, OpenMP, Optimization

May 6, 2012 by hgpu

One Stone Two Birds: Synchronization Relaxation and Redundancy Removal in GPU-CPU Translation

Ziyu Guo, Bo Wu, Xipeng Shen

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Pattern recognition

May 6, 2012 by hgpu

Random Walks based Multi-Image Segmentation: Quasiconvexity Results and GPU-based Solutions

Maxwell D. Collins, Jia Xu, Leo Grady, Vikas Singh

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Tags: Algorithms, CUDA, Image processing, Linear Algebra, nVidia, nVidia GeForce GTX 470, Optimization

April 28, 2012 by hgpu

Characterizing and Improving the Use of Demand-Fetched Caches in GPUs

Wenhao Jia, Kelly A. Shaw, Margaret Martonosi

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Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, Optimization, Performance, Tesla C2070

April 28, 2012 by hgpu

Lynx: A Dynamic Instrumentation System for Data-Parallel Applications on GPGPU Architectures

Naila Farooqui, Andrew Kerr, Greg Eisenhauer, Karsten Schwan, Sudhakar Yalamanchili

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Source codes

Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Optimization, Package, Tesla C2050

April 25, 2012 by hgpu

Parboil: A Revised Benchmark Suite for Scientific and Commercial Throughput Computing

John A. Stratton, Christopher Rodrigues, I-Jui Sung, Nady Obeid, Li-Wen Chang, Nasser Anssari, Geng Daniel Liu, Wen-mei W. Hwu

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Source codes

Tags: Algorithms, Benchmarking, Computer science, CUDA, Molecular simulation, MPI, nVidia, nVidia GeForce GTX 480, Optimization, Package

April 25, 2012 by hgpu

Visualization of Pareto Solutions by Spherical Self-Organizing Map and It’s acceleration on a GPU

Masato Yoshimi, Takuya Kuhara, Kaname Nishimoto, Mitsunori Miki, Tomoyuki Hiroyasu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8400 GS, nVidia GeForce GTX 280, Optimization, Self-organizing map, Tesla C1060, Visualization

April 5, 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

Efficient Parallelization of Natural Language Applications using GPUs

Enhancing data parallelism for Ant Colony Optimization on GPUs

Exploration of Optimization Options for Increasing Performance of a GPU Implementation of a Three-Dimensional Bilateral Filter

Parallelization of calculations using GPU in optimization approach for macromodels construction

Implementation and Optimization of Image Processing Algorithms on Embedded GPU

Effects of Compiler Optimizations in OpenMP to CUDA Translation

One Stone Two Birds: Synchronization Relaxation and Redundancy Removal in GPU-CPU Translation

Random Walks based Multi-Image Segmentation: Quasiconvexity Results and GPU-based Solutions

Characterizing and Improving the Use of Demand-Fetched Caches in GPUs

Lynx: A Dynamic Instrumentation System for Data-Parallel Applications on GPGPU Architectures

Parboil: A Revised Benchmark Suite for Scientific and Commercial Throughput Computing

Visualization of Pareto Solutions by Spherical Self-Organizing Map and It’s acceleration on a GPU

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)