high performance computing on graphics processing units: hgpu.org

Packages

hgpu.org » Applications

GPU accelerated population annealing algorithm

Lev Yu. Barash, Martin Weigel, Michal Borovsky, Wolfhard Janke, Lev N. Shchur

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Tags: Algorithms, CUDA, Ising model, Monte Carlo simulation, nVidia, Package, Physics, Tesla K80

March 14, 2017 by hgpu

Massive Exploration of Neural Machine Translation Architectures

Denny Britz, Anna Goldie, Thang Luong, Quoc Le

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Tags: Computer science, Deep learning, Neural networks, NLP, nVidia, Package, Python, TensorFlow, Tesla K40

March 14, 2017 by hgpu

Model-independent partial wave analysis using a massively-parallel fitting framework

Liang Sun, Rafael Aoude, Alberto Correa Dos Reis, Michael David Sokoloff

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Tags: Algorithms, CUDA, High Energy Physics - Experiment, nVidia, OpenMP, Package, Physics, Tesla K40

March 14, 2017 by hgpu

Optimizing Deep CNN-Based Queries over Video Streams at Scale

Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia

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Tags: Computer science, Computer vision, Databases, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1, Package, TensorFlow, Tesla P100

March 9, 2017 by hgpu

Improving the Neural GPU Architecture for Algorithm Learning

Karlis Freivalds, Renars Liepins

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, Memory model, nVidia, Package, Python, TensorFlow, Tesla K40

March 5, 2017 by hgpu

Billion-scale similarity search with GPUs

Jeff Johnson, Matthijs Douze, Herve Jegou

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Tags: Computer science, CUDA, Data Structures and Algorithms, Databases, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Package

March 5, 2017 by hgpu

Speckle Reduction with Trained Nonlinear Diffusion Filtering

Wensen Feng, Yunjin Chen

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Tags: CNN, Computer vision, Filtering, Image processing, Matlab, nVidia, nVidia GeForce GXT 780 Ti, Package, SAR

February 28, 2017 by hgpu

liquidSVM: A Fast and Versatile SVM package

Ingo Steinwart, Philipp Thomann

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Tags: Computer science, CUDA, Java, nVidia, Package, Python, Spark

February 26, 2017 by hgpu

Dynamic Buffer Overflow Detection for GPGPUs

Christopher Erb, Mike Collins, Joseph L. Greathouse

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Tags: AMD FirePro W9100, ATI, Benchmarking, Computer science, OpenCL, Package, Security

February 22, 2017 by hgpu

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

A. A. Alves Jr, M. D. Sokoloff

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Tags: Computational Physics, CUDA, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Intel Xeon Phi, Monte Carlo simulation, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan Z, nVidia Quadro K 2200, OpenMP, Package, Physics, Tesla K40

February 22, 2017 by hgpu

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

Mathias Bourgoin, Emmanuel Chailloux, Anastasios Doumoulakis

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, Performance

February 18, 2017 by hgpu

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

Shaohuai Shi, Pengfei Xu, Xiaowen Chu

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Tags: Algorithms, BLAS, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Performance

February 14, 2017 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

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high performance computing on graphics processing units: hgpu.org

Packages

GPU accelerated population annealing algorithm

Massive Exploration of Neural Machine Translation Architectures

Model-independent partial wave analysis using a massively-parallel fitting framework

Optimizing Deep CNN-Based Queries over Video Streams at Scale

Improving the Neural GPU Architecture for Algorithm Learning

Billion-scale similarity search with GPUs

Speckle Reduction with Trained Nonlinear Diffusion Filtering

liquidSVM: A Fast and Versatile SVM package

Dynamic Buffer Overflow Detection for GPGPUs

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

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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