high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Accelerating Protein Structure Prediction using Particle Swarm Optimization on GPU

Hamed Khakzad, Yasaman Karami, Seyed Shahriar Arab

View

Download (PDF)

Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Particle swarm optimization, Sequence alignment, Tesla C1060

July 24, 2015 by hgpu

Efficient Convolutional Patch Networks for Scene Understanding

Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler

View

Download (PDF)

Source codes

Tags: CNN, Computer science, Deep learning, Machine learning, OpenCL, Package

July 24, 2015 by hgpu

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

Baoguang Shi, Xiang Bai, Cong Yao

View

Download (PDF)

Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Tesla K40

July 24, 2015 by hgpu

Comparison between GPU and parallel CPU optimizations in viewshed analysis

Tobias Axell, Mattias Friden

View

Download (PDF)

Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 660 M, OpenCL, Thesis

July 24, 2015 by hgpu

Massively Deep Artificial Neural Networks for Handwritten Digit Recognition

Keiron O'Shea

View

Download (PDF)

Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 580

July 24, 2015 by hgpu

On the use of deep Boltzmann machines for road signs classification

Carlo D'Eramo

View

Download (PDF)

Tags: Computer science, Computer vision, CUDA, Image recognition, Machine learning, nVidia, Python, Tesla C1060, Thesis

July 22, 2015 by hgpu

An algebraic parallel treecode in arbitrary dimensions

William B. March, Bo Xiao, Chenhan D. Yu, George Biros

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, GPU cluster, Linear Algebra, MPI, nVidia, Tesla K40

July 22, 2015 by hgpu

Generating Binary Optimal Codes Using Heterogeneous Parallel Computing

Srajan Paliwal, Saurabh Tiwary, Bhaskar Chaudhury, Manish K. Gupta

View

Download (PDF)

Tags: Algorithms, Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K40

July 22, 2015 by hgpu

Parallel Programming in Actor-Based Applications via OpenCL

Paul Harvey, Kristian Hentschel, Joseph Sventek

View

Download (PDF)

Source codes

Tags: AMD Radeon R9 290X, ATI, Computer science, OpenACC, OpenCL, Package, Performance

July 20, 2015 by hgpu

Improving performance portability for GPU-specific OpenCL kernels on multi-core/many-core CPUs by analysis-based transformations

Mei Wen, Da-fei Huang, Chang-qing Xun, Dong Chen

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, OpenCL, Performance

July 20, 2015 by hgpu

Accelerating a Movie Recommender System Using VirtualCL on a Heterogeneous GPU Cluster

Ashish Bhatnagar

View

Download (PDF)

Tags: Algorithms, Computer science, Computer vision, GPU cluster, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 770, OpenCL, Thesis

July 20, 2015 by hgpu

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

Mark James Abraham, Teemu Murtola, Roland Schulz, Szilard Pall, Jeremy C. Smith, Berk Hess, Erik Lindahl

View

Download (PDF)

Source codes

Tags: Algorithms, Biomolecules, Chemistry, CUDA, Heterogeneous systems, Molecular simulation, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Tesla K40

July 20, 2015 by hgpu

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloads

An MLIR-based toolchain for AMD AI Engine-enabled devices

Seamless acceleration of Fortran intrinsics via AMD AI engines

Forecasting time series with constraints

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

Accelerating Protein Structure Prediction using Particle Swarm Optimization on GPU

Efficient Convolutional Patch Networks for Scene Understanding

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

Comparison between GPU and parallel CPU optimizations in viewshed analysis

Massively Deep Artificial Neural Networks for Handwritten Digit Recognition

On the use of deep Boltzmann machines for road signs classification

An algebraic parallel treecode in arbitrary dimensions

Generating Binary Optimal Codes Using Heterogeneous Parallel Computing

Parallel Programming in Actor-Based Applications via OpenCL

Improving performance portability for GPU-specific OpenCL kernels on multi-core/many-core CPUs by analysis-based transformations

Accelerating a Movie Recommender System Using VirtualCL on a Heterogeneous GPU Cluster

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

Recent source codes

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

pyATF: The Auto-Tuning Framework (ATF) in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Checkpoint/Restore tool

An MLIR-based toolchain for AMD AI Engine-enabled devices

Forecasting time series with constraints

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Most viewed papers (last 30 days)