high performance computing on graphics processing units: hgpu.org

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Faster GPU-based convolutional gridding via thread coarsening

Bruce Merry

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Tags: Algorithms, AMD Radeon R9 290X, Astrophysics, ATI, Computer science, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 980, OpenCL

May 26, 2016 by hgpu

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

Zhe Zhou, Wenrui Diao, Xiangyu Liu, Zhou Li, Kehuan Zhang, Rui Liu

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Tags: AMD Radeon R7 250, ATI, Cloud, Computer science, nVidia, nVidia GeForce GTX 750, OpenCL, Operating systems, Security

May 26, 2016 by hgpu

ImageCL: An Image Processing Language for Performance Portability on Heterogeneous Systems

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Computer science, Image processing, nVidia, nVidia GeForce GTX 960, OpenCL, Performance, performance portability, Programming Languages, Tesla K40

May 23, 2016 by hgpu

A Practical Performance Model for Compute and Memory Bound GPU Kernels

Elias Konstantinidis, Yiannis Cotronis

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Tags: Benchmarking, CUDA, OpenCL, Performance prediction

May 23, 2016 by ekondis

The Hitchhiker’s Guide to Cross-Platform OpenCL Application Development

Tyler Sorensen, Alastair F. Donaldson

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Tags: AMD Radeon R7 260, AMD Radeon R9 290X, ATI, Benchmarking, Computer science, Intel HD 5500, nVidia, nVidia GeForce GTX 980, nVidia Quadro K500, OpenCL

May 21, 2016 by hgpu

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

Ebad Salehi

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Tags: Compression, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Tesla K20, Thesis

May 21, 2016 by hgpu

GPU-Accelerated Feature Tracking

Alex Graves

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 780, OpenCL

May 17, 2016 by hgpu

DeepLearningKit – an GPU Optimized Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

Amund Tveit, Torbjorn Morland, Thomas Brox Rost

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Tags: Computer science, Deep learning, Neural networks, OpenCL, Package, Video, Vulkan

May 17, 2016 by hgpu

A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems

Oren Segal, Nasibeh Nasiri, Martin Margala

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Tags: Benchmarking, Computer science, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K40

May 17, 2016 by hgpu

Improving GPU Performance: Reducing Memory Conflicts and Latency

Gert-Jan van den Braak

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Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, Matrix multiplication, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Thesis

May 11, 2016 by hgpu

Microbenchmarks for GPU characteristics: the occupancy roofline and the pipeline model

Jan Lemeire, Jan G. Cornelis, Laurent Segers

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Tags: Benchmarking, Computer science, Java, nVidia, nVidia GeForce GT 640, nVidia Quadro K620, OpenCL, Package

May 9, 2016 by hgpu

Parallel Wavelet Schemes for Images

David Barina, Michal Kula, Pavel Zemcik

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Tags: AMD Radeon R9 290X, AMD Radeon R9 Fury, ATI, Compression, Discrete Wavelet Transform, Image processing, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan X, OpenCL, Package

May 7, 2016 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

Faster GPU-based convolutional gridding via thread coarsening

ImageCL: An Image Processing Language for Performance Portability on Heterogeneous Systems

A Practical Performance Model for Compute and Memory Bound GPU Kernels

The Hitchhiker’s Guide to Cross-Platform OpenCL Application Development

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

GPU-Accelerated Feature Tracking

DeepLearningKit – an GPU Optimized Deep Learning Framework for Apple’s iOS, OS X and tvOS developed in Metal and Swift

A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems

Improving GPU Performance: Reducing Memory Conflicts and Latency

Microbenchmarks for GPU characteristics: the occupancy roofline and the pipeline model

Parallel Wavelet Schemes for Images

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)