high performance computing on graphics processing units: hgpu.org

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Towards fully user transparent task and data parallel image processing

Jan Lemeire, Yan Zhao, Peter Schelkens, Steve De Backer, Frans Cornelissen, Bert Torfs

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Tags: Algorithms, CUDA, Image processing, nVidia

July 19, 2011 by hgpu

Tracking humans interacting with the environment using efficient hierarchical sampling and layered observation models

Jan Bandouch, Michael Beetz

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Tags: Computer science, Computer vision, CUDA, nVidia, Video tracking

July 19, 2011 by hgpu

An auto-tuning framework for parallel multicore stencil computations

Shoaib Kamil, Cy Chan, Leonid Oliker, John Shalf, Samuel Williams

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Tags: Code generation, Computer science, CUDA, Fortran, nVidia, nVidia GeForce GTX 280

July 18, 2011 by hgpu

Accelerating Partitional Algorithms for Flow Cytometry on GPUs

Jeremy Espenshade, Andrew Pangborn, Gregor von Laszewski, Douglas Roberts, James S. Cavenaugh

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Tags: Algorithms, Biology, Clustering, CUDA, nVidia, Tesla C870

July 18, 2011 by hgpu

Implementing LNS using filtering units of GPUs

Mark Arnold, Sylvain Collange, David Defour

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Tags: CUDA, Filtering, nVidia, nVidia GeForce GTX 280, Signal processing

July 17, 2011 by hgpu

Dynamic scheduling Monte-Carlo framework for multi-accelerator heterogeneous clusters

Anson H.T. Tse, David B. Thomas, K.H. Tsoi, Wayne Luk

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Monte Carlo simulation, nVidia, Tesla C1060

July 17, 2011 by hgpu

Improving the Performance of the Sparse Matrix Vector Product with GPUs

F. Vazquez, G. Ortega, J.J. Fernandez, E.M. Garzon

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Partial differential equations, PDEs, Sparse matrix

July 17, 2011 by hgpu

Boosting sphere decoding speed through Graphic Processing Units

Muhammad S. Khairy, Christian Mehlfuhrer, Markus Rupp

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Tags: Computer science, CUDA, MIMO, Network communications, nVidia, nVidia GeForce 8400 GS, nVidia GeForce GTX 285, Package

July 17, 2011 by hgpu

Arbitrary dimension Reed-Solomon coding and decoding for extended RAID on GPUs

Matthew L. Curry, Anthony Skjellum, H. Lee Ward, Ron Brightwell

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Tags: Computer science, CUDA, Error recovery, nVidia, nVidia GeForce GTX 260, Security

July 17, 2011 by hgpu

Linpack evaluation on a supercomputer with heterogeneous accelerators

Toshio Endo, Akira Nukada, Satoshi Matsuoka, Naoya Maruyama

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Performance, Tesla S1070

July 17, 2011 by hgpu

hiCUDA: High-Level GPGPU Programming

Tianyi David Han, Tarek S. Abdelrahman

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Tags: Code generation, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8800 GT, Package, Presentation

July 17, 2011 by hgpu

Highly parallel decoding of space-time codes on graphics processing units

Kalyana C. Bollapalli, Yiyue Wu, Kanupriya Gulati, Sunil Khatri, A. Robert Calderbank

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Tags: CUDA, nVidia, nVidia GeForce GTX 280, Signal processing, Tesla C1060, Wireless baseband architecture

July 17, 2011 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

Towards fully user transparent task and data parallel image processing

Tracking humans interacting with the environment using efficient hierarchical sampling and layered observation models

An auto-tuning framework for parallel multicore stencil computations

Accelerating Partitional Algorithms for Flow Cytometry on GPUs

Implementing LNS using filtering units of GPUs

Dynamic scheduling Monte-Carlo framework for multi-accelerator heterogeneous clusters

Improving the Performance of the Sparse Matrix Vector Product with GPUs

Boosting sphere decoding speed through Graphic Processing Units

Arbitrary dimension Reed-Solomon coding and decoding for extended RAID on GPUs

Linpack evaluation on a supercomputer with heterogeneous accelerators

hiCUDA: High-Level GPGPU Programming

Highly parallel decoding of space-time codes on graphics processing units

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)