high performance computing on graphics processing units: hgpu.org

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Programming finite-difference time-domain for graphics processor units using compute unified device architecture

V. Demir, A.Z. Elsherbeni

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Tags: Algorithms, CUDA, Electrodynamics, FDTD, Finite-difference time-domain, nVidia, Tesla C1060

August 3, 2011 by hgpu

Towards an embedded biologically-inspired machine vision processor

Vinay Sriram, David Cox, Kuen Hung Tsoi, Wayne Luk

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Tags: Algorithms, ASIC, Computer science, Computer vision, FPGA, Image recognition, nVidia, Tesla C1060

August 1, 2011 by hgpu

A parallel accelerator for semantic search

Abhinandan Majumdar, Srihari Cadambi, Srimat T. Chakradhar, Hans Peter Graf

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Databases, FPGA, nVidia, Semantic indexing, Tesla C1060

July 30, 2011 by hgpu

GPU accelerated Monte Carlo simulations of lattice spin models

Martin Weigel, Taras Yavors'kii

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Tags: Computational Physics, CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 480, Package, Physics, Statistical Mechanics, Tesla C1060

July 28, 2011 by hgpu

Computation of electron quantum transport in graphene nanoribbons using GPU

S. Ihnatsenka

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Tags: Computational Physics, CUDA, Mesoscale and Nanoscale Physics, nVidia, nVidia GeForce 8800, Physics, Tesla C1060

July 27, 2011 by hgpu

Accelerating Radio Astronomy Cross-Correlation with Graphics Processing Units

M. A. Clark, P. C. La Plante, L. J. Greenhill

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 480, Tesla C1060

July 22, 2011 by hgpu

Parallelizing the Cellular Potts Model on graphics processing units

Jose J. Tapia, Roshan M. D'Souza

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Tags: Biology, Computational biology, CUDA, nVidia, Tesla C1060

July 22, 2011 by hgpu

Massively parallelized replica-exchange simulations of polymers on GPUs

Jonathan Gross, Wolfhard Janke, Michael Bachmann

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Tags: Biomolecules, Computational Physics, Condensed matter, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Physics, Polymer Systems, Tesla C1060

July 19, 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

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

Real-time, fast radio transient searches with GPU de-dispersion

Alessio Magro, Aris Karastergiou, Stefano Salvini, Benjamin Mort, Fred Dulwich, Kristian Zarb Adami

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Tags: Astrophysics, CUDA, FFT, Instrumentation and Methods for Astrophysics, nVidia, Tesla C1060

July 14, 2011 by hgpu

Option pricing with multi-dimensional quadrature architectures

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

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Tags: Finance, FPGA, nVidia, nVidia GeForce 8600 GT, Performance, Tesla C1060

July 14, 2011 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming finite-difference time-domain for graphics processor units using compute unified device architecture

Towards an embedded biologically-inspired machine vision processor

A parallel accelerator for semantic search

GPU accelerated Monte Carlo simulations of lattice spin models

Computation of electron quantum transport in graphene nanoribbons using GPU

Accelerating Radio Astronomy Cross-Correlation with Graphics Processing Units

Parallelizing the Cellular Potts Model on graphics processing units

Massively parallelized replica-exchange simulations of polymers on GPUs

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

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

Real-time, fast radio transient searches with GPU de-dispersion

Option pricing with multi-dimensional quadrature architectures

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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