high performance computing on graphics processing units: hgpu.org

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Accelerating an imaging spectroscopy algorithm for submerged marine environments using heterogeneous computing

Matthew Sellitto

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Tags: Algorithms, Heterogeneous systems, Image processing, nVidia, OpenCL, Optimization, Sensing, Tesla M2070, Thesis

May 30, 2012 by hgpu

Time-dependent density-functional theory in massively parallel computer architectures: the OCTOPUS project

Xavier Andrade, Joseba Alberdi-Rodriguez, David A. Strubbe, Micael J. T. Oliveira, Fernando Nogueira, Alberto Castro, Javier Muguerza, Agustin Arruabarrena, Steven G. Louie, Alan Aspuru-Guzik, Angel Rubio, Miguel A. L. Marques

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Source codes

Tags: ATI, ATI Radeon HD 5870, Data parallelism, Density-functional theory, nVidia, nVidia GeForce GTX 480, OpenCL, Optimization, Package, Physics, Tesla M2090

May 29, 2012 by hgpu

Parameterized Verification of GPU Kernel Programs

Guodong Li, Ganesh Gopalakrishnan

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Tags: Computer science, CUDA, nVidia, Optimization

May 26, 2012 by hgpu

Parallel Parametric Optimisation with Firefly Algorithms on Graphical Processing Units

A.V. Husselmann, K.A. Hawick

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 590, Optimization

May 26, 2012 by hgpu

Effective Sparse Matrix Representation for the GPU Architectures

B. Neelima, Prakash S. Raghavendra

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 525 M, Optimization, Sparse matrix

May 25, 2012 by hgpu

Compiler optimizations for directive-based programming for accelerators

Matias Piispanen

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, OpenCL, Optimization

May 24, 2012 by hgpu

GMProf: A Low-Overhead, Fine-Grained Profiling Approach for GPU Programs

Mai Zheng, Vignesh T. Ravi, Wenjing Ma, Feng Qin, Gagan Agrawal

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Tags: Computer science, CUDA, nVidia, Optimization, Performance, Tesla C1060

May 23, 2012 by hgpu

Molecular Distance Geometry Optimization Using Geometric Build-up and Evolutionary Techniques on GPU

Levente Fabry-Asztalos, Istvan Lorentz, Razvan Andonie

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Tags: Chemistry, Molecular modeling, nVidia, nVidia GeForce GTX 560, OpenCL, Optimization

May 23, 2012 by hgpu

An MPI-CUDA Implementation and Optimization for Parallel Sparse Equations and Least Squares (LSQR)

He Huang, Liqiang Wang, En-Jui Lee, Po Chen

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Tags: Computer science, CUBLAS, CUDA, Earth and Space Sciences, Geoscience, MPI, nVidia, Optimization, Tesla C2050, Tesla M2070

May 19, 2012 by hgpu

Heterogeneous Computing in Economics: a Simplified Approach

Matt P. Dziubinski, Stefano Grassi

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Tags: CUDA, Data parallelism, Finance, Heterogeneous systems, nVidia, nVidia GeForce GT 520 M, nVidia GeForce GTS 450, Optimization

May 14, 2012 by hgpu

Multi-dimensional characterization of electrostatic surface potential computation on graphics processors

Mayank Daga, Wu-chun Feng

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Tags: Algorithms, Biology, Biomolecules, Computational Complexity, CUDA, Electrostatics, Molecular modeling, nVidia, Optimization, Tesla C1060, Tesla C2050

May 12, 2012 by hgpu

Enhancing GPU Parallelism in Nature-Inspired Algorithms

Jose M. Cecilia, Andy Nisbet, Martyn Amos, Jose M. Garcia, Manuel Ujaldon

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Tags: Algorithms, Computational modelling, Computer science, CUDA, Data parallelism, nVidia, Optimization, P systems, Tesla C1060, Tesla C2050

May 12, 2012 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

Accelerating an imaging spectroscopy algorithm for submerged marine environments using heterogeneous computing

Time-dependent density-functional theory in massively parallel computer architectures: the OCTOPUS project

Parameterized Verification of GPU Kernel Programs

Parallel Parametric Optimisation with Firefly Algorithms on Graphical Processing Units

Effective Sparse Matrix Representation for the GPU Architectures

Compiler optimizations for directive-based programming for accelerators

GMProf: A Low-Overhead, Fine-Grained Profiling Approach for GPU Programs

Molecular Distance Geometry Optimization Using Geometric Build-up and Evolutionary Techniques on GPU

An MPI-CUDA Implementation and Optimization for Parallel Sparse Equations and Least Squares (LSQR)

Heterogeneous Computing in Economics: a Simplified Approach

Multi-dimensional characterization of electrostatic surface potential computation on graphics processors

Enhancing GPU Parallelism in Nature-Inspired Algorithms

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)