high performance computing on graphics processing units: hgpu.org

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Seismic Attributes Extraction Based on GPU

Zhang Guang-Zhi, Chen Lei, Ye Duan-Nan

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Tags: CUDA, Earth and Space Sciences, nVidia, Tesla C1060

February 12, 2013 by hgpu

Hardware Accelerated Molecular Docking: A Survey

Imre Pechan, Bela Feher

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Tags: Bioinformatics, Biology, CUDA, FPGA, Molecular simulation, nVidia, Tesla C1060

February 6, 2013 by hgpu

Programmability and Performance Portability Aspects of Heterogeneous Multi-/Manycore Systems

Christoph Kessler, Usman Dastgeer, Samuel Thibault, Raymond Namyst, Andrew Richards, Uwe Dolinsky, Siegfried Benkner, Jesper Larsson Traff, Sabri Pllana

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance, Tesla C1060, Tesla C2050

January 24, 2013 by hgpu

The PEPPHER Composition Tool: Performance-Aware Dynamic Composition of Applications for GPU-based Systems

Usman Dastgeer, Lu Li, Christoph Kessler

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C1060, Tesla C2050

January 22, 2013 by hgpu

Parallel one-versus-rest SVM training on the GPU

Sander Dieleman, Aaron van den Oord, Benjamin Schrauwen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 540 M, nVidia GeForce GTX 560 Ti, Search strategies, Tesla C1060

January 4, 2013 by hgpu

An Automatic Host and Device Memory Allocation Method for OpenMPC

Hiroaki Uchiyama, Tomoaki Tsumura, Hiroshi Matsuo

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 280, OpenMP, Performance, Tesla C1060

December 20, 2012 by hgpu

Acceleration of multivariate analysis techniques in TMVA using GPUs

A. Hoecker, H. McKendrick, J. Therhaag, A. Washbrook

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Tags: Biological Physics, CUDA, Neural networks, nVidia, Physics, Tesla C1060, Tesla C2050

December 16, 2012 by hgpu

Multi-level Parallelism for Incompressible Flow Computations on GPU Clusters

Dana A. Jacobsen, Inanc Senocak

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Tags: Computer science, CUDA, Fluid dynamics, GPU cluster, MPI, nVidia, Tesla C1060, Tesla S1070

December 12, 2012 by hgpu

Simulations of Complex and Microscopic Models of Cardiac Electrophysiology Powered by Multi-GPU Platforms

Bruno Gouvea de Barros, Rafael Sachetto Oliveira, Wagner Meira Jr., Marcelo Lobosco, Rodrigo Weber dos Santos

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Tags: Cluster computing, CUDA, Heterogeneous systems, Medicine, nVidia, ODEs, Ordinary differential equations, Partial differential equations, PDEs, Tesla C1060

December 3, 2012 by hgpu

Developing Extensible Lattice-Boltzmann Simulators for General-Purpose Graphics-Processing Units

Stuart D. C. Walsh, Martin O. Saar

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Tags: Code generation, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, Tesla C1060

December 1, 2012 by hgpu

CUDA-OpenGL Interoperability to Visualize Electromagnetic Fields Calculated by FDTD

Veysel Demir, Atef Z. Elsherbeni

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

December 1, 2012 by hgpu

Evaluation of Two Parallel Finite Element Implementations of the Time-Dependent Advection Diffusion Problem: GPU versus Cluster Considering Time and Energy Consumption

Alberto F. De Souza, Lucas Veronese, Leonardo M. Lima, Claudine Badue, Lucia Catabriga

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Tags: Computer science, CUDA, Finite element method, nVidia, Tesla C1060

November 23, 2012 by hgpu

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Seismic Attributes Extraction Based on GPU

Hardware Accelerated Molecular Docking: A Survey

Programmability and Performance Portability Aspects of Heterogeneous Multi-/Manycore Systems

The PEPPHER Composition Tool: Performance-Aware Dynamic Composition of Applications for GPU-based Systems

Parallel one-versus-rest SVM training on the GPU

An Automatic Host and Device Memory Allocation Method for OpenMPC

Acceleration of multivariate analysis techniques in TMVA using GPUs

Multi-level Parallelism for Incompressible Flow Computations on GPU Clusters

Simulations of Complex and Microscopic Models of Cardiac Electrophysiology Powered by Multi-GPU Platforms

Developing Extensible Lattice-Boltzmann Simulators for General-Purpose Graphics-Processing Units

CUDA-OpenGL Interoperability to Visualize Electromagnetic Fields Calculated by FDTD

Evaluation of Two Parallel Finite Element Implementations of the Time-Dependent Advection Diffusion Problem: GPU versus Cluster Considering Time and Energy Consumption

Recent source codes

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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