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Triangular matrix inversion on Graphics Processing Unit

Florian Ries, Tommaso De Marco, Matteo Zivieri, Roberto Guerrieri

Tags: Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295

November 26, 2010 by hgpu

CFD-based analysis and two-level aerodynamic optimization on Graphics Processing Units

I. C. Kampolis, X. S. Trompoukis, V. G. Asouti, K. C. Giannakoglou

Tags: CUDA, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 285

November 26, 2010 by hgpu

Accelerating leukocyte tracking using CUDA: A case study in leveraging manycore coprocessors

M. Boyer, D. Tarjan, S. T. Acton, K. Skadron

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Tags: Biology, CUDA, Microscopy, nVidia, nVidia GeForce GTX 280

November 26, 2010 by hgpu

On the energy efficiency of graphics processing units for scientific computing

S. Huang, S. Xiao, W. Feng

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280

November 26, 2010 by hgpu

LBM based flow simulation using GPU computing processor

Frederic Kuznik, Christian Obrecht, Gilles Rusaouen, Jean-Jacques Roux

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Tags: CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 280

November 25, 2010 by hgpu

Towards real-time radiation therapy: GPU accelerated superposition/convolution

Robert Jacques, Russell Taylor, John Wong, Todd Mcnutt

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Tags: CUDA, Medicine, nVidia, nVidia GeForce GTX 280, Radiotherapy

November 25, 2010 by hgpu

Granular visibility queries on the GPU

Thomas Engelhardt, Carsten Dachsbacher

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Tags: 3D Graphics and Realism, ATI, ATI Radeon HD 3870, Computer science, nVidia, nVidia GeForce GTX 280, OpenGL, Rendering

November 25, 2010 by hgpu

Performance modeling and automatic ghost zone optimization for iterative stencil loops on GPUs

Jiayuan Meng, Kevin Skadron

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Programming techniques

November 23, 2010 by hgpu

Many-core algorithms for statistical phylogenetics

Marc A. Suchard, Andrew Rambaut

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 280

November 23, 2010 by hgpu

Efficient Probabilistic Model Checking on General Purpose Graphics Processors

Dragan Bosnacki, Stefan Edelkamp, Damian Sulewski

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Probabilistic model checking

November 23, 2010 by hgpu

Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images

Ramtin Shams, Parastoo Sadeghi, Rodney Kennedy, Richard Hartley

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Tags: CUDA, Image processing, Image registration, Medicine, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280

November 23, 2010 by hgpu

Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures

Mikhail Smelyanskiy, David Holmes, Jatin Chhugani, Alan Larson, Douglas M. Carmean, Dennis Hanson, Pradeep Dubey, Kurt Augustine, Daehyun Kim, Alan Kyker, Victor W. Lee, Anthony D. Nguyen, Larry Seiler, Richard Robb

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Tags: Computed tomography, CT, CUDA, Image processing, Larrabee, Medicine, nVidia, nVidia GeForce GTX 280, Rendering

November 23, 2010 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 plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

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

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