high performance computing on graphics processing units: hgpu.org

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QUDA programming for staggered quarks

Steven Gottlieb, Guochun Shi, Aaron Torok, Volodymyr Kindratenko

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Tags: CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Package, Physics, QCD, Tesla C1060, Tesla C2050, Tesla S1070

February 4, 2011 by hgpu

On testing GPU memory for hard and soft errors

Guochun Shi, Jeremy Enos, Michael Showerman, Volodymyr Kindratenko

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Tags: Computer science, CUDA, Hardware Architecture, Memory, nVidia, Package, Tesla C1060, Tesla S1070

February 3, 2011 by hgpu

The Saga of Landau-Gauge Propagators: Gathering New Ammo

Attilio Cucchieri, Tereza Mendes

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Tags: GPU cluster, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, Physics, Tesla S1070

January 27, 2011 by hgpu

Multi-GPU Implementation for Iterative MR Image Reconstruction with Field Correction

Y. Zhuo, X-L. Wu, J. P. Haldar, W-M. W. Hwu, Z-P. Liang, and B. P. Sutton

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Tags: CUDA, Image processing, Image reconstruction, MRI, nVidia, Tesla S1070

January 24, 2011 by hgpu

Fluid-solid coupling on a cluster of GPU graphics cards for seismic wave propagation

Dimitri Komatitsch

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Tags: CUDA, Earth and Space Sciences, Finite element method, Geoscience, nVidia, Package, Seismic modeling, Seismology, Tesla S1070

January 16, 2011 by hgpu

Accelerating Euler Equations Numerical Solver on Graphics Processing Units

Pierre Kestener, Frederic Chateau, Romain Teyssier

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Tags: CUDA, Mathematics, Numerical simulation, nVidia, nVidia GeForce 8800 GTX, Tesla S1070

January 2, 2011 by hgpu

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

Veerendra Allada, Troy Benjegerdes, Brett Bode

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Presentation, Tesla S1070, Tesla T10

December 27, 2010 by hgpu

Singular value decomposition on GPU using CUDA

Sheetal Lahabar, P. J. Narayanan

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Tags: Computer science, Linear Algebra, nVidia, nVidia GeForce GTX 280, Tesla S1070

December 24, 2010 by hgpu

GPU-based ultra fast IMRT plan optimization

Chunhua Men, Xuejun Gu, Dongju Choi, Amitava Majumdar, Ziyi Zheng, Klaus Mueller, Steve B. Jiang

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Tags: CUDA, Image processing, Magnetic resonance imaging, Medicine, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla C1060, Tesla S1070

December 21, 2010 by hgpu

GPU clusters for high-performance computing

Volodymyr V. Kindratenko, Jeremy J. Enos, Guochun Shi, Michael T. Showerman, Galen W. Arnold, John E. Stone, James C. Phillips, Wen-mei Hwu

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Tags: Computer science, CUDA, GPU cluster, nVidia, Tesla S1070

December 18, 2010 by hgpu

High-throughput Bayesian network learning using heterogeneous multicore computers

Michael D. Linderman, Robert Bruggner, Vivek Athalye, Teresa H. Meng, Narges B. Asadi, Garry P. Nolan

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Tags: Algorithms, CUDA, GPU cluster, Mathematics, nVidia, nVidia GeForce GTX 285, Statistics, Tesla C1060, Tesla S1070

December 15, 2010 by hgpu

Accelerating a three-dimensional finite-difference wave propagation code using GPU graphics cards

David Michea, Dimitri Komatitsch

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Tags: CUDA, Earth and Space Sciences, FDTD, Finite difference, Geoscience, GPU cluster, MPI, nVidia, nVidia GeForce 8800 GTX, Tesla S1070

December 15, 2010 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

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high performance computing on graphics processing units: hgpu.org

QUDA programming for staggered quarks

On testing GPU memory for hard and soft errors

The Saga of Landau-Gauge Propagators: Gathering New Ammo

Multi-GPU Implementation for Iterative MR Image Reconstruction with Field Correction

Fluid-solid coupling on a cluster of GPU graphics cards for seismic wave propagation

Accelerating Euler Equations Numerical Solver on Graphics Processing Units

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

Singular value decomposition on GPU using CUDA

GPU-based ultra fast IMRT plan optimization

GPU clusters for high-performance computing

High-throughput Bayesian network learning using heterogeneous multicore computers

Accelerating a three-dimensional finite-difference wave propagation code using GPU graphics cards

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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