high performance computing on graphics processing units: hgpu.org

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Power and Performance Studies of the Explicit Multi-Threading (XMT) Architecture

Fuat Keceli

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Tags: Algorithms, ASIC, Benchmarking, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 280, Package, Performance, Thesis

October 23, 2011 by hgpu

Computer Finite-Difference Time-Domain Simulation of Electromagnetic Wave Propagation using GPUs

Anna Bonet Manchado

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Tags: Algorithms, CUDA, Data parallelism, Electrodynamics, FDTD, Finite-difference time-domain, Maxwell's equations, nVidia, nVidia GeForce GTX 280, Tesla C2050, Thesis, Visualization

October 23, 2011 by hgpu

Implementing a Preconditioned Iterative Linear Solver Using Massively Parallel Graphics Processing Units

Amirhassan Asgari Kamiabad

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Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 280, Power Systems Simulation, Thesis

October 22, 2011 by hgpu

Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control

Sergei Morozov, Sudhanshu Mathur

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Tags: CUDA, Finance, Fortran, Machine learning, nVidia, nVidia GeForce GTX 280, Package

October 21, 2011 by hgpu

Experimental B+-tree for GPU

Krzysztof Kaczmarski

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 280, Sorting

October 20, 2011 by hgpu

Characterization of FPGA-based High Performance Computers

Karl Savio Pimenta Pereira

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Tags: Algorithms, Benchmarking, Chemistry, Computer science, CUDA, DSP, FFT, FPGA, Molecular dynamics, nVidia, nVidia GeForce GTX 280, OpenCL, Tesla C2050, Thesis

October 18, 2011 by hgpu

Experience Applying Fortran GPU Compilers to Numerical Weather Prediction

T. Henderson, J. Middlecoff, J. Rosinski, M. Govett, P. Madden

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Tags: CUDA, Earth and Space Sciences, Fortran, nVidia, nVidia GeForce GTX 280, Tesla C2050, Weather prediction

October 18, 2011 by hgpu

An Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU

Andrew Davidson, Yao Zhang, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia GeForce GTX 470

October 16, 2011 by hgpu

GPU-to-CPU callbacks

Jeff A. Stuart, Michael Cox, John D. Owens

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

October 16, 2011 by hgpu

Data Layout Pruning on GPU

Xinbiao Gan, Zhiying Wang, Li Shen, Qi Zhu

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

October 16, 2011 by hgpu

GPU-Based Translation-Invariant 2D Discrete Wavelet Transform for Image Processing

Dietmar Wippig, Bernd Klauer

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Tags: ATI, ATI Radeon HD 4870, Discrete Wavelet Transform, GLSL, Image processing, Java, nVidia, nVidia GeForce GTX 280, OpenGL, Shaders

October 12, 2011 by hgpu

Anti-parallel Patterns in Fine-grain Data-parallel Programs

Jan Cornelis

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Tags: Benchmarking, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce 8500 GT, nVidia GeForce GTX 280, Performance, Thesis

October 10, 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

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high performance computing on graphics processing units: hgpu.org

Power and Performance Studies of the Explicit Multi-Threading (XMT) Architecture

Computer Finite-Difference Time-Domain Simulation of Electromagnetic Wave Propagation using GPUs

Implementing a Preconditioned Iterative Linear Solver Using Massively Parallel Graphics Processing Units

Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control

Experimental B+-tree for GPU

Characterization of FPGA-based High Performance Computers

Experience Applying Fortran GPU Compilers to Numerical Weather Prediction

An Auto-tuned Method for Solving Large Tridiagonal Systems on the GPU

GPU-to-CPU callbacks

Data Layout Pruning on GPU

GPU-Based Translation-Invariant 2D Discrete Wavelet Transform for Image Processing

Anti-parallel Patterns in Fine-grain Data-parallel Programs

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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