high performance computing on graphics processing units: hgpu.org

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The Comparisons of OpenCL and OpenMP Computing Paradigm

Slo-Li Chu, Chih-Chieh Hsiao

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Tags: ATI, ATI Radeon HD 5850, Benchmarking, Computer science, nVidia GeForce GTX 285, OpenCL, OpenMP, Performance

December 9, 2013 by hgpu

Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method

Michal Januszewski, Marcin Kostur

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Tags: Code generation, Computational Physics, CUDA, Fluid dynamics, Fluid simulation, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 285, OpenCL, Package, Physics, Python, Tesla C1060, Tesla C2050, Tesla K10, Tesla K20

November 12, 2013 by hgpu

Toward Better Computation Models for Modern Machines

Tomasz Jurkiewicz

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Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 285, Thesis

November 10, 2013 by hgpu

Dynamical simulations of extrasolar planetary systems with debris disks using a GPU accelerated N-body code

Alexander Moore

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Tags: Astrophysics, CUDA, N-body simulation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia Quadro FX 5800, Package, Thesis

October 27, 2013 by hgpu

Multi-Scale Scheduling Techniques for Signal Processing Systems

Zheng Zhou

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Tags: Algorithms, CUDA, DSP, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, Particle swarm optimization, Signal processing, Thesis

October 19, 2013 by hgpu

Empirical performance modeling of GPU kernels using active learning

Prasanna Balaprakash, Karl Rupp, Azamat Mametjanov, Robert B. Gramacy, Paul D. Hovland, Stefan M. Wild

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 470, OpenCL, Performance, Tesla C2050, Tesla K20

October 17, 2013 by hgpu

Coupling a Generalized DEM and an SPH Models Under a Heterogeneous Massively Parallel Framework

Ricardo Canelas, Jose M. Dominguez, Rui M. L. Ferreira

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Tags: CUDA, Fluid dynamics, Heterogeneous systems, MPI, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia GeForce GTX 680

October 12, 2013 by hgpu

Algorithmic GPGPU Memory Optimization

Byunghyun Jang, Minsu Choi, Kyung Ki Kim

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Tags: Algorithms, Computer science, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 285, OpenCL, Performance

September 15, 2013 by hgpu

Strategies for Optimization of Parallel Programs

Alcides Fonseca

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, OpenCL, Optimization, Thesis

August 3, 2013 by hgpu

Methods for Optimizing OpenCL Applications on Heterogeneous Multicore Architectures

Slo-Li Chu, Chih-Chieh Hsiao

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Tags: ATI, ATI Radeon HD 4670, ATI Radeon HD 5850, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GT 220, nVidia GeForce GTX 285, OpenCL, Optimization, Playstation

July 19, 2013 by hgpu

GPU Based Implementation of Recursive Digital Filtering Algorithms

Dong-hwan Lee, Wonyong Sung

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Tags: Algorithms, CUDA, Filtering, nVidia, nVidia GeForce GTX 285, Signal processing

July 15, 2013 by hgpu

Meshfree/GFEM in hardware-efficiency prospective

Rong Tian

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Partial differential equations, PDEs

July 10, 2013 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

The Comparisons of OpenCL and OpenMP Computing Paradigm

Sailfish: a flexible multi-GPU implementation of the lattice Boltzmann method

Toward Better Computation Models for Modern Machines

Dynamical simulations of extrasolar planetary systems with debris disks using a GPU accelerated N-body code

Multi-Scale Scheduling Techniques for Signal Processing Systems

Empirical performance modeling of GPU kernels using active learning

Coupling a Generalized DEM and an SPH Models Under a Heterogeneous Massively Parallel Framework

Algorithmic GPGPU Memory Optimization

Strategies for Optimization of Parallel Programs

Methods for Optimizing OpenCL Applications on Heterogeneous Multicore Architectures

GPU Based Implementation of Recursive Digital Filtering Algorithms

Meshfree/GFEM in hardware-efficiency prospective

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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