high performance computing on graphics processing units: hgpu.org

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Graphics Processing Units in Acceleration of Bandwidth Selection for Kernel Density Estimation

Witold Andrzejewski, Artur Gramacki, Jaroslaw Gramacki

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Tags: Algorithms, CUDA, nVidia, nVidia GeForce GTX 480, Package, Probability, Statistics

December 24, 2013 by hgpu

GPU Acceleration of Melody Accurate Matching in Query-by-Humming

Liming Xiao, Yao Zheng, Wenqi Tang, Guangchao Yao, Li Ruan

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480

December 23, 2013 by hgpu

A Data-Driven Model for Anisotropic Heterogeneous Subsurface Scattering

Ying Song, Wencheng Wang

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Tags: 3D Graphics and Realism, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX 480

December 19, 2013 by hgpu

A comparative analysis of the performance and deployment overhead of parallelized Finite Difference Time Domain (FDTD) algorithms on a selection of high performance multiprocessor computing systems

Robert Georg Ilgner

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Tags: Algorithms, CUDA, Electrodynamics, FDTD, Finite difference, Finite-difference time-domain, nVidia, nVidia GeForce GTX 480, Tesla C2070, Thesis

December 18, 2013 by hgpu

Development methodologies for GPU and cluster of GPUs

Sylvain Contassot-Vivier, Stephane Vialle, Jens Gustedt

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580

December 17, 2013 by hgpu

OpenCL Accelerated Multi-GPU Cone-Beam Reconstruction

Marc Kaseberg, Steffen Melnik, Erwin Keeve

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Tags: Algorithms, ATI, ATI Radeon HD 5770, ATI Radeon HD 6970, Computed tomography, CT, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 680, nVidia Quadro 600, OpenCL, Tomography

December 17, 2013 by hgpu

Data Transfer Matters for GPU Computing

Yusuke Fujii, Takuya Azumi, Nobuhiko Nishio, Shinpei Kato, Masato Edahiro

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Package, Performance

December 13, 2013 by hgpu

TuCCompi: A Multi-Layer Programing Model for Heterogeneous Systems with Auto-Tuning Capabilities

Hector Ortega-Arranz, Yuri Torres, Diego R. Llanos, Arturo Gonzalez-Escribano

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, OpenMP

December 13, 2013 by hgpu

Multilayered Abstractions for Partial Differential Equations

Graham Robert Markall

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Code generation, CUDA, Differential equations, FEM, Finite element method, Mathematics, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Partial differential equations, PDEs, Software Engineering, Thesis

December 11, 2013 by hgpu

A New Software Based GPU Framework

Evgeny Miretsky

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Thesis

December 11, 2013 by hgpu

Evaluating the Performance and Energy Efficiency of N-Body Codes on Multi-Core CPUs and GPUs

Ivan Zecena, Martin Burtscher, Tongdan Jin, Ziliang Zong

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Tags: Computer science, CUDA, N-body simulation, nVidia, nVidia GeForce GTX 480, Performance

November 27, 2013 by hgpu

Diagrammatic Determinantal Quantum Monte Carlo Calculations on GPUs

Markus Schmitt

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Tags: Algorithms, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 480, Physics, QMC, Tesla C2050, Thesis

November 25, 2013 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Graphics Processing Units in Acceleration of Bandwidth Selection for Kernel Density Estimation

GPU Acceleration of Melody Accurate Matching in Query-by-Humming

A Data-Driven Model for Anisotropic Heterogeneous Subsurface Scattering

A comparative analysis of the performance and deployment overhead of parallelized Finite Difference Time Domain (FDTD) algorithms on a selection of high performance multiprocessor computing systems

Development methodologies for GPU and cluster of GPUs

OpenCL Accelerated Multi-GPU Cone-Beam Reconstruction

Data Transfer Matters for GPU Computing

TuCCompi: A Multi-Layer Programing Model for Heterogeneous Systems with Auto-Tuning Capabilities

Multilayered Abstractions for Partial Differential Equations

A New Software Based GPU Framework

Evaluating the Performance and Energy Efficiency of N-Body Codes on Multi-Core CPUs and GPUs

Diagrammatic Determinantal Quantum Monte Carlo Calculations on GPUs

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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