high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce GTX 280

Modeling GPU-CPU Workloads and Systems

Andrew Kerr, Gregory Diamos, Sudhakar Yalamanchili

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce 8600 GS, nVidia GeForce GTX 280, Performance, Task scheduling, Tesla C1060

March 23, 2011 by hgpu

GPU-Assisted Computation of Centroidal Voronoi Tessellation

Guodong Rong, Yang Liu, Wenping Wang, Xiaotian Yin, David Gu, Xiaohu Guo

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Tags: Cg, Computer science, nVidia, nVidia GeForce GTX 280, Visualization, Voronoi diagram

March 23, 2011 by hgpu

Dense linear algebra solvers for multicore with GPU accelerators

Stanimire Tomov, Rajib Nath, Hatem Ltaief, Jack Dongarra

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Source codes

Tags: Algorithms, Computer science, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Package

March 23, 2011 by hgpu

Real-time Stochastic Rasterization on Conventional GPU Architectures

M McGuire, E Enderton, P Shirley, D Luebke

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Tags: 3D Graphics and Realism, Algorithms, Computer science, nVidia, nVidia GeForce GTX 280, OpenGL

March 20, 2011 by hgpu

A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

David Barrie Thomas, Lee Howes, Wayne Luk

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 280, Pseudo-random number generators

March 20, 2011 by hgpu

Hardware Acceleration of EDA Algorithms: GPU Architecture and the CUDA Programming Model

Kanupriya Gulati and Sunil P. Khatri

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Tags: Book, CAD, Computer science, CUDA, Electronic design automation, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 280, nVidia Quadro FX 5800, Review

March 18, 2011 by hgpu

Memory-Scalable GPU Spatial Hierarchy Construction

Qiming Hou, Xin Sun, Kun Zhou, Christian Lauterbach, Dinesh Manocha, Baining Guo

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Tags: Computer science, CUDA, KD-tree, Memory, nVidia, nVidia GeForce GTX 280

March 17, 2011 by hgpu

GPU Accelerated Face Detection

Jiangang Kong, Yangdong Deng

Tags: Computer science, Computer vision, Image recognition, nVidia, nVidia GeForce GTX 280

March 14, 2011 by hgpu

Hardware Acceleration of EDA Algorithms: Custom ICs, FPGAs and GPUs

Kanupriya Gulati and Sunil P. Khatri

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Tags: Book, CAD, Computer science, CUDA, Electronic design automation, FPGA, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 280, nVidia Quadro FX 5800, Review

March 14, 2011 by hgpu

Interactive Volume Rendering Aurora on the GPU

Orion Sky Lawlor, Jon Genetti

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Tags: Algorithms, ATI, ATI Radeon HD 4830, Computer science, GLSL, nVidia, nVidia GeForce 8800 M GTS, nVidia GeForce GTX 280, OpenGL, Raytracing, Rendering

March 13, 2011 by hgpu

GPU Computing with Orientation Maps for Extracting Local Invariant Features

Naoyuki Ichimura

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Tags: Computer science, Computer vision, CUDA, Image recognition, nVidia, nVidia GeForce GTX 280, nVidia Quadro FX 4600

March 12, 2011 by hgpu

To GPU Synchronize or Not GPU Synchronize?

Wu-Chun Feng, Shucai Xiao

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

March 12, 2011 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

Modeling GPU-CPU Workloads and Systems

GPU-Assisted Computation of Centroidal Voronoi Tessellation

Dense linear algebra solvers for multicore with GPU accelerators

Real-time Stochastic Rasterization on Conventional GPU Architectures

A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

Hardware Acceleration of EDA Algorithms: GPU Architecture and the CUDA Programming Model

Memory-Scalable GPU Spatial Hierarchy Construction

GPU Accelerated Face Detection

Hardware Acceleration of EDA Algorithms: Custom ICs, FPGAs and GPUs

Interactive Volume Rendering Aurora on the GPU

GPU Computing with Orientation Maps for Extracting Local Invariant Features

To GPU Synchronize or Not GPU Synchronize?

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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