high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce 8600 GS

Fault Tree Analysis Speed-up with GPU Parallel Computing

Hadi Aghassi, Farrokh Aghassi

View

Tags: Algorithms, Computer science, CUDA, Fault tolerance, Monte Carlo simulation, nVidia, nVidia GeForce 8600 GS

July 1, 2012 by hgpu

Modeling GPU-CPU Workloads and Systems

Andrew Kerr, Gregory Diamos, Sudhakar Yalamanchili

View

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 Accelerated Computation and Visualization of Hexagonal Cellular Automata

Stephane Gobron, Herve Bonafos and Daniel Mestre

View

Tags: Cellular automata, Computer science, Genetic programming, GLSL, nVidia, nVidia GeForce 7600 GT, nVidia GeForce 7900 M GTX, nVidia GeForce 8600 GS, nVidia GeForce 8800 GTX, nVidia Quadro FX 3500, OpenGL, Real-time graphics, Rendering

January 23, 2011 by hgpu

Sinus Endoscopy – Application of Advanced GPU Volume Rendering for Virtual Endoscopy

Arno Kruger, Christoph Kubisch, Bernhard Preim, Gero Strauss

View

Tags: Cg, Medicine, nVidia, nVidia GeForce 8600 GS, OpenGL, Rendering, Visualization

January 2, 2011 by hgpu

Watershed analysis algorithm implementation in CUDA and OpenACC

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

OpenCL Performance Prediction using Architecture-Independent Features

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

PySPH: a Python-based SPH framework

PySPH: a Python-based framework for smoothed particle hydrodynamics

Efficient Interleaved Batch Matrix Solvers for CUDA

Efficient Interleaved Batch Matrix Solvers for CUDA

ArborX: Performance-portable geometric search library

ArborX: A Performance Portable Search Library

FlowSeq: Generative Flow based Sequence-to-Sequence Toolkit

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Compositional Deep Learning in Futhark

Compositional Deep Learning in Futhark

MLPerf Benchmark Suite

Demystifying the MLPerf Benchmark Suite

stdgpu: Efficient STL-like Data Structures on the GPU

stdgpu: Efficient STL-like Data Structures on the GPU

ikra-cpp: C++ Library for Object-oriented Programming with Structure of Arrays Layout

Memory-Efficient Object-Oriented Programming on GPUs

See all packages

* * *

* * *

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

+

HGPU group © 2010-2019 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: