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Exploring Multiple Dimensions of Parallelism in Junction Tree Message Passing

Lu Zheng, Ole J. Mengshoel

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Tags: Artificial intelligence, Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 460

September 13, 2013 by hgpu

Coalition Structure Generation with the Graphic Processor Unit

Krzysztof Pawlowski, Karol Kurach, Tomasz Michalak, Talal Rahwan

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce GTX 660

July 8, 2013 by hgpu

libCudaOptimize: an Open Source Library of GPU-based Metaheuristics

Youssef S.G. Nashed, Roberto Ugolotti, Pablo Mesejo, Stefano Cagnoni

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Tags: Artificial intelligence, Computer science, CUDA, Differential evolution, Evolutionary Computations, Metaheuristics, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTS 450, Optimization, Package, Particle swarm optimization

June 21, 2013 by hgpu

A Massively Parallel Associative Memory Based on Sparse Neural Networks

Zhe Yao, Vincent Gripon, Michael G. Rabbat

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Tags: Artificial intelligence, Computer science, CUDA, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla C1060

March 31, 2013 by hgpu

Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks

Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber

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Tags: Artificial intelligence, Computational Complexity, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX 580

February 12, 2013 by hgpu

Portable Performance on Heterogeneous Architectures

Phitchaya Mangpo Phothilimthana, Jason Ansel, Jonathan Ragan-Kelley, Saman Amarasinghe

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Tags: Artificial intelligence, ATI, ATI Radeon HD 6630 M, Compilers, Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, OpenCL, Performance, Tesla C2070

February 2, 2013 by hgpu

Techniques for designing GPGPU games

Mark Joselli, Jose Ricardo da Silva Junior, Marcelo Zamith, Esteban Clua, Mateus Pelegrino, Evandro Mendonca, Eduardo Soluri

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Tags: Artificial intelligence, Computer science, CUDA, Game physics, nVidia, nVidia GeForce 8200 M

October 23, 2012 by hgpu

High Performance Computing on Astrophysics with Artificial Intelligence Algorithms

Juan C. Cuevas-Tello

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Tags: Algorithms, Artificial intelligence, Astrophysics, CUDA, MPI, Neural networks, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, Physics

September 16, 2012 by hgpu

Large Scale Monte Carlo Tree Search on GPU

Kamil Marek Rocki

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Games, nVidia, Optimization, Search, Tesla C2050, Thesis

August 11, 2012 by hgpu

Automatic Discovery of Algorithms for Multi-Agent Systems

Sjors van Berkel, Daniel Turi, Andrei Pruteanu, Stefan Dulman

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Genetic programming, nVidia, Search strategies

May 9, 2012 by hgpu

CudaRF: A CUDA-based Implementation of Random Forests

Hakan Grahn, Niklas Lavesson, Mikael Hellborg Lapajne, Daniel Slat

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Machine learning, nVidia, nVidia GeForce GT 220

February 9, 2012 by hgpu

Parallel Algorithms for GPU accelerated Probabilistic Inference

Nico Piatkowski

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Package, Tesla C2050

December 27, 2011 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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high performance computing on graphics processing units: hgpu.org

Exploring Multiple Dimensions of Parallelism in Junction Tree Message Passing

Coalition Structure Generation with the Graphic Processor Unit

libCudaOptimize: an Open Source Library of GPU-based Metaheuristics

A Massively Parallel Associative Memory Based on Sparse Neural Networks

Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks

Portable Performance on Heterogeneous Architectures

Techniques for designing GPGPU games

High Performance Computing on Astrophysics with Artificial Intelligence Algorithms

Large Scale Monte Carlo Tree Search on GPU

Automatic Discovery of Algorithms for Multi-Agent Systems

CudaRF: A CUDA-based Implementation of Random Forests

Parallel Algorithms for GPU accelerated Probabilistic Inference

Recent source codes

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

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