Michal Karpinski, Maciej Pacut
The goal of this paper is to propose and test a new memetic algorithm for the capacitated vehicle routing problem in parallel computing environment. In this paper we consider simple variation of vehicle routing problem in which the only parameter is the capacity of the vehicle and each client only needs one package. We present […]
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Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang
The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision. These results have largely come from computational break throughs of two forms: model parallelism, e.g. GPU accelerated training, which has seen quick adoption in computer vision circles, and data parallelism, e.g. A-SGD, whose large scale has been […]
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Youssef S.G. Nashed, Roberto Ugolotti, Pablo Mesejo, Stefano Cagnoni
Evolutionary Computation techniques and other metaheuristics have been increasingly used in the last years for solving many real-world tasks that can be formulated as optimization problems. Among their numerous strengths, a major one is their natural predisposition to parallelization. In this paper, we introduce libCudaOptimize, an open source library which implements some metaheuristics for continuous […]
Peter Wittek
Somoclu is a C++ tool for training self-organizing maps on large data sets using a high-performance cluster. It builds on MPI for distributing the workload across the nodes of the cluster. It is also able to boost training by using CUDA if graphics processing units are available. A sparse kernel is included, which is useful […]
Zhe Yao, Vincent Gripon, Michael G. Rabbat
Associative memories store content in such a way that the content can be later retrieved by presenting the memory with a small portion of the content, rather than presenting the memory with an address as in more traditional memories. Associative memories are used as building blocks for algorithms within database engines, anomaly detection systems, compression […]
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Chung-Yu Shao, Tian-Li Yu
Parallelization is a straightforward approach to enhance the efficiency for evolutionary computation due to its inherently parallel nature. Since NVIDIA released the compute unified device architecture (CUDA), graphic processing units have enabled lots of scalable parallel programs in a wide range of fields. However, parallelization of model building for EDAs is rarely studied. In this […]
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Johannes Hofmann
Financial Time Series prediction attempts to model the behavior of financial markets using, among other things, tools like technical, intermarket, and fundamental indicators. Accurate prediction, however, is difficult for a number of reasons: financial markets are influenced, often in a non-linear, sometimes time-lagged fashion, by factors including interest and exchange rates, the rate of economic […]
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Shigeyoshi Tsutsui, Noriyuki Fujimoto
There are several studies on solving the quadratic assignment problem (QAP) withGPUs using an evolutionary computation. In our previous studies [3], we applied GPU computation to solve quadratic assignment problems (QAPs) using a distributed parallel GA model on GPUs. However, in those studies no local searches were applied. In this QAP solver, we implemented a […]
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Francis Cabarle, Henry Adorna, Miguel A. Martinez-del-Amor
We present in this paper our work regarding simulating a type of P system known as a spiking neural P system (SNP system) using graphics processing units (GPUs). GPUs, because of their architectural optimization for parallel computations, are well-suited for highly parallelizable problems. Due to the advent of general purpose GPU computing in recent years, […]
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Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Juergen Schmidhuber
Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to greatly speed up learning.
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Dan C. Ciresan, Ueli Meier, Luca M. Gambardella, Jurgen Schmidhuber
Good old online backpropagation for plain multilayer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning. Good […]
Dan C. Ciresan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber
We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, […]
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