4,191 research outputs found
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
We implement a master-slave parallel genetic algorithm (PGA) with a bespoke
log-likelihood fitness function to identify emergent clusters within price
evolutions. We use graphics processing units (GPUs) to implement a PGA and
visualise the results using disjoint minimal spanning trees (MSTs). We
demonstrate that our GPU PGA, implemented on a commercially available general
purpose GPU, is able to recover stock clusters in sub-second speed, based on a
subset of stocks in the South African market. This represents a pragmatic
choice for low-cost, scalable parallel computing and is significantly faster
than a prototype serial implementation in an optimised C-based
fourth-generation programming language, although the results are not directly
comparable due to compiler differences. Combined with fast online intraday
correlation matrix estimation from high frequency data for cluster
identification, the proposed implementation offers cost-effective,
near-real-time risk assessment for financial practitioners.Comment: 10 pages, 5 figures, 4 tables, More thorough discussion of
implementatio
GPU acceleration of brain image proccessing
Durante los últimos años se ha venido demostrando el alto poder computacional
que ofrecen las GPUs a la hora de resolver determinados problemas.
Al mismo tiempo, existen campos en los que no es posible beneficiarse completamente
de las mejoras conseguidas por los investigadores, debido principalmente
a que los tiempos de ejecución de las aplicaciones llegan a ser extremadamente
largos. Este es por ejemplo el caso del registro de imágenes en medicina.
A pesar de que se han conseguido aceleraciones sobre el registro de imágenes,
su uso en la práctica clÃnica es aún limitado. Entre otras cosas, esto se debe
al rendimiento conseguido.
Por lo tanto se plantea como objetivo de este proyecto, conseguir mejorar los
tiempos de ejecución de una aplicación dedicada al resgitro de imágenes en medicina,
con el fin de ayudar a aliviar este problema
TensorFlow Enabled Genetic Programming
Genetic Programming, a kind of evolutionary computation and machine learning
algorithm, is shown to benefit significantly from the application of vectorized
data and the TensorFlow numerical computation library on both CPU and GPU
architectures. The open source, Python Karoo GP is employed for a series of 190
tests across 6 platforms, with real-world datasets ranging from 18 to 5.5M data
points. This body of tests demonstrates that datasets measured in tens and
hundreds of data points see 2-15x improvement when moving from the scalar/SymPy
configuration to the vector/TensorFlow configuration, with a single core
performing on par or better than multiple CPU cores and GPUs. A dataset
composed of 90,000 data points demonstrates a single vector/TensorFlow CPU core
performing 875x better than 40 scalar/Sympy CPU cores. And a dataset containing
5.5M data points sees GPU configurations out-performing CPU configurations on
average by 1.3x.Comment: 8 pages, 5 figures; presented at GECCO 2017, Berlin, German
Genetic Programming Approach for Classification Problem using GPU
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer programs using a genetic algorithm. Genetic programming have proven to be a good technique for solving data set classification problems but at high computational cost. The objectives of this research is to accelerate the execution of the classification algorithms by proposing a general model of execution in GPU of the adjustment function of the individuals of the population. The computation times of each of the phases of the evolutionary process and the operation of the model of parallel programming in GPU were studied. Genetic programming is interesting to parallelize from the perspective of evolving a population of individuals in paralle
Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU
A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to
solve the Uncapacitated Single Allocation p-Hub Median problem. The GA uses
binary and integer encoding and genetic operators adapted to this problem. Our
GA is improved by generated initial solution with hubs located at middle nodes.
The obtained experimental results are compared with the best known solutions on
all benchmarks on instances up to 1000 nodes. Furthermore, we solve our own
randomly generated instances up to 6000 nodes. Our approach outperforms most
well-known heuristics in terms of solution quality and time execution and it
allows hitherto unsolved problems to be solved
Genetic Programming Approach for Classification Problem using GPU
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer programs using a genetic algorithm. Genetic programming have proven to be a good technique for solving data set classification problems but at high computational cost. The objectives of this research is to accelerate the execution of the classification algorithms by proposing a general model of execution in GPU of the adjustment function of the individuals of the population. The computation times of each of the phases of the evolutionary process and the operation of the model of parallel programming in GPU were studied. Genetic programming is interesting to parallelize from the perspective of evolving a population of individuals in parallel
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