29,614 research outputs found
Accelerating Scientific Computing Models Using GPU Processing
GPGPUs offer significant computational power for programmers to leverage. This computational power is especially useful when utilized for accelerating scientific models. This thesis analyzes the utilization of GPGPU programming to accelerate scientific computing models.
First the construction of hardware for visualization and computation of scientific models is discussed. Several factors in the construction of the machines focus on the performance impacts related to scientific modeling.
Image processing is an embarrassingly parallel problem well suited for GPGPU acceleration. An image processing library was developed to show the processes of recognizing embarrassingly parallel problems and serves as an excellent example of converting from a serial CPU implementation to a GPU accelerated implementation. Genetic algorithms are biologically inspired heuristic search algorithms based on natural selection. The Tetris genetic algorithm with A* pathfinding discusses memory bound limitations that can prevent direct algorithm conversions from the CPU to the GPU. An analysis of an existing landscape evolution model, CHILD, for GPU acceleration explores that even when a model shows promise for GPU acceleration, the underlying data structures can have a significant impact upon that ability to move to a GPU implementation. CHILD also offers an example of creating tighter MATLAB integration between existing models.
Lastly, a parallel spatial sorting algorithm is discussed as a possible replacement for current spatial sorting algorithms implemented in models such as smoothed particle hydrodynamics
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
Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance
The paper presents a Stateflow based network test-bed to validate real-time
optimal control algorithms. Genetic Algorithm (GA) based time domain
performance index minimization is attempted for tuning of PI controller to
handle a balanced lag and delay type First Order Plus Time Delay (FOPTD)
process over network. The tuning performance is validated on a real-time
communication network with artificially simulated stochastic delay, packet loss
and out-of order packets characterizing the network.Comment: 6 pages, 12 figure
MATSuMoTo: The MATLAB Surrogate Model Toolbox For Computationally Expensive Black-Box Global Optimization Problems
MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally
expensive, black-box, global optimization problems that may have continuous,
mixed-integer, or pure integer variables. Due to the black-box nature of the
objective function, derivatives are not available. Hence, surrogate models are
used as computationally cheap approximations of the expensive objective
function in order to guide the search for improved solutions. Due to the
computational expense of doing a single function evaluation, the goal is to
find optimal solutions within very few expensive evaluations. The multimodality
of the expensive black-box function requires an algorithm that is able to
search locally as well as globally. MATSuMoTo is able to address these
challenges. MATSuMoTo offers various choices for surrogate models and surrogate
model mixtures, initial experimental design strategies, and sampling
strategies. MATSuMoTo is able to do several function evaluations in parallel by
exploiting MATLAB's Parallel Computing Toolbox.Comment: 13 pages, 7 figure
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Intelligent Active Vibration Control for a Flexible Beam System
YesThis paper presents an investigation into the
development of an intelligent active vibration control
(AVC) system. Evolutionary Genetic algorithms (GAs)
and Adaptive Neuro-Fuzzy Inference system (ANFIS)
algorithms are used to develop mechanisms of an AVC
system, where the controller is designed on the basis of
optimal vibration suppression using the plant model. A
simulation platform of a flexible beam system in
transverse vibration using finite difference (FD) method
is considered to demonstrate the capabilities of the AVC
system using GAs and ANFIS. MATLAB GA tool box for
GAs and Fuzzy Logic tool box for ANFIS function are
used for AVC system design. The system is then
implemented, tested and its performance assessed for GAs
and ANFIS based design. Finally a comparative
performance of the algorithm in implementing AVC
system using GAs and ANFIS is presented and discussed
through a set of experiments
Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm
The Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations
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