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parallel Evolutionary Association Rule Mining for Efficient Summarization of Wireless Sensor Network Data Pattern 5

Parallel Evolutionary Association Rule Mining for Efficient ...

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5th international conference on Cyber and IT Service Management (CITSM)

作者： Wedashwara, Wirarama Mabu, Shingo Ahmadi, Candra STIKOM Bali Bali Indonesia Yamaguchi Univ Ube Yamaguchi Japan

ISBN: (纸本)9781538627372

Evolutionary computation is widely used to solve dynamic problems such as association rule mining(ARM) by adapting the solutions to changes of the data pattern. the summarization in the ARM for a wireless sensor network(WSN) is still an issue when its applied to big number of sensor input from multiple location. this paper proposes a parallel processing of ARM for efficient WSN processing using genetic network programming (GNP). the proposed method adopt the concept of "assumption" as rules from the training data and optimize the rules definition to be a "specific rules" for each sensors network location via parallel evolutionary processing. then summarization is build by calculating hierarchy of "common rules" or similarity between different location. the simulation done using the set of weather forecast sensors. the results shows that the proposed method is capable to efficiently summarize the sensor input from multiple location with online processing and archived a close result to conventional method that performed without on-line processing.

关键词： Evolutionary Computation Genetic Network Programming Rule Association Mining Wireless Sensor Network Internet of things

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Graphie: Large-Scale Asynchronous Graph Traversals on Just a GPU 26

Graphie: Large-Scale Asynchronous Graph Traversals on Just a...

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26th international conference on parallel architectures and Compilation Techniques (PACT)

作者： Han, Wei Mawhirter, Daniel Wu, Bo Buland, Matthew Colorado Sch Mines Dept Comp Sci Golden CO 80401 USA Salesforce Louisville CO USA Colorado Sch Mines Golden CO 80401 USA

ISBN: (纸本)9781509067640

Most GPU-based graph systems cannot handle large-scale graphs that do not fit in the GPU memory. the ever-increasing graph size demands a scale-up graph system, which can run on a single GPU with optimized memory access efficiency and well-controlled data transfer overhead. However, existing systems either incur redundant data transfers or fail to use shared memory. In this paper we present Graphie, a system to efficiently traverse large-scale graphs on a single GPU. Graphie stores the vertex attribute data in the GPU memory and streams edge data asynchronously to the GPU for processing. Graphie's high performance relies on two renaming algorithms. the first algorithm renames the vertices so that the source vertices can be easily loaded to the shared memory to reduce global memory accesses. the second algorithm inserts virtual vertices into the vertex set to rename real vertices, which enables the use of a small boolean array to track active partitions. the boolean array also resides in shared memory and can be updated in constant time. the renaming algorithms do not introduce any extra overhead in the GPU memory or graph storage on disk. Graphie's runtime overlaps data transfer with kernel execution and reuses transferred data in the GPU memory. the evaluation of Graphie on 7 real-world graphs with up to 1.8 billion edges demonstrates substantial speedups over X-Stream, a state-of-the-art edge-centric graph processing framework on the CPU, and GraphReduce, an out-of-memory graph processing systems on GPUs.

关键词： Graph Traversal GPUs Out-Of-Memory processing Data Transformation

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Implementing a space-aware stochastic simulator on low-power architectures: a systems biology case study 25

Implementing a space-aware stochastic simulator on low-power...

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25th Euromicro international conference on parallel, Distributed and Network-Based processing (PDP)

作者： Morganti, Lucia Corni, Elena Ferraro, Andrea Cesini, Daniele D'Agostino, Daniele Merelli, Ivan Italian Inst Nucl Phys CNAF Bologna Italy Natl Res Council Italy Inst Appl Math & Informat Technol E Magenes Genoa Italy Natl Res Council Italy Inst Biomed Technol Segrate Mi Italy

ISBN: (纸本)9781509060580

In the last decade, different computing paradigms and modelling frameworks for the description and simulation of biochemical systems based on stochastic modelling have been proposed. From a computational point of view, many simulations of the model are necessary to identify the behaviour of the system. the execution of thousands of simulations can require huge amount of time, therefore the parallelization of these algorithms is highly desirable. In particular, models that consider the size of volumes and objects involved in the reaction are very time-consuming, since many rules should be considered to take into account the position of the different molecules. In this work we present an implementation of a stochastic space-aware simulator which exploits the benefit and features of hybrid low-power computing architectures. this work shows that the simulator dynamic probabilistic approach to select possible chemical reactions can be applied and implemented in hybrid low-power low-cost architectures as well as current industry high-end servers.

关键词： Computational modeling Biological system modeling Stochastic processes Computer architecture Evolution (biology) Biomembranes Power system dynamics

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A parallel approach of spectrometry Raman image processing using OpenCL 5

A parallel approach of spectrometry Raman image processing u...

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5th international conference on Electrical Engineering - Boumerdes (ICEE-B)

作者： Zouaneb, Imane Belarbi, Mostefa Chouarfia, Abdellah Ibn Khldoun Univ LIM Res Lab Tiaret Algeria Univ Sci & Technol Mohamed Boudiaf LIM Res Lab Oran Algeria

ISBN: (纸本)9781538606865

Raman spectrometry is a technique that allows detecting chemical products through a number of representative peaks found in an image spectrum or numeric series of data. Raman spectrometry is a necessary technic in many fields such as physics, chemistry and Biology. the Raman spectrometry machine analyses a product and generates images as a curve. the interpretation of the curves peaks permits to detect the chemical origin of the analyzed product. Scientists do this operation manually, which makes it hard and long in terms of time. the aim of the present paper is to automate the molecule detection operation using image-processing techniques. We propose a parallel solution to detect the peaks using OpenCL on Graphics processing Unit (GPU). GPUs allow us to make the application faster and more efficient, thanks to its multicore architecture.

关键词： Raman spectrometry image processing GPU OpenCL

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Extraction of Recursion Level parallelism for Embedded Multicore Systems 17

Extraction of Recursion Level Parallelism for Embedded Multi...

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17th Annual international conference on Embedded Computer Systems - architectures, Modeling, and Simulation (SAMOS)

作者： Aguilar, Miguel Angel Leupers, Rainer Ascheid, Gerd Eusse, Juan Fernando Rhein Westfal TH Aachen Inst Commun Technol & Embedded Syst Aachen Germany Silex GmbH Herne Germany

ISBN: (纸本)9781538634370

Recursive programs that typically implement divide-and-conquer algorithms are well-suited for multicore systems, as they offer a high degree of parallelization potential. So far, existing parallelizing compilers have mainly focused on extracting other parallel patterns, such as data or pipeline level parallelism. In this paper, we propose a toolflow for the extraction of recursion level parallelism for embedded multicore systems. To achieve this, the toolflow verifies not only the mutual independence of recursive call-sites, but also selects an appropriate task granularity to ensure a good trade-off between load balancing and parallelization overhead. Profitable parallelization opportunities are implemented by using compiler directives from the OpenMP tasking model. Results show the effectiveness of our toolflow, as it is able to speedup sequential recursive programs between 2.5x and 3.8x on a quad-core platform.

关键词： Task analysis Multicore processing parallel processing Analytical models Performance analysis Static analysis Load management task analysis load management parallel processing (COMPUTERS) ANALYSIS MODELS Multi-core processors parallelism Synclitism Recursion Recursive Procedure parallelizing compilers parallelization

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A Direct Proof of the 4/3 Bound of LPT Scheduling Rule

A Direct Proof of the 4/3 Bound of LPT Scheduling Rule

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2017 5th international conference on Frontiers of Manufacturing Science and Measuring Technology(FMSMT 2017)

作者： Xiao Xin College of Foreign Studies Shandong Institute of Business and Technology

ISBN: (纸本)9781510840232

the problem of scheduling independent jobs on identical parallel machines for minimizing makespan has been intensely studied in the literature. One of the most popular constructive algorithms for this problem is the LPT(Longest processing Time First) rule whose approximation ratio has been proved by contradiction. A direct proof of its approximation ratio is presented, which can be regarded as an acquisition of knowledge by deductive means.

关键词： Scheduling parallel Machines Longest processing Time First Worst-case Analysis

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Classification of EEG Signals for Cognitive Load Estimation Using Deep Learning architectures 10th

Classification of EEG Signals for Cognitive Load Estimation ...

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10th international conference on Intelligent Human Computer Interaction (IHCI)

作者： Saha, Anushri Minz, Vikash Bonela, Sanjith Sreeja, S. R. Chowdhury, Ritwika Samanta, Debasis Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur W Bengal India

ISBN: (纸本)9783030040215;9783030040208

Measuring cognitive load is crucial for many applications such as information personalization, adaptive intelligent tutoring systems, etc. Cognitive load estimation using Electroencephalogram (EEG) signals is widespread as it produces clear indications of cognitive activities by measuring changes of neural activation in the brain. However, the existing cognitive load estimation techniques are based on machine learning algorithms, which follow signal denoising and hand-crafted feature extraction to classify different loads. there is a need to find a better alternative to the machine learning approach. Of late, deep learning approach has been successfully applied to many applications namely, computer vision, pattern recognition, speech processing, etc. However, deep learning has not been extensively studied for the classification of cognitive load data captured by an EEG. In this work, two deep learning models are studied, namely stacked denoising autoencoder (SDAE) followed by a multilayer perceptron (MLP) and long short term memory (LSTM) followed by an MLP to classify cognitive load data. SDAE and LSTM are used for feature extraction and MLP for classification. It is observed that deep learning models perform significantly better than the conventional machine learning classifiers such as support vector machine (SVM), k-nearest neighbors (KNN), and linear discriminant analysis (LDA).

关键词： Cognitive load Stacked denoising autoencoder Long short term memory Multilayer perceptron

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An Improved Abstract GPU Model with Data Transfer 46

An Improved Abstract GPU Model with Data Transfer

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46th international conference on parallel processing Workshops (ICPPW)

作者： Carroll, thomas C. Wong, Prudence W. H. Univ Liverpool Dept Comp Sci Liverpool Merseyside England

ISBN: (纸本)9781538610442

GPUs are commonly used as coprocessors to accelerate a compute-intensive task, thanks to their massively parallel architecture. there is study into different abstract parallel models, which allow researchers to design and analyse parallel algorithms. However, most work on analysing GPU algorithms has been software based tools for profiling a GPU algorithm. Recently, some abstract GPU models have been proposed, yet they do not capture all elements of a GPU. In particular, they miss the data transfer between CPU and GPU, which in practice can cause a bottleneck and reduce performance dramatically. We propose a comprehensive model called Abstract Transferring GPU which to our knowledge is the first abstract GPU model to capture data transfer between CPU and GPU. We show via experiments, that existing abstract GPU models cannot sufficiently capture all of the actual running of a GPU algorithm time in all cases, as they do not capture data transfer. We show that by capturing data transfer with our model, we are able to obtain more accurate predictions of the GPU algorithm actual running time. It is expected that our model helps improve design and analysis of heterogeneous systems consisting of CPU and GPU, and will allow researchers to make better informed implementation decisions, as they will be aware how data transfer will affect their programs.

关键词： Graphics processors parallel architectures Data communications Modelling and prediction

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the Open Community Runtime on the Intel Knights Landing Architecture 17th

The Open Community Runtime on the Intel Knights Landing Arch...

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17th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Dokulil, Jiri Benkner, Siegfried Yaghob, Jakub Univ Vienna Fac Comp Sci Vienna Austria Charles Univ Prague Dept Software Engn Prague Czech Republic

ISBN: (纸本)9783319654829;9783319654812

the Intel Xeon Phi Knights Landing manycore processor comes with new interesting features: on-chip high-bandwidth memory and several user-selectable NUMA configurations. In this paper, we look into how these affect applications that target the Open Community Runtime (OCR), an asynchronous tasked-based runtime system for future parallel architectures. We have extended our OCR runtime to make it NUMA aware and to allow it to use the high-bandwidth memory. We have conducted a range of experiments, comparing OpenMP, TBB, our OCR implementation, and the reference OCR implementation on different machine configurations using a memory intensive seismic simulation.

关键词： Open Community Runtime Knights Landing Intel Xeon Phi High-bandwidth memory parallel runtime systems NUMA

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A Generic High throughput Architecture for Stream processing 27

A Generic High Throughput Architecture for Stream Processing

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27th international conference on Field Programmable Logic and Applications (FPL)

作者： Rousopoulos, Christos Karandeinos, Ektoras Chrysos, Grigorios Dollas, Apostolos Pnevmatikatos, Dionisios N. Tech Univ Crete Sch Elect & Comp Engn Khania 73100 Greece

ISBN: (纸本)9789090304281

Stream join is a fundamental and computationally expensive data mining operation for relating information from different data streams. this paper presents two FPGA-based architectures that accelerate stream join processing. the proposed hardware-based systems were implemented on a multi-FPGA hybrid system with high memory bandwidth. the experimental evaluation shows that our proposed systems can outperform a software-based solution that runs on a high-end, 48-core multiprocessor platform by at least one order of magnitude. In addition, the proposed solutions outperform any other previously proposed hardware-based or software-based solutions for stream join processing. Finally, our proposed hardware-based architectures can be used as generic templates to map stream processing algorithms on reconfigurable logic, taking into consideration real-world challenges and restrictions.

关键词： Computer architecture Hardware throughput Bandwidth Field programmable gate arrays parallel processing

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