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parallel MEAN SHIFT ACCURACY AND PERFORMANCE TRADE-OFFS

PARALLEL MEAN SHIFT ACCURACY AND PERFORMANCE TRADE-OFFS

引用

IEEE international conference on Image processing

作者： Kirsty R. Duncan Robert Stewart Greg Michaelson School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh United Kingdom

this paper decomposes the algorithmic parameters that affect the accuracy and parallel run times of mean shift segmentation. Following Comaniciu and Meer, rather than perform calculations in the feature space of the image, the joint spatial-range domain is represented by the image space, with feature space information associated with each point. We report parallel speedup and segmentation accuracy using a standardised segmentation dataset and the Probabilistic Rand index (PRI) accuracy measure. Changes to the algorithmic parameters are analysed and a sweet spot between PRI and run time is found. Using a range window radius of 20, spatial window radius of 10 and threshold of 50, the PRI is improved by 0.17, an increase of 34% which is comparable to state of the art. Mean shift clustering run time is reduced by 97% with parallelism, a speedup of 32 on a 64-core CPU.

关键词： Microsoft Windows Image segmentation Merging Clustering algorithms Indexes Probabilistic logic parallel processing

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Accelerating Exhaustive Pairwise Metagenomic Comparisons 1

引用

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

作者： Perez-Wohlfeil, Esteban Torreno, Oscar Trelles, Oswaldo Univ Malaga Dept Comp Architecture Blvd Louis Pasteur 35 Malaga Spain

ISBN: (数字)9783319654829

ISBN: (纸本)9783319654829;9783319654812

In this manuscript, we present an optimized and parallel version of our previous work IMSAME, an exhaustive gapped aligner for the pairwise and accurate comparison of metagenomes. parallelization strategies are applied to take advantage of modern multiprocessor architectures. In addition, sequential optimizations in CPU time and memory consumption are provided. these algorithmic and computational enhancements enable IMSAME to calculate near optimal alignments which are used to directly assess similarity between metagenomes without requiring reference databases. We show that the overall efficiency of the parallel implementation is superior to 80% while retaining scalability as the number of parallel cores used increases. Moreover, we also show that sequential optimizations yield up to 8x speedup for scenarios with larger data.

关键词： High Performance Computing Pairwise comparison parallel computing Next Generation Sequencing Metagenome comparison

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parallel acceleration of HEVC decoder based on CPU+GPU heterogeneous platform 7

Parallel acceleration of HEVC decoder based on CPU+GPU heter...

引用

7th international conference on Information Science and Technology, ICIST 2017

作者： Ma, Aidi Guo, Chengan School of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781509054015

the High Efficiency Video Coding (HEVC) standard, as the newest generation video coding standard issued in 2013, significantly improves compression performance relative to existing standards in about 50% bit-rate reduction for equal perceptual video quality with the cost of greatly increasing the computation complexity of the encoder/decoder. In order to improve the decoding efficiency, we design a set of parallel decoding algorithms based on the CPU+GPU heterogeneous platform for the HEVC decoder, in which, the reconstruction processes with high computation complexity, including the inverse quantization (IQ), the inverse discrete cosine transformation (IDCT), the intra/inter decoder, the de-blocking filter (DF), and the sample adaptive offset (SAO), are processed by GPU in parallel, while the network abstract layer (NAL) bit stream parsing and the CABAC bit stream decoding are processed by CPU using serial algorithms on account that they are not suitable for parallel implementation due to their internally contextual relevance. We implement the parallel algorithms by using the compute unified device architecture (CUDA) and test them with various video sequences. the experimental results show that our method can achieve a significant improvement on the computation efficiency for the whole decoding processes and can achieve real-Time decoding with more than 39 frames per second for HD videos. © 2017 IEEE.

关键词： Graphics processing unit

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Educational Multiprocessor Simulator "E14" and Its Usage for Expanding the Formula of Amdahl's Law 25

Educational Multiprocessor Simulator "E14" and Its Usage for...

引用

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

作者： Eremin, Evgeny A. Perm State Humanitarian Pedag Univ Perm Russia

ISBN: (纸本)9781509060580

this paper describes demonstrative software simulator "E14", helpful in studying on an ordinary PC essentials of parallel calculations. It contains five virtual processors with identical instruction set, one of which controls the other four. Simulator has several mechanisms of data exchange between processors, so it can be used for studying both architectures with shared and distributed memory. parallel architecture of "E14" naturally extends classical single-processor one, hence proposed approach makes students' knowledge more systematic. "E14" may also be employed as a platform for the estimation of parallel algorithms' performance. the example, considered in the paper, clearly demonstrates how data exchange between processors may essentially degrade speedup, predicted by Amdahl's law.

关键词： multiprocessor mullticore computer architecture parallel calculations speedup Amdahl's law education simulator

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Feature Selection and Intrusion Detection in Cloud Environment based on Machine Learning algorithms 15

Feature Selection and Intrusion Detection in Cloud Environme...

引用

15th IEEE international Symposium on parallel and Distributed processing with Applications (ISPA) / 16th IEEE international conference on Ubiquitous Computing and Communications (IUCC)

作者： Javadpour, Amir Abharian, Sanaz Kazemi Wang, Guojun Guangzhou Univ Guangzhou Guangdong Peoples R China Sharif Univ Technol Tehran Iran

ISBN: (纸本)9781538637906

Characteristics and way of behavior of attacks and infiltrators on computer networks are very difficult and need an expert. In addition;the advancement of computer networks, the number of attacks and infiltrations is also increasing. In fact, the knowledge coming from expert will lose its value over time and must be updated and made available to the system and this makes the need for expert person always felt. In machine learning techniques, knowledge is extracted from the data itself which has diminished the role of the expert. Various methods used to detect intrusions, such as statistical models, safe system approach, neural networks, etc., all weaken the fact that it uses all the features of an information packet rotating in the network for intrusion detection. Also, the huge volume of information and the unthinkable state space is also an important issue in the detection of intrusion. therefore, the need for automatic identification of new and suspicious patterns in attempt for intrusion with the use of more efficient methods (Lower cost and higher performance) is needed more than before. the purpose of this paper is to provide a new method based on intrusion detection systems and its various architectures aimed at increasing the accuracy of intrusion detection in cloud computing.

关键词： intrusion detection feature Selection classification Algorithm machine learning neural network cloud computing

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GPU-Accelerated Block-Max Query processing 1

引用

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

作者： Huang, Haibing Ren, Mingming Zhao, Yue Stones, Rebecca J. Zhang, Rui Wang, Gang Liu, Xiaoguang Nankai Univ Coll Comp & Control Engn Nankai Baidu Joint Lab Tianjin 300350 Peoples R China

ISBN: (数字)9783319654829

ISBN: (纸本)9783319654829;9783319654812

In this paper, we propose a method for parallel top-k query processing on GPU(s). We employ a novel partitioning strategy which splits the posting lists according to document ID numbers. Individual GPU threads simultaneously perform top-k query processing within their allocated subsets of posting lists, the results of the query are merged to give the final top-k results. We further design a CPU-GPU cooperative query processing method, where a majority of queries involving shorter posting lists are processed on the GPU side. We experiment with AND, OR, WAND, and Block-Max WAND (BMW) queries, with experimental results showing a promising improvement in query throughput, particularly in the case of BMW queries.

关键词： Information retrieval GPU Index partition Query assignment

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SRIANN: SPHERE RING INTERSECTION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH IN VIDEOS

SRIANN: SPHERE RING INTERSECTION FOR APPROXIMATE NEAREST NEI...

引用

IEEE international conference on Image processing

作者： Yi-Ting Chen Wei-Chih Tu Shao-Yi Chien Graduate Institute of Electronics Engineering National Taiwan University

In the field of approximate nearest neighbor (ANN) search, rare of the existing approaches are tailored for video applications. the Ring Intersection Approximate Nearest Neighbor (RIANN) is the first ANN search algorithm for videos. It achieves real-time by performing the ANN search on the sparse grid and interpolating others. For some applications, the dense ANN search is needed to ensure the searching accuracy. To achieve dense ANN search in real-time, we consider the parallel computing as a solution. However, the RIANN algorithm is not suitable for parallel computing as the algorithm itself suffers from bad thread coherency. In this paper, we propose the Sphere Ring Intersection Approximate Nearest Neighbor (SRIANN), which solves the problem of bad thread coherency and improves the accuracy of ANN search compared to the original RIANN method. the experimental results show that the proposed method is the only one able to perform dense ANN search for CIF videos in real-time.

关键词： Instruction sets Videos Graphics processing units Real-time systems Databases Approximation algorithms parallel processing

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Power-Performance Evaluation of parallel Multi-objective EEG Feature Selection on CPU-GPU Platforms 1

引用

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

作者： Jose Escobar, Juan Ortega, Julio Francisco Diaz, Antonio Gonzalez, Jesus Damas, Miguel Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain

ISBN: (数字)9783319654829

ISBN: (纸本)9783319654829;9783319654812

Heterogeneous CPU-GPU platforms include resources to benefit from different kinds of parallelism present in many data mining applications based on evolutionary algorithms that evolve solutions with time-demanding fitness evaluation. this paper describes an evolutionary parallel multi-objective feature selection procedure with subpopulations using two scheduling alternatives for evaluation of individuals according to the number of subpopulations. Evolving subpopulations usually provides good diversity properties and avoids premature convergence in evolutionary algorithms. the proposed procedure has been implemented in OpenMP to distribute dynamically either subpopulations or individuals among devices and OpenCL to evaluate the individuals taking into account the devices characteristics, providing two parallelism levels in CPU and up to three levels in GPUs. Different configurations of the proposed procedure have been evaluated and compared with a master-worker approach considering not only the runtime and achieved speedups but also the energy consumption between both scheduling models.

关键词： Heterogeneous subpopulations scheduling Energy-aware computing EEG classification Multi-objective feature selection GPU

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A Hybrid parallel Search Algorithm for Solving Combinatorial Optimization Problems on Multicore Clusters 1

引用

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

作者： Sanz, Victoria De Giusti, Armando Naiouf, Marcelo Natl Univ La Plata III LIDI Sch Comp Sci La Plata Argentina CIC Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (数字)9783319654829

ISBN: (纸本)9783319654829;9783319654812

Multicore clusters are widely used to solve combinatorial optimization problems, which require high computing power and a large amount of memory. In this sense, Hash Distributed A* (HDA*) parallelizes A*, a combinatorial optimization algorithm, using the MPI library. HDA* scales well on multicore clusters and on multicore machines. Additionally, there exist several versions of HDA* that were adapted for multicore machines, using the Pthreads library. In this paper, we present Hybrid HDA* (HHDA*), a hybrid parallel search algorithm based on HDA* that combines message-passing (MPI) with shared-memory programming (Pthreads) to better exploit the computing power and memory of multicore clusters. We evaluate the performance and memory consumption of HHDA* on a multicore cluster, using the 15-puzzle as a case study. the results reveal that HHDA* achieves a slightly higher average performance and uses considerably less memory than HDA*. these improvements allowed HHDA* to solve one of the hardest 15-Puzzle instances.

关键词： parallel search algorithms Hybrid programming Multicore cluster Combinatorial optimization problems Hash Distributed A

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E-Technologies: Embracing the Internet of things 1st ed. 2017

引用

丛书名： Lecture Notes in Business Information processing

2017年

作者： Esma Aïmeur Umar Ruhi Michael Weiss

ISBN: (数字)9783319590417

ISBN: (纸本)9783319590400

this book constitutes the refereed proceedings of the 7th international conference on E-Technologies, MCETECH 2017, held in Ottawa, ON, Canada, in May 2017. this years conference drew special attention to the ever-increasing role of the Internet of things (IoT); and the contributions span a variety of application domains such as e-Commerce, e-Health, e-Learning, and e-Justice, comprising research from models and architectures, methodology proposals, prototype implementations, and empirical validation of theoretical models. the 19 papers presented were carefully reviewed and selected from 48 submissions. they were organized in topical sections named: pervasive computing and smart applications; security, privacy and trust; process modeling and adaptation; data analytics and machine learning; and e-health and e-commerce.

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