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Asynchronous parallel Dijkstra's Algorithm on Intel Xeon Phi Processor How to Accelerate Irregular Memory Access Algorithm 18th

Asynchronous Parallel Dijkstra's Algorithm on Intel Xeon Phi...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Zhang, Weidong Zhang, Lei Chen, Yifeng Peking Univ EECS Beijing 100871 Peoples R China HuaWei Inc Shenzhen Peoples R China

ISBN: (纸本)9783030050511;9783030050504

As the instruction-level parallelism (ILP) on CPU develops to a rather advanced level, the exploration that whether many-core architecture is applicable for graph algorithms is generating more interests in researchers. However, due to the irregular memory access and the low ratio of computation to memory access, the performance of graph algorithms on many-core architectures has never worked good enough. To obtain outstanding speedup on many-core architecture, first of all, we need to figure out three questions: (i) how to optimize the memory access, (ii) how to minimize the overhead of synchronization, (iii) how to exploit the parallelism in algorithm. Prior works hardly reach the goal if such questions are treated in separated way. throughout this paper, we aim to settle these questions systematically, and try to provide a set of methods of optimizing graph algorithms on many-core architecture. this paper mainly discusses how to accelerate the Single Source Shortest Path (SSSP) problem on Intel Many Integrated Core (MIC) architecture, on which we propose an asynchronous parallel Dijkstra's algorithm. It aims at maximizing parallelism and minimizing overhead of synchronization. Experimental result shows that the MIC architecture could efficiently solve the SSSP problem, and its performance could be sped up by 9.2x compared to the benchmark of DIMACS.

关键词： Single source shortest path parallel algorithm Intel MIC Road network

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A Caching-Based parallel FP-Growth in Apache Spark 18th

A Caching-Based Parallel FP-Growth in Apache Spark

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Cai, Zhicheng Zhu, Xingyu Zheng, Yuehui Liu, Duan Xu, Lei Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9783030050573;9783030050566

the association-rule-based recommendation is widespread in many big data applications which need quick response to improve user experience. Spark is a widely used distributed computing platform, which accelerates the processing of large-scale distributed data. Developing appropriate distributed algorithm for Spark is essential to decrease the processing time of distributed recommendation. the existing FP-Growth in Spark is a popular parallel recommendation method but getting the best performance only when the memory of machines can accommodate all immediate Resilient Distributed DataSets (RDDs). However, memory of many practice data centers is still not large enough for large data sets. therefore, in this paper, a caching-based parallel FP-Growth is proposed which consists of an integer-based sorting and an RDD-caching strategy to improve the efficiency. Experimental results show that the proposal decreases the execution time by 32.37% on average compared with the existing parallel FP-Growth in Spark. Furthermore, impacts of some important parameters upon the performance of the proposal are analyzed by numerous realistic experiments in Spark.

关键词： Spark parallel FP-Growth Caching strategy

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A parallel Method for All-Pair SimRank Similarity Computation 18th

A Parallel Method for All-Pair SimRank Similarity Computatio...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Huang, Xuan Gao, Xingkun Tang, Jie Wu, Gangshan Nanjing Univ Dept Comp Sci & Technol Nanjing 210023 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

ISBN: (纸本)9783030050511;9783030050504

How to measure SimRank similarity of all-pair vertices in a graph is a very important research topic which has a wide range of applications in many fields. However, computation of SimRank is costly in both time and space, making traditional computing methods failing to handle graph data of ever-growing size. this paper proposes a parallel multi-level solution for all-pair SimRank similarity computing on large graphs. We partition the objective graph first with the idea of modularity maximization and get a collapsed graph based on the blocks. then we compute the similarities between verteices inside a block as well as the similarities between the blocks. In the end, we integrate these two types of similarities and calculate the approximate SimRank simlarities between all vertex pairs. the method is implemented on Spark platform and it makes an improvement on time efficiency while maintaining the effectiveness compared to SimRank.

关键词： SimRank similarity Spark parallel computing

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A parallel framework for maximal clique enumeration

A parallel framework for maximal clique enumeration

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5th Biennial international conference on Emerging Trends in Engineering, Science and Technology (ICETEST)

作者： Anusree, thilak Rahamathulla, K. Govt Engn Coll Dept Comp Sci & Engn Trichur Kerala India

ISBN: (纸本)9781351124140;9780815357605

Graphs play a major role in modeling many real-world problems. Due to the availability of huge data, the graph processing in serial environment become more complex. thus, fast and efficient algorithms which work effectively utilizing the modern technologies are required. A maximal clique problem is one of the graph processing methods which is used in many applications. the Bron-Kerbosch (BK) algorithm is the most widely used and accepted algorithm for listing each and every maximal clique in a graph. Here an idea of a parallel version of BK algorithm is proposed which will reduce the computation time to a large extent than its serial implementation. It utilizes the cluster computing strategy.

关键词： Cluster computing

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parallelization of a Multipartite Graph Matching Algorithm for Tracking Multiple Football Players 5

Parallelization of a Multipartite Graph Matching Algorithm f...

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5th IEEE international conference on parallel, Distributed and Grid Computing (PDGC)

作者： Villalta, M. Siles, F. Univ Costa Rica Sch Engn Dept Elect Engn Pattern Recognit & Intelligent Syst Lab San Jose Costa Rica

ISBN: (纸本)9781728106465

this work describes the parallel methodology for a football tracking algorithm based on multipartite graphs using MPI and OpenMP. the proposed algorithm use a consumer producer scheme to overlap the computing time of the two main procedures of the tracking algorithm: segmentation and tracking;as well a send-and-receive communication pattern to propagate the blob identities. We show how an hybrid system of data and task parallelization improves the execution time for 4K videos, achieving a speedup equal to 19.24 and a processing speed of 21.71 FPS with 128 threads.

关键词： Terms parallel algorithms Association Football Temporal Segmentation Tracking of Football Players

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Screening Feasibility and Comparison of Deep Artificial Neural Networks algorithms for Classification of Skin Lesions 18

Screening Feasibility and Comparison of Deep Artificial Neur...

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5th international conference on Bioinformatics Research and Applications (ICBRA)

作者： Santos, A. P. Sousa, R. M. Bianchi, M. H. G. Cordioli, E. Silva, L. A. Univ Presbiteriana Mackenzie BR-01302000 Sao Paulo SP Brazil Hosp Israelita Albert Einstein Telemed Dept BR-05652900 Sao Paulo SP Brazil

ISBN: (纸本)9781450366113

Deep convolutional neural networks (CNNs) have proven its potential for many tasks related to object identification and classification. this study aims to show the performance of several convolutional neural networks architectures applied to the diagnosis and screening of skin lesions in patients using different training techniques: Random weights initialization, feature extraction and extending model. A dataset of 1000 clinical images proven by biopsy or consensus among specialists were the examples applied at the various architectures which were end-to-end trained from images directly, using only pixels and disease labels as inputs. the predictions provided from the models intended to claim whether the lesion could be treated by doctors with images only on a teledermatology approach or if it is necessary to prescribe a biopsy or referral to a face-to-face consultation. the model can also tell the urgency of the case and the group of diseases which that lesion belongs to. Performances of deep neural networks in all proposed tasks demonstrated that artificial intelligence has the potential to perform the screening of skin lesions with a level of competence comparable to dermatologists. It is projected 6.3 billion signatures of smartphone by the year 2021 [38]. therefore, deep neural networks incorporated in mobile devices can amplify the reach of dermatologists outside their offices providing universal low-cost access to dermatological diagnostics.

关键词： Deep neural networks Telemedicine Teledermatology Patient screening Image processing Artificial intelligence

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Scheduler Accelerator for TDMA Data Centers 26

Scheduler Accelerator for TDMA Data Centers

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26th Euromicro international conference on parallel, Distributed, and Network-Based processing (PDP)

作者： Patronas, Ioannis Gkatzios, Nikolaos Kitsakis, Vasileios Reisis, Dionysios Christodoulopoulos, Konstantinos Varvarigos, Emmanouel Univ Athens Elect Lab Phys Dpt GR-15784 Zografos Greece Natl Tech Univ Athens Inst Commun & Comp ICCS Athens Greece Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece

ISBN: (纸本)9781538649756

Today's Data Centers networks depend on optical switching to overcome the scalability limitations of traditional architectures. All optical networks most often use slotted Time Division Multiple Access (TDMA) operation;their buffers are located at the optical network edges and their organization relies on effective scheduling of the TDMA frames to achieve efficient sharing of the network resources and a collision-free network operation. Scheduling decisions have to be taken in real time, a process that becomes computationally demanding as the network size increases. Accelerators provide a solution and the present paper proposes a scheduler accelerator to accommodate a data center network divided into points of delivery (pods) of racks and exploiting hybrid electro-optical top-of-rack (ToR) switches that access an all-optical inter-rack network. the scheduler accelerator is a parallel scalable architecture with application specific processing engines. Case studies of 2, 4, 8, 16 processors configuration are presented for the processing of all the transfer TDMA time slot requests for the cases of 512 and 1024 ToR network nodes. the architecture is realized on a Xilinx VC707 board to validate the results.

关键词： Data Center Scheduler parallel algorithms FPGA accelarator

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Performance Evaluation of Classification Algorithm in Weka using parallel Performance Profiling and Computing Technique 5

Performance Evaluation of Classification Algorithm in Weka u...

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5th IEEE international conference on parallel, Distributed and Grid Computing (PDGC)

作者： Upadhyay, Navin Mani Singh, Ravi Shankar Banaras Hindu Univ Indian Inst Technol Dept Comp Sci & Engn Varanasi Uttar Pradesh India

ISBN: (纸本)9781728106465

At present, the size of the data is growing rapidly. In such a situation, it is necessary to keep an eye on the speed of the data without undermining it. It is also important to note that while processing the data, its quality remains intact. this is the reason that data mining technology has become very important in the field of predictions in the scientific mining area, commercial and environment sectors. In this case, the need for parallel processing becomes important. therefore, the aim of this paper is to analyze and perform computation times of different classification algorithms on many datasets using parallel profiling and computing techniques. Performance analysis is based on many factors, such as the unique nature of the dataset, the size, and type of the class, the diversity of the data in the data set, and so on. Many researchers are working on the optimization of classification algorithms which are not showing accurate results according to the processor (core) capacity. So, in this paper, we have displayed some simulation results which discuss the processor's size, efficiency, and workload as well as the complexity of input instructions in the group;Which will help researchers to optimize the code for maximum use of the core. At the end of the paper, we have given a comparative study of the optimized algorithm based on a parallel approach and tuned algorithm based on parallel performance.

关键词： Keywords Data Mining Classification Performance WEKA parallel Profiling Computation

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ParallNormal: an efficient variant calling pipeline for unmatched sequencing data 26

ParallNormal: an efficient variant calling pipeline for unma...

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26th Euromicro international conference on parallel, Distributed, and Network-Based processing (PDP)

作者： Follia, Laura Tordini, Fabio Pernice, Simone Romano, Greta Piaggeschi, Giulia Beatrice Ferrero, Giulio Univ Turin Comp Sci Dept Turin Italy Ctr Expt Res & Med Studies CeRMS Turin Italy Univ Turin Mol Biotechnol & Hlth Sci Dept Turin Italy Italian Inst Genom Med Turin Italy Univ Turin Clin & Biol Sci Dept Turin Italy

ISBN: (纸本)9781538649756

Nowadays, next generation sequencing is closer to clinical application in the field of oncology. Indeed, it allows the identification of tumor-specific mutations acquired during cancer development, progression and resistance to therapy. In parallel with an evolving sequencing technology, novel computational approaches are needed to cope with the requirement of a rapid processing of sequencing data into a list of clinically-relevant genomic variants. Since sequencing data from both tumors and their matched normal samples are not always available (unmatched data), there is a need of a computational pipeline leading to variants calling in unmatched data. Despite the presence of many accurate and precise variant calling algorithms, an efficient approach is still lacking. Here, we propose a parallel pipeline (ParallNormal) designed to efficiently identify genomic variants from whole-exome sequencing data, in absence of their matched normal. ParallNormal integrates well-known algorithms such as BWA and GATK, a novel tool for duplicate removal (DuplicateRemove), and the FreeBayes variant calling algorithm. A re-engineered implementation of FreeBayes, optimized for execution on modern multi-core architectures is also proposed. ParallNormal was applied on whole-exome sequencing data of pancreatic cancer samples without considering their matched normal. the robustness of ParallNormal was tested using results of the same dataset analyzed using matched normal samples and considering genes involved in pancreatic carcinogenesis. Our pipeline was able to confirm most of the variants identified using matched normal data.

关键词： Genomics Sequential analysis Bioinformatics Pipelines Tools Tumors Bayes methods

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Biomedical Data Analysis Based on parallel Programming Technology Application for Computation Features' Effectiveness

Biomedical Data Analysis Based on Parallel Programming Techn...

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international conference on Frontiers of Signal processing (ICFSP)

作者： Nataly Ilyasova Alexander Shirokanev Rustam Paringer Alexandr Kupriyanov Samara National Research University IPSI of the RAS - Branch of the FSRC Crystallography and Photonics Samara Russia

this paper proposes a technology for large biomedical data analyzing based on CUDA computation. the technology was used to analyze a large set of fundus images used for diabetic retinopathy automatic diagnostics. A highperformance algorithm has been developed to calculate effective textural characteristics for medical image analysis. During the automatic image diagnostics, the following classes were distinguished: thin vessels, thick vessels, exudates and healthy areas. the mentioned algorithm's efficiency study was conducted with 500×500-1000×1000 pixels images using a 12×12 dimension window. the relationship between the developed algorithm's acceleration and data sizes was demonstrated. the study showed that the algorithm effectiveness can be depends of certain characteristics of the image, as its clarity, the shape of exudate zone, the variability of blood vessels, and the optic disc's location.

关键词： Graphics processing units Acceleration Medical diagnostic imaging Image segmentation Diabetes Retina

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