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ParaCA: A Speculative parallel Crawling Approach on Apache Spark 20th

ParaCA: A Speculative Parallel Crawling Approach on Apache S...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Li, Yuxiang Zhang, Zhiyong Niu, DanMei Jing, Junchang Henan Univ Sci & Technol Luoyang 471023 Henan Peoples R China Henan Int Joint Lab Cyberspace Secur Applicat Luoyang 471023 Henan Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

the World Wide Web today is growing at a phenomenal rate. the crawling approach is of vital importance to improve the efficiency of crawling the web. the existing sequent crawling algorithms are mostly time consuming and do not support large data well. In order to improve parallelism and efficiency of crawler on distributed network environments, based on the software thread-level speculation technique, this paper raises a speculative parallel crawler approach (ParaCA) on Apache Spark. By analyzing the process of web crawler, the ParaCA firstly hires a function to divide a crawling process into several sub-processes which can be implemented independently and then spawns a number of threads to speculatively crawl in parallel. At last, the speculative results are merged to form the final outcome. Comparing with the conventional parallel approach on multicore platform, ParaCA is very efficiency and obtains a high parallelism degree by making the best of the resources of the cluster. Experiments show that the proposed approach could achieve a significant speedup improvement with compare to the traditional approach in average. In addition, with the growing number of working nodes, the execution time decreases gradually, and the speedup scales linearly. the results indicate that the crawling efficiency can be significantly enhanced by adopting this speculative parallel algorithm.

关键词： Crawling approach Speculative multithreading Apache Spark

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High-Level Stream and Data parallelism in C++ for Multi-Cores 21

High-Level Stream and Data Parallelism in C++ for Multi-Core...

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25th Brazilian Symposium on Programming Languages, SBLP 2021, held in conjunction with the Brazilian conference on Software: theory and Practice, CBSoft 2021

作者： Loff, Junior Hoffman, Renato B. Griebler, Dalvan Fernandes, Luiz G. PUCRS Brazil

ISBN: (纸本)9781450390620

Stream processing applications have seen an increasing demand with the increased availability of sensors, IoT devices, and user data. Modern systems can generate millions of data items per day that require to be processed timely. To deal with this demand, application programmers must consider parallelism to exploit the maximum performance of the underlying hardware resources. However, parallel programming is often difficult and error-prone, because programmers must deal with low-level system and architecture details. In this work, we introduce a new strategy for automatic data-parallel code generation in C++ targeting multi-core architectures. this strategy was integrated with an annotation-based parallel programming abstraction named SPar. We have increased SPar's expressiveness for supporting stream and data parallelism, and their arbitrary composition. therefore, we added two new attributes to its language and improved the compiler parallel code generation. We conducted a set of experiments on different stream and data-parallel applications to assess the efficiency of our solution. the results showed that the new SPar version obtained similar performance with respect to handwritten parallelizations. Moreover, the new SPar version is able to achieve up to 74.9x better performance with respect to the original ones due to this work. © 2021 ACM.

关键词： parallel programming

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iPUG for Multiple Graphcore IPUs: Optimizing Performance and Scalability of parallel Breadth-First Search

iPUG for Multiple Graphcore IPUs: Optimizing Performance and...

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international conference on High Performance Computing

作者： Luk Burchard Xing Cai Johannes Langguth Simula Research Laboratory Norway Simula Research Laboratory Norway University of Oslo Norway BI Norwegian Business School Norway

ISBN: (纸本)9781665410175

parallel graph algorithms have become one of the principal applications of high-performance computing besides numerical simulations and machine learning workloads. However, due to their highly unstructured nature, graph algorithms remain extremely challenging for most parallel systems, with large gaps between observed performance and theoretical limits. Further-more, most mainstream architectures rely heavily on single instruction multiple data (SIMD) processing for high floating-point rates, which is not beneficial for graph processing which instead requires high memory bandwidth, low memory latency, and efficient processing of unstructured data. On the other hand, we are currently observing an explosion of new hardware architectures, many of which are adapted to specific purposes and diverge from traditional designs. A notable example is the Graphcore Intelligence processing Unit (IPU), which is developed to meet the needs of upcoming machine intelligence applications. Its design eschews the traditional cache hierarchy, relying on SRAM as its main memory instead. the result is an extremely high-bandwidth, low-latency memory at the cost of capacity. In addition, the IPU consists of a large number of independent cores, allowing for true multiple instruction multiple data (MIMD) processing. Together, these features suggest that such a processor is well suited for graph processing. We test the limits of graph processing on multiple IPUs by implementing a low-level, high-performance code for breadth-first search (BFS), following the specifications of Graph500, the most widely used benchmark for parallel graph processing. Despite the simplicity of the BFS algorithm, implementing efficient parallel codes for it has proven to be a challenging task in the past. We show that our implementation scales well on a system with 8 IPUs and attains roughly twice the performance of an equal number of NVIDIA V100 GPUs using state-of-the-art CUDA code.

关键词： Codes Machine learning algorithms Scalability Memory management Graphics processing units Random access memory Benchmark testing

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Selective Velocity Distributed Indexing for Continuously Moving Objects Model 19th

Selective Velocity Distributed Indexing for Continuously Mov...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Bareche, Imene Xia, Ying Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9783030389611;9783030389604

the widespread of GPS embedded devices has lead to a ubiquitous location dependent services, based on the generated real-time location data. this introduced the notion of continuous querying, and with the aid of advanced indexing techniques several complex query types could be supported. However the efficient querying and manipulation of such highly dynamic data is not trivial, processing factors of crucial importance should be carefully thought out such as accuracy and scalability. In this study we focus on Continuous KNN (CKNN) queries processing, one of the most well-know spatio-temporal queries over large scale of continuously moving objects. In this paper we provide an overview of CKNN queries and related challenges, as well as an outline of proposed works in the literature and their limitations, before getting to our contribution proposal. We propose a novel indexing approach model for CKNN querying, namely VS-TIMO. the proposed structure is based on a selective velocity partitioning method, since we have different objects with varying speeds. Our structure base unit is a comprised of a non overlapping R-tree and a two dimensions grid. In order to enhance performances, we design a compact multi-layer index structure on a distributed setting, and propose a CKNN search algorithm for accurate results using a candidate cells identification process. We provide a comprehensive vision of our indexing model and the adopted querying technique.

关键词： Continuous KNN querying Moving objects indexing Distributed spatio-temporal indices Velocity partitioning based index parallel processing

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PRIAG: Proximal Reweighted Incremental Aggregated Gradient Algorithm for Distributed Optimizations 20th

PRIAG: Proximal Reweighted Incremental Aggregated Gradient A...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Deng, Xiaoge Sun, Tao Liu, Feng Huang, Feng Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc PDL Changsha 410073 Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Large-scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e., algorithms that leverage multiple workers for training. However, collecting the information from all workers in every iteration is sometimes expensive or even prohibitive. In this paper, we propose an iterative algorithm called proximal reweighted incremental aggregated gradient (PRIAG) for solving a class of nonconvex and nonsmooth problems, which are ubiquitous in machine learning tasks and distributed optimization problems. In each iteration, this algorithm just needs the information from one worker due to the incremental aggregated method. Combined with the reweighted technique, we only require an easy-to-calculate proximal operator to deal with the nonconvex and nonsmooth properties. Using the Lyapunov function analysis method, we prove that the PRIAG algorithm is convergent under some mild assumptions. We apply this approach to nonconvex nonsmooth problems and distributed optimization tasks. Numerical experiments on both synthetic and real data sets show that our algorithm can achieve comparative learning performance, but more efficiently, compared with previous nonconvex solvers.

关键词： Distributed optimization Incremental method Proximal operator Nonconvex optimization Iteratively reweighted algorithm

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DOS-GAN: A Distributed Over-Sampling Method Based on Generative Adversarial Networks for Distributed Class-Imbalance Learning 20th

DOS-GAN: A Distributed Over-Sampling Method Based on Generat...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Guan, Hongtao Ma, Xingkong Shen, Siqi Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9783030602482;9783030602475

Class-imbalance Learning is one of the hot research issues in machine learning. In the practical application of distributed class-imbalance learning, data continues to arrive, which often leads to classimbalance situations. the imbalance problem in the distributed scenario is particular: the imbalanced state of different nodes may be complementary. the imbalanced states of different nodes may be complementary. Using this complementary relationship to do oversampling to change the imbalanced state is a valuable method. However, the data island limits data sharing in this case between the nodes. To this end, we propose DOS-GAN, which can take turns to use the data of one same class data on multiple nodes to train the global GAN model, and then use this GAN generator to oversampling the class without the original data being exchanged. Extensive experiments confirm that DOS-GAN outperforms the combination of traditional methods and achieves classification accuracy closes to the method of data aggregating.

关键词： Class-imbalance learning Distributed learning Generative adversarial networks Data isolated islands

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FastDRC: Fast and Scalable Genome Compression Based on Distributed and parallel processing 19th

FastDRC: Fast and Scalable Genome Compression Based on Distr...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Ji, Yimu Fang, Houzhi Yao, Haichang He, Jing Chen, Shuai Li, Kui Liu, Shangdong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Nanjing Inst Ind Technol Sch Comp & Software Nanjing 210023 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Nanjing Univ Posts & Telecommun Inst High Performance Comp & Big Data Nanjing 210003 Peoples R China Nanjing Ctr HPC China Nanjing 210003 Peoples R China Jiangsu HPC & Intelligent Proc Engineer Res Ctr Nanjing 210003 Peoples R China

ISBN: (纸本)9783030389611;9783030389604

With the advent of next-generation sequencing technology, sequencing costs have fallen sharply compared to the previous sequencing technologies. Genomic big data has become the significant big data application. In the face of growing genomic data, its storage and migration face enormous challenges. therefore, researchers have proposed a variety of genome compression algorithms, but these algorithms cannot meet the processing requirements for large amount of biological data and high processing speed. this manuscript proposes a parallel and distributed referential genome compression algorithm-Fast Distributed Referential Compression (FastDRC). this algorithm compresses a large number of genomic sequences in parallel under the Apache Hadoop distributed computing framework. Experiments show that the compression efficiency of the FastDRC is greatly improved when it compresses large quantities of genomic data. Moreover, FastDRC leads to the only distributed computing method known to us in the field of genome compression. the source code for FastDRC can be obtained from this link: https://***/GhostCCCathenry/FastDRC.

关键词： Genome compression Distributed processing Apache Hadoop FASTA

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Machine Learning Migration for Efficient Near-Data processing

Machine Learning Migration for Efficient Near-Data Processin...

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Euromicro conference on parallel, Distributed and Network-Based processing

作者： Aline S. Cordeiro Sairo R. dos Santos Francis B. Moreira Paulo C. Santos Luigi Carro Marco A. Z. Alves Federal University of Paraná Curitiba Brazil Federal Rural University of Semi-arid Angicos Brazil Informatics Institute Federal University of Rio Grande do Sul Porto Alegre Brazil

Machine Learning (ML) rises as a highly useful tool to analyze the vast amount of data generated in every field of science nowadays. Simultaneously, data movement inside computer systems gains more focus due to its high impact an time and energy consumption. In this context, the Near-Data processing (NDP) architectures emerged as a prominent solution to increasing data by drastically reducing the required amount of data movement For NDP, we see three main approaches, Application-Specific Integrated Circuits (ASICs), full Central processing Units (CPUs) and Graphics processing Units (GPIIs), or vector units integration. However, previous work considered only ASICs, CPUs and GPUs when executing ML algorithms inside the memory. In this paper, we present an approach to execute ML algorithms near-data, using a general-purpose vector architecture and applying near-data parallelism to kernels from KNN, MEP, and CNN algorithms. To facilitate this process, we also present an NDP intrinsics library to ease the evaluation and debugging tasks. Our results show speedups up to fox for KNN, 11× for MLP, and 3× for convolution when processing near-data compared to a high-performance ×86 baseline.

关键词： Integrated circuits Machine learning algorithms Graphics processing units Machine learning Computer architecture Tools parallel processing

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Proceedings of the 34th ACM international conference on Supercomputing, ICS 2020

Proceedings of the 34th ACM International Conference on Supe...

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34th ACM international conference on Supercomputing, ICS 2020

ISBN: (纸本)9781450379830

the proceedings contain 40 papers. the topics discussed include: a scalable framework for solving fractional diffusion equations;fast distributed bandits for online recommendation systems;wavefront parallelization of recurrent neural networks on multi-core architectures;NV-group: link-efficient reduction for distributed deep learning on modern dense GPU systems;a coordinate-oblivious index for high-dimensional distance similarity searches on the GPU;V-combiner: speeding-up iterative graph processing on a shared-memory platform with vertex merging;efficient parallel algorithms for betweenness- and closeness-centrality in dynamic graphs;parallelizing pruned landmark labeling: dealing with dependencies in graph algorithms;Graptor: efficient pull and push style vectorized graph processing;and SB-fetch: synchronization aware hardware prefetching for chip multiprocessors.

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Multi-scaled Non-local Means parallel Filters for Medical Image Denoising 20th

Multi-scaled Non-local Means Parallel Filters for Medical Im...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Rabbouch, Hana Ben Messaoud, Othman Saadaoui, Foued Fac Sci Lab LR18ES15 Algebre Theorie Nombres & Anal Nonli Monastir 5019 Tunisia Compagnie Phosphates Gafsa CPG Cite Bayech 2100 Gafsa Tunisia King Abdulaziz Univ POB 80203 Jeddah 21589 Saudi Arabia

ISBN: (纸本)9783030602451;9783030602444

In recent years, there has been an increased interest in denoising techniques that are applicable in various medical imaging fields. the extraordinary development of the denoising area is no doubt due to the ever expanding and successful computing technology, but also to the emergence of the multi-resolution analysis (MRA) on both mathematical and algorithmic levels. However, many denoising techniques still remain ineffective in dealing with certain types of noise. Other methods can be too expensive, given their nested and complicated structure. therefore, in this paper, A new multi-scale parallel denoising paradigm is defined and tested. A comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). the comparison is carried out in this framework of multi-scaled parallel denoising, where a Non-Local Means (NLM) filter is implemented and adjusted scale-by-scale to a sample of X-ray benchmark images. Some state-of-the-art denoising methods were also used in the evaluation. the numerical results proved the effectiveness of the multi-scaled parallel denoising in terms of accuracy and speed of convergence, especially when the NLM filtering is coupled with the EMD. this shows a bright future for their medical use in the next few years.

关键词： Medical imaging Non-local means Multi-scaled denoising Empirical mode decomposition Wavelets

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