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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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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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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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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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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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CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochastic Gradient Descent over Streaming Data and Its Asynchronous parallel Variants 20th

CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochas...

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

作者： Peng, Yaqiong Fei, Haiqiang Li, Lun Ding, Zhenquan Hao, Zhiyu Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Stochastic Gradient Descent (SGD) is widely used to train a machine learning model over large datasets, yet its slow convergence rate can be a bottleneck. As a remarkable family of variance reduction techniques, memory algorithms such as SAG and SAGA have been proposed to accelerate the convergence rate of SGD. However, these algorithms need to store per training data point corrections in memory. the unlimited space usage feature is impractical for modern large-scale applications, especially over data points that arrive over time (referred to as streaming data in this paper). To overcome this weakness, this paper investigates the methods that bound the space usage in the state-of-the-art family of variance-reduced stochastic gradient descent over streaming data, and presents CHEAPS2AGA. At each step of updating the model, the key idea of CHEAPS2AGA is always reserving N random data points as samples, while re-using information about past stochastic gradients across all the observed data points with limited space usage. In addition, training an accurate model over streaming data requires the algorithm to be time-efficient. To accelerate the model training phase, CHEAPS2AGA embraces a lock-free data structure to insert new data points and remove unused data points in parallel, and updates the model parameters without using any locking. We conduct comprehensive experiments to compare CHEAPS2AGA to prior related algorithms suited for streaming data. the experimental results demonstrate the practical competitiveness of CHEAPS2AGA in terms of scalability and accuracy.

关键词： Machine learning Stochastic gradient descent Streaming data parallel algorithm Multicore

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Inline Double Layer Depth Estimation with Transparent Materials 42nd

Inline Double Layer Depth Estimation with Transparent Materi...

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42nd German conference on Pattern Recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020

作者： Kopf, Christian Pock, thomas Blaschitz, Bernhard Štolc, Svorad Graz University of Technology Graz Austria AIT Austrian Institute of Technology GmbH Vienna Austria Photoneo s.r.o. Bratislava Slovakia

ISBN: (纸本)9783030712778

3D depth computation from stereo data has been one of the most researched topics in computer vision. While state-of-art approaches have flourished over time, reconstruction of transparent materials is still considered an open problem. Based on 3D light field data we propose a method to obtain smooth and consistent double-layer estimates of scenes with transparent materials. Our novel approach robustly combines estimates from models with different layer hypotheses in a cost volume with subsequent minimization of a joint second order TGV energy on two depth layers. Additionally we showcase the results of our approach on objects from common inspection use-cases in an industrial setting and compare our work to related methods. © 2021, Springer Nature Switzerland AG.

关键词： Stereo image processing

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Performance Analysis of Continuous Binary Data processing using Distributed Databases within Stream processing Environments 10

Performance Analysis of Continuous Binary Data Processing us...

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10th international conference on Cloud Computing and Services Science (CLOSER)

作者： Weissbach, Manuel Hilbert, Hannes Springer, thomas Tech Univ Dresden Fac Comp Sci Dresden Germany

ISBN: (纸本)9789897584244

Big data applications must process increasingly large amounts of data within ever shorter time. Often a stream processing engine (SPE) is used to process incoming data with minimal latency. While these engines are designed to process data quickly, they are not made to persist and manage it. thus, databases are still integrated into streaming architectures, which often becomes a performance bottleneck. To overcome this issue and achieve maximum performance, all system components used must be examined in terms of their throughput and latency, and how well they interact with each other. Several authors have already analyzed the performance of popular distributed database systems. While doing so, we focus on the interaction between the SPEs and the databases, as we assume that stream processing leads to changes in the access patterns to the databases. Moreover, our main focus is on the efficient storing and loading of binary data objects rather than typed data, since in our use cases the actual data analysis is not to be performed by the database, but by the SPE. We've benchmarked common databases within streaming environments to determine which software combination is best suited for these requirements. Our results show that the database performance differs significantly depending on the access pattern used and that different software combinations lead to substantial performance differences. Depending on the access pattern, Cassandra, MongoDB and PostgreSQL achieved the best throughputs, which were mostly the highest when Apache Flink was used.

关键词： Stream processing Benchmarking Database Benchmark Big Data Performance

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