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Automatic tuning of master/worker applications

Automatic tuning of master/worker applications

引用

11th international Euro-Par conference

作者： Morajko, A César, E Caymes-Scutari, P Margalef, T Sorribes, J Luque, E Univ Autonoma Barcelona Dept Comp Sci Bellaterra 08193 Spain

ISBN: (纸本)3540287000

The Master/Worker paradigm is one of the most commonly used by parallel/distributed application developers. This paradigm is easy to understand and is fairly close to the abstract concept of a wide range of applications. However, to obtain adequate performance indexes, such a paradigm must be managed in a very precise way. There are certain features, such as data distribution or the number of workers, that must be tuned properly in order to obtain such performance indexes, and in most cases they cannot be tuned statically since they depend on the particular conditions of each execution. In this context, dynamic tuning seems to be a highly promising approach since it provides the capability to change the parameters during the execution of the application to improve performance. In this paper, we demonstrate the usage of a dynamic tuning environment that allows for adaptation of the number of workers based on a theoretical model of Master/Worker behavior. The results show that such an approach significantly improves the execution time when the application modifies its behavior during execution.

关键词： parallel processing systems

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SquirrelJoin: Network-aware distributed join processing with lazy partitioning 43

SquirrelJoin: Network-aware distributed join processing with...

引用

43rd international conference on Very Large Data Bases, VLDB 2017

作者： Rupprecht, Lukas Culhane, William Pietzuch, Peter Imperial College London United Kingdom

To execute distributed joins in parallel on compute clusters, systems partition and exchange data records between workers. With large datasets, workers spend a considerable amount of time transferring data over the network. When compute clusters are shared among multiple applications,workers must compete for network bandwidth with other applications. These variances in the available network bandwidth lead to network skew, which causes straggling workers to prolong the join completion time. We describe SquirrelJoin, a distributed join processing technique that uses lazy partitioning to adapt to transient network skew in clusters. Workers maintain in-memory lazy partitions to withhold a subset of records, i.e. not sending them immediately to other workers for processing. Lazy partitions are then assigned dynamically to other workers based on network conditions: each worker takes periodic throughput measurements to estimate its completion time, and lazy partitions are allocated as to minimise the join completion time. We implement SquirrelJoin as part of the Apache Flink distributed dataflow framework and show that, under transient network contention in a shared compute cluster, SquirrelJoin speeds up join completion times by up to 2.9× with only a small, fixed overhead. © 2017 VLDB Endowment.

关键词： Bandwidth

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distributed Deep Learning for Big Remote Sensing Data processing on Apache Spark: Geological Remote Sensing Interpretation as a Case Study 1

引用

7th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) international Joint conference on Web and Big Data

作者： Long, Ao Han, Wei Huang, Xiaohui Li, Jiabao Wang, Yuewei Chen, Jia China Univ Geosci Sch Comp Sci Wuhan Peoples R China Hubei Key Lab Intelligent Geoinformat Proc Wuhan Peoples R China

ISBN: (数字)9789819723034

ISBN: (纸本)9789819723027;9789819723034

The advent of sensor technologies has led to the sheer amount volume of remote sensing data containing fruitful spatial and spectral information. Insights into Earth's surface's objects are gained with the help of remote sensing processing methods and techniques and are applied in various applications. Recently, deep-learning-based methods are widely used in remote sensing data processing due to their ability to mine relationships using multiple layers. However, the time spent by deep learning-based methods with numerous layers and large parameter sizes in processing remote sensing data with "big data" characteristics is unacceptable in real-time applications. Combining deep learning with distributed computing namely distributed deep learning, has become an emerging topic in deep learning-based remote sensing processing. This paper first surveys recent methods and open-source solutions of Apache Spark-based distributed deep learning. Then, the pros and cons of each distributed deep learning open-source solution in processing remote sensing data are summarized. Later, the geological remote sensing interpretation is chosen as the case study by implementing the online training of a deep learning-based interpretation model called D-AMSDFNet for geological environments on Apache Spark. Experiments on Landsat 8 and Sentinel 2 satellite images investigate the effectiveness of the proposed D-AMSDFNet, which also indicates the promising development of distributed deep learning in processing remote sensing data.

关键词： Remote sensing distributed deep learning Apache spark distributed geological remote sensing interpretation

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Novel parallel givens QR decomposition implementation on VLIW architecture with efficient memory access for real time image processing applications 17

Novel parallel givens QR decomposition implementation on VLI...

引用

2nd international conference on Big Data Cloud and applications, BDCA 2017

作者： Najoui, Mohamed Hatim, Anas Belkouch, Said LGECOS Lab ENSA-Marrakech University of Cadi Ayyad Marrakech Morocco ENSA-Agadir Ibn Zohr University Agadir Morocco

ISBN: (纸本)9781450348522

Compressed Sensing (CS) methods impact is important in the health care systems. The acquisition and the processing speed of many medical imaging applications is highly improved using this technique. Orthogonal Matching Pursuit (OMP) is one of the widely used image reconstruction algorithms in CS techniques. Traditionally, OMP is divided on two main tasks: optimization stage and least square problem (LSP) resolution stage. The LSP resolution is the most complex and time consuming step in OMP. QRD is one of the main techniques used to solve the LSP in a very short time. However, the increasing image and video resolutions (UHDTV) makes the real time constraints very tight. In this paper, a novel implementation of QRD on VLIW DSP architecture with an efficient memory access approach is introduced. An instruction, data and loop levels parallelism (ILP, DLP and LLP) based Givens QRD kernel is designed. A robust memory access management based on intelligent loading/storing strategies and proficient data alignment techniques is adopted for the proposed parallel implementation. Therefore, memory read misses and CPU stalls are significantly diminished. As a final result the processing time is greatly reduced. The proposed scheme is implemented on C6678 DSP and reaches 2.22 GFLOPS. The performances achieved are 7.4, 3.83 and 2 times faster than standard Givens, Texas Instrument optimized QRD and state of the art, respectively. © 2017 Association for Computing Machinery.

关键词： Compressed sensing

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Design and Optimization of Nonlinear Dimensionality Reduction Algorithm for Hyperspectral Images on Heterogeneous System 10

Design and Optimization of Nonlinear Dimensionality Reductio...

引用

EEE international conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

作者： Zhou, Haifang Gao, Chang Liu, Xunyun Natl Univ Def Technol Sch Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781538630662

Hyperspectral remote sensing image data has been widely used in a variety of applications due to its continuous spectrum and high spectral resolution. However, reducing huge dimensions with high data relevance is time-consuming, and parallel processing is required to accelerate this process. In this paper, we studied the KPCA (Kernel Principal Component Analysis), a nonlinear dimensionality reduction method, and proposed a parallel KPCA algorithm (KPCA_G) based on the CPU/GPU heterogeneous system. The experimental results prove that our KPCA_G algorithm achieves up to 173x speedup over the original serial KPCA. Moreover, to tackle the limitation of insufficient memory caused by the reduction of large-scale hyperspectral data dimension, we exploit the intra-node parallelization using multi-core CPUs and many-core GPUs to improve the parallel hierarchy of distributed-storage KPCA. Finally, we designed and implemented a multilevel hybrid parallel KPCA algorithm that achieves 2.56 similar to 9.03 times speedup compared to the traditional coarse-grained parallel KPCA method on MPI.

关键词： nonlinear dimensionality reduction hyperspectral image GPU heterogeneous system multilevel parallel

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applications of Artificial Intelligence X: Knowledge-Based Systems

Applications of Artificial Intelligence X: Knowledge-Based S...

引用

applications of Artificial Intelligence X: Knowledge-Based Systems

ISBN: (纸本)0819408727

The conference materials contain 43 papers. Knowledge-based systems, model-based diagnosis, parallel architectures/distributed artificial intelligence, machine learning, natural language processing, model-based reasoning, applications of genetic algorithms, and engineering applications of artificial intelligence are the main topics covered.

关键词： Expert Systems

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Tajo: A distributed Data Warehouse System on Large Clusters

Tajo: A Distributed Data Warehouse System on Large Clusters

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29th IEEE international conference on Data Engineering (ICDE)

作者： Choi, Hyunsik Son, Jihoon Yang, Haemi Ryu, Hyoseok Lim, Byungnam Kim, Soohyung Chung, Yon Dohn Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781467349093;9781467349086

The increasing volumes of relational data let us find an alternative to cope with them. Recently, several hybrid approaches (e. g., HadoopDB and Hive) between parallel databases and Hadoop have been introduced to the database community. Although these hybrid approaches have gained wide popularity, they cannot avoid the choice of suboptimal execution strategies. We believe that this problem is caused by the inherent limits of their architectures. In this demo, we present Tajo, a relational, distributed data warehouse system on shared-nothing clusters. It uses Hadoop distributed File System (HDFS) as the storage layer and has its own query execution engine that we have developed instead of the MapReduce framework. A Tajo cluster consists of one master node and a number of workers across cluster nodes. The master is mainly responsible for query planning and the coordinator for workers. The master divides a query into small tasks and disseminates them to workers. Each worker has a local query engine that executes a directed acyclic graph of physical operators. A DAG of operators can take two or more input sources and be pipelined within the local query engine. In addition, Tajo can control distributed data flow more flexible than that of MapReduce and supports indexing techniques. By combining these features, Tajo can employ more optimized and efficient query processing, including the existing methods that have been studied in the traditional database research areas. To give a deep understanding of the Tajo architecture and behavior during query processing, the demonstration will allow users to submit TPC-H queries to 32 Tajo cluster nodes. The web-based user interface will show (1) how the submitted queries are planned, (2) how the query are distributed across nodes, (3) the cluster and node status, and (4) the detail of relations and their physical information. Also, we provide the performance evaluation of Tajo compared with Hive.

关键词： Query processing

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Developing a new modified Algorithms for Digital Image Sampling and Quantization in Serial and parallel Image processing

Developing a new modified Algorithms for Digital Image Sampl...

引用

2023 IEEE international conference on Paradigm Shift in Information Technologies with Innovative applications in Global Scenario, ICPSITIAGS 2023

作者： Jayashankari, J. Shalini, V. Pandey, Megha Swetha, S. Sharma, Mohit Kumar Khurana, Jatin Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology U P India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

The improvement of a new changed algorithms for virtual photograph sampling and quantization in serial and parallel image processing is proposed. The set of rules improves upon existing methods with the aid of combining picture sampling, quantization, and parallelization techniques with an efficient, in-region serial photograph processing framework. The ensuing algorithm has several advantages, which includes more accurate and steady photograph output, quicker convergence fees, and flexibility in process scheduling. The set of rules is nicely-suitable to parallel computing environments, together with clusters and multi-center structures. Via widespread experimentation and simulation, the proposed set of rules is located to be effective and to outperform present techniques. Moreover, the performance of the proposed set of rules changed into further confirmed thru a comparison to present methods, and the parameters of the version had been tuned in order to optimize overall performance. © 2023 IEEE.

关键词： Image enhancement

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Hierarchical Agglomerative Clustering Using Graphics Processor with Compute Unified Device Architecture

Hierarchical Agglomerative Clustering Using Graphics Process...

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international conference on Signal processing Systems (ICSPS 2009)

作者： Shalom, S. A. Arial Dash, Manoranjan Tue, Minh Wilson, Nithin Nanyang Technol Univ Sch Comp Engn 50 Nanyang Ave Singapore Singapore NUS High Sch Math & Sci Singapore Singapore

ISBN: (纸本)9780769536545

We explore the use of today's high-end Graphics processing units on desktops to perform hierarchical agglomerative clustering with the Compute Unified Device Architecture - CUDA of NVIDIA. Although the advancement in graphics cards has made the gaming industry to flourish, there is a lot more to be gained the field of scientific computing, high performance computing and their applications. Previous works have illustrated considerable speed gains on computing pair wise Euclidean distances between vectors, which is the fundamental operation in hierarchical clustering. We have used CUDA to implement the complete hierarchical agglomerative clustering algorithm and show almost double the speed gain using much cheaper desk top graphics card. In this paper we briefly explain the highly parallel and internally distributed programming structure of CUDA. We explore CUDA capabilities and propose methods to efficiently handle data within the graphics hardware for data intense, data independent, iterative or repetitive general-purpose algorithms such as the hierarchical clustering. We achieved results with speed gains of about 30 to 65 times over the CPU implementation using micro array gene expressions.

关键词： CUDA hierarchical clustering high performance computing GPGPU acceleration of computations parallel computing

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Real-time textured object recognition on distributed systems 3rd

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3rd international Computer Science conference (ICSC 95) - Image Analysis applications and Computer Graphics

作者： You, J Zhu, WP Cohen, HA Pissaloux, E LA TROBE UNIV DEPT COMP SCI & COMP ENGN BUNDOORA VIC 3083 AUSTRALIA UNIV PARIS 11 INST ELECTR FONDAMENTALE F-91405 ORSAY FRANCE

ISBN: (纸本)3540606971

This paper presents the development of a real-time system for recognition of textured objects. In contrast to current approaches which mostly rely on specialized multiprocessor architectures for fast processing, we use a distributed network architecture to support parallelism and attain real-time performance. In this paper, a new approach to image matching is proposed as the basis of object localization and positioning, which involves dynamic texture feature extraction and hierarchical image matching. A mask based stochastic method is introduced to extract feature points for matching. Our experimental results demonstrate that the combination of texture feature extraction and interesting point detection provides a better solution to the search of the best matching between two textured images. Furthermore, such an algorithm is implemented on a low cost heterogeneous PVM (parallel Virtual Machine) network to speed up the processing without specific hardware requirements.

关键词： object recognition image matching feature extraction interesting points distance transform parallel processing distributed systems

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