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20th international conference on Algorithms and Architectures for parallel processing, ICA3PP 2020

20th International Conference on Algorithms and Architecture...

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20th international conference on Algorithms and Architectures for parallel processing, ICA3PP 2020

ISBN: (纸本)9783030602444

The proceedings contain 145 papers. The special focus in this conference is on Algorithms and Architectures for parallel processing. The topics include: An Improved Heterogeneous Dynamic List Schedule Algorithm;fastThetaJoin: An Optimization on Multi-way Data Stream θ -join with Range Constraints;a distributed Framework for Online Stream Data Clustering;end-System Aware Large File Transfer Solution for Rich Media applications over 5G Mobile Networks;broad Learning System with Proportional-Integral-Differential Gradient Descent;Accelerating De Novo Assembler WTDBG2 on Commodity Servers;Typing Everywhere with an EMG Keyboard: A Novel Myo Armband-Based HCI Tool;accelerating Pattern Matching on Intel Xeon Phi Processors;Redistributing and Optimizing High-Resolution Ocean Model POP2 to Million Sunway Cores;Efficient Sorting and Join on NVM-Based Hybrid Memory;performance Optimization for Feature Extraction Section of DeepChem;principal Component Analysis for Fingerprint Positioning;priority Based Service Placement Strategy in Heterogeneous Mobile Edge Computing;VTC: A Scheduling Framework Between Soft Real-Time and Hard Real-Time on Multimedia OS;A BSP Based Approach for NFAs Intersection;tight Bound of parallel Request Latency for Erasure-Coded distributed Storage System;High-Performance Simulations on GPUs Using Adaptive Time Steps;Performance Modeling of Stencil Computation on SW26010 Processors;Optimizing B+ -Tree Searches on Coupled CPU-GPU Architectures;OCVM: Optimizing the Isolation of Virtual Machines with Open-Channel SSDs;parallel SCC Detection Based on Reusing Warps and Coloring Partitions on GPUs;CANRT: A Client-Active NVM-Based Radix Tree for Fast Remote Access;distributed and parallel Ensemble Classification for Big Data Based on Kullback-Leibler Random Sample Partition;SWAF: A distributed Solar WSN Adaptive Framework.

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6th international European conference on parallel Computing, Euro-Par 2000

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6th international European conference on parallel Computing, Euro-Par 2000

ISBN: (纸本)9783540679561

The proceedings contain 189 papers. The special focus in this conference is on Support Tools, Evironments, Performance Evaluation, Prediction and Load Balancing. The topics include: Grid-based asynchronous migration of execution context in java virtual machines;visualization and computational steering in heterogeneous computing environments;a web-based finite element meshes partitioner and load balancer;an infrastructure for the construction of memory hierarchy analysis tools;on combining computational differentiation and toolkits for parallel scientific computing;generating parallel program frameworks from parallel design patterns;a callgraph-based search strategy for automated performance diagnosis;automatic performance analysis of MPI applications based on event traces;an extensible environment for visualizing multi-threaded programs executions;a statistical-empirical hybrid approach to hierarchical memory analysis;use of performance technology for the management of distributed systems;delay behavior in domain decomposition applications;automating performance analysis from UML design patterns;integrating automatic techniques in a performance analysis session;the specific cluster operation and performance evaluation benchmark suite;a fast and accurate approach to analyze cache memory behavior;performance prediction of a NAS benchmark program with chronosmix environment;a hierarchical approach to irregular problems;the impact of migration on parallel job scheduling for distributed systems;exploiting knowledge of temporal behaviour in parallel programs for improving distributed mapping;preemptive task scheduling for distributed systems and scheduling trees with large communication delays on two identical processors.

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Restructuring and Implementations of 2D Matrix Transpose Algorithm Using SSE4 Vector Instructions 1

Restructuring and Implementations of 2D Matrix Transpose Alg...

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international conference on Applied Research in Computer Science and Engineering (ICAR)

作者： Zekri, Ahmed S. Beirut Arab Univ Fac Sci Dept Math & Comp Sci Beirut Lebanon

ISBN: (纸本)9781467385435

Current general-purpose processors are augmented with vector instructions that can process many elements of matrices and vectors in parallel. Transposing a matrix in-place is a main kernel operation required by many scientific and engineering applications to shuttle data before, during, or after processing. This operation increases the traffic on the memory bus and hence clever techniques such as blocking are required to enhance the performance. In this paper, we present an enhanced version of a previously published algorithm for transposing a matrix on a two-dimensional processor arrays. We restructured this algorithm to fit the one-dimensional vector register architecture augmented to general-purpose CPUs. We implemented the new vector algorithm using Intel SSE4 vector instruction set and compare its performance with the standard sequential algorithm in addition to an already employed implementation of Ekhlundh's algorithm. We also studied the automatic compiler optimizations and their effect on the vectorization of the algorithm. The best of our implementations showed a maximum speedup of 1.6 compared with the sequential algorithm, and an almost equal performance compared with Eklundh's algorithm implementation.

关键词： instruction sets mathematics computing matrix algebra optimising compilers parallel processing 2D matrix transpose algorithm Ekhlundh algorithm Intel SSE4 vector instruction set automatic compiler optimization general-purpose CPU general-purpose processor memory bus one-dimensional vector register architecture standard sequential algorithm two-dimensional processor arrays Kernel Multicore processing parallel processing Program processors Registers Signal processing algorithms Yttrium parallel processing (COMPUTERS) instruction sets mathematics computing Program processors Signal processing algorithms Registers matrix algebra Rare earths yttrium sequential algorithm Kernel general processor

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20th international conference on Algorithms and Architectures for parallel processing, ICA3PP 2020

20th International Conference on Algorithms and Architecture...

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20th international conference on Algorithms and Architectures for parallel processing, ICA3PP 2020

ISBN: (纸本)9783030602475

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PRISMA-MDE - Information visualization environment for multiple display

PRISMA-MDE - Information visualization environment for multi...

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2013 17th international conference on Information Visualisation, IV 2013

作者： Franco, Roberto Yuri Da Silva Meiguins, Bianchi Serique Meiguins, Aruanda Simoes Postgraduate Program in Computer Science - PPGCC Federal University of Pará Brazil

ISBN: (纸本)9780769550497

This paper aims to minimize the problems encountered in the handling of large amounts of visual data, proposing a scalable environment for distributed applications and information visualization (IV) with multiple coordinated views. The benefits of this architecture are improving the processing power for visualization tools, reduce the rendering time of visualization techniques, reducing the problem of occlusion and aggregation, among others. We will be presented usability testing, and comparative testing of performance (rendering time, memory usage, and processor) with an application of IV in a single desktop. © 2013 IEEE.

关键词： distributed computer systems

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GridSys- A state of the art grid framework 3

GridSys- A state of the art grid framework

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3rd international conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Shenoy, Sanath John, Nimmy Eeratta, Raghavendra Nair, Ranjith Siemens Technol & Serv Pvt Ltd Bangalore Karnataka India

ISBN: (纸本)9781479930807

Today many applications are developed using distributed technologies such as cluster, cloud and grid computing. These applications demand more resources for computation and storage. They demand flexible scaling and improved performance. Application now days can make use of multiple nodes (machines) to get the tasks completed. In this paper we discuss the, implementation details of a grid computing framework known as GridSys. This framework provides a fast and easy way to program a grid. It can easily help the application break the problem to compute intensive tasks. The framework distributes these tasks to different nodes of the grid efficiently and easily aggregate the results of these tasks provide fault tolerance and reliability.

关键词： Grid Computing Framework parallel processing

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Improving parallel processing of Matrix-Based Similarity Measures on Modern GPUs 8th

Improving Parallel Processing of Matrix-Based Similarity Mea...

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8th international conference on Similarity Search and applications (SISAP)

作者： Krulis, Martin Bednarek, David Brabec, Michal Charles Univ Prague Fac Math & Phys Parallel Architectures Algorithms Applicat Res Gr Malostranske Nam 25 Prague Czech Republic

ISBN: (纸本)9783319250878;9783319250861

Dynamic programming techniques are well-established and employed by various practical algorithms which are used as similarity measures, for instance the edit-distance algorithm or the dynamic time warping algorithm. These algorithms usually operate in iteration-based fashion where new values are computed from values of the previous iteration, thus they cannot be processed by simple data-parallel approaches. In this paper, we propose a way how to utilize computational power of massively parallel GPUs to compute dynamic programming algorithms effectively and efficiently. We address both the problem of computing one distance on large inputs concurrently and the problem of computing large number of distances simultaneously (e.g., when a similarity query is being resolved).

关键词： GPU CUDA Dynamic programming Edit distance Dynamic time warping

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Scale Drone Mapping on K8S: Auto-scale Drone Imagery processing on Kubernetes-orchestrated On-premise Cloud-computing Platform 6

Scale Drone Mapping on K8S: Auto-scale Drone Imagery Process...

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6th international conference on Geographical Information Systems Theory, applications and Management (GISTAM)

作者： Vithlani, Hemang Narendra Dogotari, Marcel Lam, Olee Hoi Ying Pruem, Moritz Melville, Bethany Zimmer, Frank Becker, Rolf Rhine Waal Univ Appl Sci Fac Commun & Environm D-47475 Kamp Lintfort Germany Astron Environm Serv 129 Royal St East Perth WA 6004 Australia

ISBN: (纸本)9789897584251

Aerial images acquired using drone-based imaging sensors can be processed by photogrammetry toolkits to create geometrically corrected 2D orthophoto and/or 3D models. This is a crucial step for many of the ever-evolving civil applications of drones such as precision agriculture and surveying. Nevertheless, limited computational resources become bottleneck in providing these results quickly. Cloud computing helps in such scenarios because of its value-added features, namely virtualization, elasticity, high performance and distributed computing for the web-based image processing. The containerization approach plays a vital role in cloud computing by providing operational efficiency. Container orchestration engine, Kubernetes, not only provides template-based or GUI-based service deployment but also better monitoring, log querying and autoscaling. The present work displays a scalable photogrammetry service, deployed on a Kubernetes-orchestrated on-premise cluster. This reference implementation on Kubernetes enables the parallel processing of large datasets in less time than a single computer using the free and open-source toolkit OpenDroneMap.

关键词： Drone Imagery Opendronemap Photogrammetry Open-source Kubernetes Cloud Computing

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A New Scalable Approach for distributed Metadata in HPC 16th

A New Scalable Approach for Distributed Metadata in HPC

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16th international conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Rodriguez-Quintana, Cristina Diaz, Antonio F. Ortega, Julio Palacios, Raul H. Ortiz, Andres Univ Granada Dept Comp Architecture & Technol Granada Spain Univ Malaga Commun Engn Dept Malaga Spain

ISBN: (纸本)9783319495835;9783319495828

In the last years not only a growth of data-intensive storage has been observed, but also compute-intensive workloads need a high computing power and high parallelism with good performance and great scalability. Many distributed filesystem have focused in how to distribute data across multiple processing nodes, but one of the main problem to solve is the management of the ever-greater number of metadata requests. In fact, some studies have identified that an optimized metadata management is a key factor to achieve good performance. applications in high performance computing usually require filesystems able to provide a huge amount of operations per second to achieve the required level of performance. Although the metadata storage is smaller than data storage, metadata operations consume large CPU cycles, so a single metadata server cannot be longer sufficient. In this paper we define a completely distributed method that provides efficient metadata management and seamlessly adapts to general purpose and scientific computing filesystem workloads. The throughput performance is measured by a metadata benchmark and compared with several distributed filesystems. The results show great scalability in creating operations on a single directory accessed by multiple clients.

关键词： distributed filesystems HPC Metadata management

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Scalable Repositories for Virtual Clusters

Scalable Repositories for Virtual Clusters

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15th international Euro-Par conference on parallel Computing

作者： Anedda, Paolo Leo, Simone Gaggero, Massimo Zanetti, Gianluigi CRS4 Distributed Comp Grp Polaris Pula Italy

ISBN: (纸本)9783642141218

For a large class of scientific data analysis applications it is becoming important, due to the sheer size of datasets, to have the option to perform the analysis directly where the data are stored, rather than on remote computational clusters. A possible strategy is the use of virtual clusters, thus guaranteeing a high degree of isolation from the underlying physical computational structure, and a very compact initial description. Deploying, saving and restoring HPC dedicated virtual clusters introduces, however, a different class of requirements on the virtual machines managing infrastructure, in particular for what concerns storage I/O requirements, whose scalability boundaries are easily reached. Here we discuss an alternative approach based on a storage model that leverages the WORM (write once, read many) character of the data used by VM management to increase, in a scalable way, the aggregate data bandwidth available to virtual cluster level operations and provide preliminary results indicating that it is a viable solution.

关键词： Cluster computing

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