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检索条件"任意字段=23rd IEEE International Parallel and Distributed Processing Symposium"

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Efficient GPU-based Query processing with Pruned List Caching in Search Engines 23

Efficient GPU-based Query Processing with Pruned List Cachin...

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23rd ieee international Conference on parallel and distributed Systems (ICPADS)

作者： Wang, Dongdong Yu, Wenqing Stones, Rebecca J. Ren, Junjie Wang, Gang Liu, Xiaoguang Ren, Mingming Nankai Univ Coll Comp & Control Engn Nankai Baidu Joint Lab Tianjin Peoples R China Baidu Inc Beijing Peoples R China

ISBN: (纸本)9781538621295

There are two inherent obstacles to effectively using Graphics processing Units (GPUs) for query processing in search engines: (a) the highly restricted GPU memory space, and (b) the CPU-GPU transfer latency. Previously, Ao et al. presented a GPU method for lists intersection, an essential component in AND-based query processing. However, this work assumes the whole inverted index can be stored in GPU memory and does not address document ranking. In this paper, we describe and analyze a GPU query processing method which incorporates both lists intersection and top-k ranking. We introduce a parameterized pruned posting list GPU caching method where the parameter determines how much GPU memory is used for caching. This method allows list caching for large inverted indexes using the limited GPU memory, thereby making a qualitative improvement over previous work. We also give a mathematical model which can identify an approximately optimal choice of the parameter. Experimental results indicate that this GPU approach under the pruned list caching policy achieves better query throughput than its CPU counterpart, even when the inverted index size is much larger than the GPU memory space.

关键词： Graphics processing units Query processing Instruction sets Indexes Memory management Search engines Acceleration

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Event Description and Detection in Cyber-Physical Systems: An Ontology-Based Language and Approach 23

Event Description and Detection in Cyber-Physical Systems: A...

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23rd ieee international Conference on parallel and distributed Systems (ICPADS)

作者： Ma, Meng Liu, Ling Lin, Yangxin Pan, Disheng Wang, Ping Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Peking Univ Sch Software & Microelect Beijing Peoples R China Beijing Natl Railway Res & Design Inst Signal & C Beijing Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing Peoples R China

ISBN: (纸本)9781538621295

In this paper, we propose an ontology-based language, OntoEvent, for semantic complex event modeling and detection in Cyber-Physical Systems (CPS). We divide the core concepts of OntoEvent model into two levels: general concepts and domain-specific instances, to promise it can be dynamic extended for different CPS application domains. Complex events are modeled based on event ontology with logical and temporal operators, and these operators are extended by nature language synonymies. OntoEvent language is of rich expressiveness compared to other traditional languages. Based on OntoEvent, We propose an event detection model and elaborate its construction procedures. Experimental results prove that OntoEvent-based event detection model outperforms other selected models in processing efficiency, especially when processing multiple complex event ontologies.

关键词： Cyber-Physical System Complex Event processing Description Language Semantic Ontology

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The AMPERE Project: A Model-driven development framework for highly parallel and EneRgy-Efficient computation supporting multi-criteria optimization 23

The AMPERE Project: A Model-driven development framework for...

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ieee 23rd international symposium on Real-Time distributed Computing (ISORC)

作者： Quinones, Eduardo Royuela, Sara Scordino, Claudio Gai, Paolo Pinho, Luis Miguel Nogueira, Luis Rollo, Jan Cucinotta, Tommaso Biondi, Alessandro Hamann, Arne Ziegenbein, Dirk Saoud, Hadi Soulat, Romain Forsberg, Bjoern Benini, Luca Mando, Gianluca Rucher, Luigi Barcelona Supercomp Ctr Barcelona Spain Evidence Srl Pisa Italy Inst Super Engn Porto Porto Portugal Sysgo Prague Czech Republic Scuola Super Sant Anna Pisa Italy Bosch Stuttgart Germany Thales Res & Technol Palaiseau France Swiss Fed Inst Technol Zurich Switzerland Thales Italy Florence Italy

ISBN: (纸本)9781728169583

The high-performance requirements needed to implement the most advanced functionalities of current and future Cyber-Physical Systems (CPSs) are challenging the development processes of CPSs. On one side, CPSs rely on model-driven engineering (MDE) to satisfy the non-functional constraints and to ensure a smooth and safe integration of new features. On the other side, the use of complex parallel and heterogeneous embedded processor architectures becomes mandatory to cope with the performance requirements. In this regard, parallel programming models, such as OpenMP or CUDA, are a fundamental brick to fully exploit the performance capabilities of these architectures. However, parallel programming models are not compatible with current MDE approaches, creating a gap between the MDE used to develop CPSs and the parallel programming models supported by novel and future embedded platforms. The AMPERE project will bridge this gap by implementing a novel software architecture for the development of advanced CPSs. To do so, the proposed software architecture will be capable of capturing the definition of the components and communications described in the MDE framework, together with the non-functional properties, and transform it into key parallel constructs present in current parallel models, which may require extensions. These features will allow for making an efficient use of underlying parallel and heterogeneous architectures, while ensuring compliance with non-functional requirements, including those on real-time performance of the system.

关键词： parallel programming models parallel and heterogeneous embedded processor architectures model-driven approaches safety-critical embedded systems

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Reducing Job Slowdown Variability for Data-Intensive Workloads 23

Reducing Job Slowdown Variability for Data-Intensive Workloa...

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23rd international symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)

作者： Ghit, Bogdan Epema, Dick Delft Univ Technol NL-2600 AA Delft Netherlands Eindhoven Univ Technol NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)9781467377201

A well-known problem when executing dataintensive workloads with such frameworks as MapReduce is that small jobs with processing requirements counted in the minutes may suffer from the presence of huge jobs requiring hours or days of compute time, leading to a job slowdown distribution that is very variable and that is uneven across jobs of different sizes. Previous solutions to this problem for sequential or rigid jobs in single-server and distributed-server systems include priority-based FeedBack Queueing (FBQ), and Task Assignment by Guessing Sizes (TAGS), which kills and restarts from scratch on another server jobs that exceed the local time limit. In this paper, we derive four scheduling policies that are rightful descendants of existing size-based scheduling disciplines (among which FBQ and TAGS) with appropriate adaptations to dataintensive frameworks. The two main mechanisms employed by these policies are partitioning the resources of the datacenter, and isolating jobs with different size ranges. We evaluate these policies by means of realistic simulations of representative MapReduce workloads from Facebook and show that under the best of these policies, the vast majority of short jobs in MapReduce workloads experience close to ideal job slowdowns even under high system loads (in the range of 0.7-0.9) while the slowdown of the very large jobs is not prohibitive. We validate our simulations by means of experiments on a real multicluster system, and we find that the job slowdown performance results obtained with both match remarkably well.

关键词： computer centres data handling parallel processing queueing theory resource allocation scheduling FBQ Facebook MapReduce workload TAGS data center resource partitioning data intensive framework data-intensive workload execution distributed-server system job isolation job size job slowdown distribution job slowdown variability multicluster system priority-based feedback queueing scheduling policy server job single-server system size-based scheduling task assignment by guessing sizes Analytical models Computational modeling Computers Delays Runtime Servers MapReduce datacenters Facebook jobs scheduling policy TAGGING Social Media Servers data processing Runtime queueing theory SLOWING-DOWN parallel processing (COMPUTERS) ANALYSIS MODELS Computational modeling Computers Workload

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distributed In-band Network Telemetry 23

Distributed In-band Network Telemetry

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23rd ieee/ACM international symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Thummar, Dhyey Nawab, Iram Kulkarni, Sameer G. Indian Inst Technol Gandhinagar Gandhinagar Gujarat India

ISBN: (纸本)9798350302080

Modern-day networks are becoming bigger and more complex as the days go by. Thus, network telemetry plays a critical role in modern networking. One of the approaches has been In-band network telemetry (INT) which relies on the usage of network data packets to collect the data plane state without the need for separate control or data channels. This benefits to provide real-time and accurate network monitoring. The traditional in-band telemetry techniques usually incur significant overheads to the large packet size (INT meta-data) and redundant computation/processing at each switch for the marked packets. To address the limitations of traditional in-band network telemetry, we propose a hierarchically distributed INT that leverages the logically centralized control plane, such as Software Defined Networking (SDN), to distribute INT processing and analysis components across multiple switches, providing scalable and efficient network monitoring capabilities.

关键词： In-Band Network Telemetry Software Defined Networking (SDN) SmartNICs programmable switches

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D-ISMC: A distributed unsupervised classification algorithm for optical satellite imagery

D-ISMC: A distributed unsupervised classification algorithm ...

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23rd international Geoscience and Remote Sensing symposium (IGARSS 2003)

作者： Bo, L Bretschneider, T Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

ISBN: (纸本)0780379292

In this paper, a distributed version of the Improved Split-and-Merge Clustering algorithm for ground cover classification is presented, whereby the focus is on the implementation on two different parallel architectures. The analysis of the algorithm presents the obtained speedup factors by the parallelisation and investigates the proposed changes for the improvement of the classification accuracy.

关键词： Remote sensing

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Highly Scalable Large-Scale Asynchronous Graph processing using Actors 23

Highly Scalable Large-Scale Asynchronous Graph Processing us...

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23rd ieee/ACM international symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Elmougy, Youssef Hayashi, Akihiro Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350302080

With the accelerating growth of Big Data, real-world graph processing applications now need to tackle graphs with billions of vertices and trillions of edges, thereby increasing the demand for effective solutions to application scalability. Unfortunately, current approaches to implementing these applications on modern HPC systems exhibit poor scale-out performance with increasing numbers of nodes. The scalability challenges for these applications are driven by large data sizes, synchronization overheads, and fine-grained communications with irregular data accesses and poor locality. This paper presents the scalability of a novel Actor-based programming system, which provides a lightweight runtime that supports fine-grained asynchronous execution and automatic message aggregation atop a Partitioned Global Address Space (PGAS) communication layer. Evaluations of the Jaccard Index and PageRank applications on the NERSC Perlmutter system demonstrate nearly perfect scaling up to 1, 000 nodes and 64K cores (one-third of the targeted 3000-nodes for Perlmutter). In addition, our Actor-based implementations of Jaccard Index and PageRank executed with parallel efficiencies of 85.7% and 63.4% for the largest run of 64K cores. This performance represents a 29.6x speedup relative to UPC and OpenSHMEM versions of PageRank.

关键词： parallel Graph processing Actors Selectors distributed Systems Shared Memory PGAS OpenSHMEM

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The Suzaku Pattern Programming Framework 30

The Suzaku Pattern Programming Framework

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30th ieee international parallel and distributed processing symposium (IPDPS)

作者： Wilkinson, Barry Ferner, Clayton Univ N Carolina 9201 Univ City Blvd Charlotte NC 28223 USA Univ N Carolina 601 S Coll Rd Wilmington NC 28403 USA

ISBN: (纸本)9781509036820

Suzaku is a pattern programming framework that enables programmers to create pattern-based parallel MPI programs without writing the MPI message-passing code implicit in the patterns. The purpose of this framework is to simplify message-passing programming and create better structured programs based upon established parallel design patterns. The focus for developing Suzaku is on teaching parallel programming. This paper covers the main features of Suzaku and describes our experiences using it in parallel programming classes.

关键词： pattern programming parallel programming distributed computing MPI OpenMP

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A High-Performance distributed Relational Database System for Scalable OLAP processing 33

A High-Performance Distributed Relational Database System fo...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Arnold, Jason Glavic, Boris Raicu, Ioan IIT Chicago IL 60616 USA

ISBN: (纸本)9781728112466

The scalability of systems such as Hive and Spark SQL that are built on top of big data platforms have enabled query processing over very large data sets. However, the per-node performance of these systems is typically low compared to traditional relational databases. Conversely, Massively parallel processing (MPP) databases do not scale as well as these systems. We present HrdBMS, a fully implemented distributed shared-nothing relational database developed with the goal of improving the scalability of OLAP queries. HrdBMS achieves high scalability through a principled combination of techniques from relational and big data systems with novel communication and work-distribution techniques. While we also support serializable transactions, the system has not been optimized for this use case. HrdBMS runs on a custom distributed and asynchronous execution engine that was built from the ground up to support highly parallelized operator implementations. Our experimental comparison with Hive, Spark SQL, and Greenplum confirms that HrdBMS's scalability is on par with Hive and Spark SQL (up to 96 nodes) while its per-node performance can compete with MPP databases like Greenplum.

关键词： SQL big data distributed query processing

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HBLast: An open-source FPGA library for DNA sequencing acceleration 33

HBLast: An open-source FPGA library for DNA sequencing accel...

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33rd ieee international parallel and distributed processing symposium (IPDPS)

作者： Bekbolat, Marzhan Kairatova, Sabina Shymyrbay, Ayan Vipin, Kizheppatt Nazarbayev Univ Dept Elect & Comp Engn Astana Kazakhstan

ISBN: (纸本)9781538655559

Biological sequence alignment algorithms work by identifying the degree of similarity between a newly discovered biological sequence with already known sequences. Exponential growth in the database and query sizes make pure software-based solutions inefficient in finding a solution within the time constraints. This calls for hardware acceleration-based solutions such as those based on field programmable gate arrays (FPGAs). In this work we present the design and implementation of the popular BLAST algorithm for nucleotide sequence alignment, called HBLast, targeting FPGA-based reconfigurable platforms.

关键词： FPGA BLAST algorithm parallel architecture Sequence alignment

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