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检索条件"任意字段=Proceedings of the 1997 Conference on Advances in Parallel and Distributed Computing"

共 690 条记录，以下是681-690 订阅

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Load balancing and communication optimization for parallel adaptive finite element methods

Load balancing and communication optimization for parallel a...

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Computer Science Society (SCCC) International conference Chilean FLAGGED

作者： J.E. Flaherty R.M. Loy P.C. Scully M.S. Shephard B.K. Szymanski J.D. Teresco L.H. Ziantz Computer Science Department and Scientific Computation Research Center Rensselaer Polytechnic Institute Troy NY USA

The paper describes predictive load balancing schemes designed for use with parallel adaptive finite element methods. We provide an overview of data structures suitable for distributed storage of finite element mesh data as well as software designed for mesh adaptation and load balancing. During the course of a parallel computation, processor load imbalances are introduced at adaptive enrichment steps. The predictive load balancing methods described here use a priori estimates of work load for adaptive refinement and subsequent computation to improve enrichment efficiency and reduce total balancing time. An analysis code developed with these components for solving compressible flow problems is used to obtain predictive load balancing results on an IBM SP2 computer. Our test problem involves the transient solution of the three dimensional Euler equations of compressible flow inside a perforated shock tube. We also present a message passing library extension in development which allows for automated packing of messages to improve communication efficiency.

关键词： Load management Finite element methods Data structures Software design Concurrent computing Load flow analysis Testing Equations Electric shock Message passing

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Efficient network management for collaborative services and application development

Efficient network management for collaborative services and ...

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IEEE International conference on Multimedia and Expo (ICME)

作者： K.H. Liu V.T. Tsaoussidis Department of Network Operations and Management Telcordia Technologies Inc. USA Department of Computer Science SUNY at Stony Brook USA

Collaborative services and applications are becoming increasingly important and complex, and have requirements for real-time multimedia support, high bandwidth availability and low delays. The collaborative application view of QoS encompasses the transport network and the end systems, as well as computational requirements. Therefore, how to coordinate and integrate collaboration management and network resource sharing efficiently and effectively is becoming more complex: a higher-level functionality is required in parallel with a satisfying performance. The functionality should satisfy the requirements of different levels of collaboration and respect a hierarchy of priorities among users and tasks. The performance, depending on the application or the network, should be sufficient for real-time information exchange, which is quite often a "heavy data exchange". We have developed a management framework to satisfy the complex functionality requirements that arise in the development of collaborative applications and services. The emphasis in designing such a framework is given on several levels of design that affect the performance of collaboration environments, the connection management, the replication management, the data management and the effective and interoperable usage of resources. Based on the proposed framework, we present a Java-based application for mission planning.

关键词： Collaboration Application software Resource management Quality of service Protocols Computer network management Computer networks Disaster management Environmental management distributed computing

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Primal-target neural net heuristics for the hypergraph k-coloring problem

Primal-target neural net heuristics for the hypergraph k-col...

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International conference on Neural Networks

作者： D. Kaznachey A. Jagota S. Das St. Jude Chidren's Research Hospital University of Memphis Memphis TN USA Dept of Computer Science University of California Santa Cruz Santa Cruz CA USA Dept of Computer Science University of North Texas Denton TX USA

The hypergraph strong coloring problem (HC) is an NP-hard problem on hypergraphs arising naturally in applications involving conflict-free access of data in parallel memory systems. There has been much work on solving graph optimization problems using neural networks; however, almost none on solving hypergraph problems. Hypergraph problems present interesting challenges to neural networks because they involve higher-order structures than those in graphs In this paper we introduce a primal-target approach to solve hard combinatorial problems having inequality constraints. We consider the variant of HC,-the maximum induced subhypergraph strong k-coloring problem (HkC). We present two primal-target algorithms that map HkC onto a Hopfield network. The first algorithm uses a larger set of target variables which leads to a more elaborate search mechanism. Experiments showed that both algorithms, PT1 and PT2, were competitive, with a naive optimal algorithm on small instances of random hypergraphs, while being significantly faster. Algorithm PT1 scaled significantly better to larger hypergraphs than PT2, while PT2 ran faster than PT1.

关键词： Neural networks Computer science Radio access networks Hopfield neural networks Computer networks Concurrent computing distributed computing Neural network hardware

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Neuromorphic processing: a new frontier in scaling computer architecture 14

Neuromorphic processing: a new frontier in scaling computer ...

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proceedings of the 19th international conference on Architectural support for programming languages and operating systems

作者： Jeff Gehlhaar Qualcomm Technologies Inc. San Diego CA USA

ISBN: (纸本)9781450323055

The desire to build a computer that operates in the same manner as our brains is as old as the computer itself. Although computer engineering has made great strides in hardware performance as a result of Dennard scaling, and even great advances in 'brain like' computation, the field still struggles to move beyond sequential, analytical computing architectures. Neuromorphic systems are being developed to transcend the barriers imposed by silicon power consumption, develop new algorithms that help machines achieve cognitive behaviors, and both exploit and enable further research in neuroscience. In this talk I will discuss a system im-plementing spiking neural networks. These systems hold the promise of an architecture that is event based, broad and shallow, and thus more power efficient than conventional computing solu-tions. This new approach to computation based on modeling the brain and its simple but highly connected units presents a host of new challenges. Hardware faces tradeoffs such as density or lower power at the cost of high interconnection overhead. Consequently, software systems must face choices about new language design. Highly distributed hardware systems require complex place and route algorithms to distribute the execution of the neural network across a large number of highly interconnected processing units. Finally, the overall design, simulation and testing process has to be entirely reimagined. We discuss these issues in the context of the Zeroth processor and how this approach compares to other neuromorphic systems that are becoming available.

关键词： spiking neural network parallel computing low power zeroth neural network neuromorphic

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A comparative evaluation of hierarchical network architecture of the HP-Convex Exemplar

A comparative evaluation of hierarchical network architectur...

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IEEE International conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： R. Castaneda Xiaodong Zhang J.M. Hoover University of Texas San Antonio USA The College of William and Mary USA Tech NOW Inc.

The Convex Exemplar (SPP1000 and SPP2000 series) is a new commercial distributed shared-memory architecture. Using a set of system kernels and two application programs, we examine performance effects on network latency, hot spot contention, cache coherence and overall scaling capability, which result both from the choice of the network structure as well as from its CC-NUMA memory system feature. Since the KSR-1 was also targeted at scalable cache coherent shared-memory systems by using hierarchical interconnection networks, we compared the architecture and performance results with the KSR-1. Our experiments indicate that the memory access latency of the Exemplar is comparatively low due to its fast processor node and the unique network/system structure. In addition, the Coherent Toroidal Interconnect (CTI) rings are efficient in handling cache coherence activities on the Exemplar. However, the Exemplar synchronization primitives need further exploit its hierarchical architecture in a high contention environment. As we expect with extremely fast processors in a hierarchical memory structure, the high-speed cluster-based Exemplar system is more sensitive to data locality.

关键词： Large-scale systems Computer architecture Scalability Multiprocessor interconnection networks Educational institutions Military computing parallel processing Kernel Process design Microprocessors

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The UCLA AGCM in high performance computing environments 98

The UCLA AGCM in high performance computing environments

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proceedings of the 1998 ACM/IEEE conference on Supercomputing

作者： C. R. Mechoso L. A. Drummond J. D. Farrara J. A. Spahr University of California Los Angeles Los Angeles CA

ISBN: (纸本)9780897919845

General Circulation Models (GCMs) are at the top of the hierarchy of numerical models that are used to study the Earth's climate. To increase the significance of predictions using GCMs requires ensembles of integrations that in turn demand large amounts of computing resources. GCMs codes are particularly difficult to optimize in view of their heterogeneity. In this paper we focus on code optimization for GCMs of the atmosphere (AGCMs), one of the major components of the climate *** this paper, we present our efforts in optimizing the parallel UCLA AGCM code. The UCLA AGCM is a state-of-the-art finite-difference model of the global atmosphere. Our optimization efforts include the implementation of load balancing schemes, new physical parameterizations of atmospheric processes, code restructuring and use of special mathematical functions. At the beginning of this work, the overall execution time of the code was 459 seconds per simulated day in 256 nodes of a CRAY T3D. At present, the same model configuration requires 51 seconds per simulated day in 256 nodes of a CRAY T3E-900, which is approximately 9 times faster. The peak model performance is about 40 GFLOPs on 512 T3E-900 nodes. We present results in support of our conclusion that major advances in our ability to carry out longer and more detailed climate simulations depend primarily upon development of more powerful supercomputers and that code optimization, for a particular computer architecture, and development of more efficient algorithms can be nearly as important.

关键词： code optimization high performance computing scientific computing climate modeling atmospheric model distributed computing

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Artificial Intelligence And Machine Learning

Artificial Intelligence And Machine Learning

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2022 IEEE Future Networks World Forum, FNWF 2022

作者： Kataria, Deepak Walid, Anwar Daneshmand, Mahmoud Dutta, Ashutosh Enright, Michael A. Gu, Rentao Lackpour, Alex Ramachandran, Prakash Wang, Honggang Chen, Chi-Ming Chng, Baw Darema, Frederica Ip Junction United States Nokia Bell Labs United States Stevens Institute of Technology United States Johns Hopkins University Applied Physics Lab United States Quantum Dimension Inc. United States Bupt China Drexel University United States Dell Technologies United States EOTF India UMass Dartmouth United States United States Bawman Llc United States Info Symbiotic Systems Society United States

ISBN: (纸本)9781665462501

In the evolution of artificial Intelligence (AI) and machine learning (ML), reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects have been widely used. These features enable the creation of intelligent mechanisms for decision support to overcome the limits of human knowledge processing. In addition, ML algorithms enable applications to draw conclusions and make predictions based on existing data without human supervision, leading to quick, near-optimal solutions even in problems with high dimensionality. Hence, autonomy is a key aspect of current and future AI/ML algorithms. This chapter focuses on the development and implementation of AI/ML technologies for 5G and future networks. The objective is to illustrate how these technologies can be smoothly migrated into 5G systems to increase their performance and to decrease their cost. To that end, this chapter presents the Drivers, Needs, Challenges, Enablers, and Potential Solutions identified for the AI/ML field as applicable to future networks over three-, five-, and ten-year horizons. AI/ML applications for 5G are wide and diverse. In this document, some of the key areas are described which includes networking, securing, cloud computing and others. Over time, this white paper will evolve to encompass even more areas where AI/ML technologies can improve future network performance objectives. © 2022 IEEE.

关键词： Cloud computing

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Ultra-Scalable Simulations of Networks of Polarized Evolutionary Processors 19

Ultra-Scalable Simulations of Networks of Polarized Evolutio...

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proceedings of the 3rd International conference on advances in Artificial Intelligence

作者： Sandra Gomez-Canaval Victor Mitrana Mihaela Paun Stanislav Vararuk Depto. de Sistemas Informáticos Universidad Politécnica de Madrid Madrid Spain National Institute for Research and Development of Biological Sciences Bucharest Romania Research Group of Modelling and Mathematical Biocomputation Universidad Politécnica de Madrid Madrid Spain

ISBN: (纸本)9781450372534

The Network of Polarized Evolutionary Processors (NPEP) is a rather new variant of the bio-inspired computing model called Network of Evolutionary Processors (NEP). This model, together with its variants, is able to provide theoretical feasible solutions to hard computational problems. NPEPE is a software engine able to simulate NPEP which is deployed over Giraph, an ultra-scalable platform based on the Bulk Synchronous parallel (BSP) programming model. Rather surprisingly, the BSP model and the underlying architecture of NPEP have many common points. Moreover, these similarities are also shared with all variants in the NEP family. We take advantage of these similarities and propose an extension of NPEPE (named gNEP) that enhances it to simulate any variant of the NEP's family. Our extended gNEP framework, presents a twofold contribution. Firstly, a flexible architecture able to extend software components in order to include other NEP models (including the seminal NEP model and new ones). Secondly, a component able to translate input configuration files representing the instance of a problem and an algorithm based on different variants of the NEP model into some suitable input files for gNEP framework. In this work, we simulate a solution to the "3-colorability" problem which is based on NPEP. We compare the results for a specific experiment using NPEPE engine and gNEP. Moreover, we show several experiments in the aim of studying, in a preliminary way, the scalability offered by gNEP to easily deploy and execute instances of problems requiring more intensive computations.

关键词： Bio-inspired computational model Networks of Polarized Evolutionary Processors distributed architectures parallel computation Bulk Synchronous parallel programming model

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases 1

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丛书名： Communications in Computer and Information Science

1000年

作者： Michael Kamp Irena Koprinska Adrien Bibal Benoît Frénay Tassadit Bouadi Luis Galárraga José Oramas Linara Adilova Yamuna Krishnamurthy Bo Kang Tim Verbelen Christine Largeron Jefrey Lijffijt Tiphaine Viard Pascal Welke Massimiliano Ruocco Erlend Aune Claudio Gallicchio Riccardo Guidotti Anna Monreale Gregor Schiele Franz Pernkopf Michaela Blott Holger Fröning Günther Schindler Salvatore Rinzivillo Przemyslaw Biecek Eirini Ntoutsi Mykola Pechenizkiy Bodo Rosenhahn Christopher Buckley Daniela Cialfi Pablo Lanillos Maxwell Ramstead Pedro M. Ferreira Giuseppina Andresini Donato Malerba Ibéria Medeiros Guilherme Graça Philippe Fournier-Viger M. Saqib Nawaz Sebastian Ventura Meng Sun Min Zhou Valerio Bitetta Andrea Ferretti Ilaria Bordino Francesco Gullo Lorenzo Severini Giovanni Ponti Rita Ribeiro João Gama Ricard Gavaldà Lee Cooper Naghmeh Ghazaleh Jonas Richiardi Damian Roqueiro Diego Saldana Miranda Konstantinos Sechidis

ISBN: (数字)9783030937362

ISBN: (纸本)9783030937355

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online.;The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops:;Workshop on advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021);Workshop on parallel, distributed and Federated Learning (PDFL 2021);Workshop on Graph Embedding and Mining (GEM 2021);Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021);Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021);Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021);Workshop on Bias and Fairness in AI (BIAS 2021);Workshop on Workshop on Active Inference (IWAI 2021);Workshop on Machine Learning for Cybersecurity (MLCS 2021);Workshop on Machine Learning in Software Engineering (MLiSE 2021);Workshop on MIning Data for financial applications (MIDAS 2021);Sixth Workshop on Data Science for Social Good (SoGood 2021);Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021);Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020);Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021)

关键词： Artificial Intelligence Information Systems and Communication Service Computer Appl. in Social and Behavioral Sciences Computers and Education Mathematics of computing Software Engineering/Programming and Operating Systems

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases 1

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丛书名： Communications in Computer and Information Science

1000年

ISBN: (数字)9783030937331

ISBN: (纸本)9783030937324

关键词： Artificial Intelligence Information Systems and Communication Service Computer Appl. in Social and Behavioral Sciences computing Milieux Computer Systems Organization and Communication Networks Computer Imaging, Vision, Pattern Recognition and Graphics

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