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data-intensive computing in the 21st century

COMPUTER 2008年第4期41卷 30-32页

作者： Gorton, Ian Greenfield, Paul Szalay, Alex Williams, Roy Johns Hopkins Univ Dept Phys & Astron Baltimore MD 21218 USA CALTECH Ctr Adv Comp Pasadena CA 91125 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

The deluge of data that future applications must process—in domains ranging from science to business informatics—creates a compelling argument for substantially increased R&D targeted at discovering scalable har... 详细信息

关键词： data-intensive computing compute-intensive problems

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data-intensive computing Modules for Teaching Parallel and Distributed computing

Data-Intensive Computing Modules for Teaching Parallel and D...

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35th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Gowanlock, Michael Gallet, Benoit No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA

ISBN: (纸本)9781665435772

Parallel and distributed computing (PDC) has found a broad audience that exceeds the traditional fields of computer science. This is largely due to the increasing computational demands of many engineering and domain science research objectives. Thus, there is a demonstrated need to train students with and without computer science backgrounds in core PDC concepts. Given the rise of data science and other data-enabled computational fields, we propose several data-intensive pedagogic modules that are used to teach PDC using message-passing programming with the Message Passing Interface (MPI). These modules employ activities that are common in database systems and scientific workflows that are likely to be employed by domain scientists. Our hypothesis is that using application-driven pedagogic materials facilitates student learning by providing the context needed to fully appreciate the goals of the activities. We evaluated the efficacy of using the data-intensive pedagogic modules to teach core PDC concepts using a sample of graduate students enrolled in a high performance computing course at Northern Arizona University. In the sample, only 30% of students have a traditional computer science background. We found that the hands-on application-driven approach was generally successful at helping students learn core PDC concepts.

关键词： Computer Science Education data-intensive computing High Performance computing Parallel and Distributed computing

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data-intensive computing Infrastructure Systems for Unmodified Biological data Analysis Pipelines 11th

Data-Intensive Computing Infrastructure Systems for Unmodifi...

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11th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)

作者： Bongo, Lars Ailo Pedersen, Edvard Ernstsen, Martin Univ Tromso Dept Comp Sci Tromso Norway Univ Tromso Ctr Bioinformat Tromso Norway

ISBN: (纸本)9783319244624;9783319244617

Biological data analysis is typically implemented using a deep pipeline that combines a wide array of tools and databases. These pipelines must scale to very large datasets, and consequently require parallel and distributed computing. It is therefore important to choose a hardware platform and underlying data management and processing systems well suited for processing large datasets. There are many infrastructure systems for such data-intensive computing. However, in our experience, most biological data analysis pipelines do not leverage these systems. We give an overview of data-intensive computing infrastructure systems, and describe how we have leveraged these for: (i) scalable fault-tolerant computing for large-scale biological data;(ii) incremental updates to reduce the resource usage required to update large-scale compendium;and (iii) interactive data analysis and exploration. We provide lessons learned and describe problems we have encountered during development and deployment. We also provide a literature survey on the use of data-intensive computing systems for biological data processing. Our results show how unmodified biological data analysis tools can benefit from infrastructure systems for data-intensive computing.

关键词： data-intensive computing biological data analysis flexible pipelines infrastructure systems

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A capabilities-aware framework for using computational accelerators in data-intensive computing

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2011年第2期71卷 185-197页

作者： Rafique, M. Mustafa Butt, Ali R. Nikolopoulos, Dimitrios S. Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA FORTH Inst Comp Sci GR-70013 Iraklion Greece Univ Crete Dept Comp Sci GR-71409 Iraklion Greece

Multicore computational accelerators such as GPUs are now commodity components for high-performance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Cloud computing MapReduce Asymmetric multicores Programming accelerators data-intensive computing Heterogeneous clusters

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G-Hadoop: MapReduce across distributed data centers for data-intensive computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2013年第3期29卷 739-750页

作者： Wang, Lizhe Tao, Jie Ranjan, Rajiv Marten, Holger Streit, Achim Chen, Jingying Chen, Dan China Univ Geosci Sch Comp Wuhan 430074 Peoples R China Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100864 Peoples R China Karlsruhe Inst Technol Steinbuch Ctr Comp D-76021 Karlsruhe Germany CSIRO ICT Ctr Informat Engn Lab Canberra ACT Australia Cent China Normal Univ Natl Engn Ctr E Learning Beijing Peoples R China Karlsruhe Inst Technol Inst Telemat Dept Informat D-76021 Karlsruhe Germany

Recently, the computational requirements for large-scale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data is processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In this paper, we present the design and implementation of G-Hadoop, a MapReduce framework that aims to enable large-scale distributed computing across multiple clusters. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Cloud computing Massive data processing data-intensive computing Hadoop MapReduce

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Hardware technologies for high-performance data-intensive computing

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COMPUTER 2008年第4期41卷 60-+页

作者： Gokhale, Maya Cohen, Jonathan Yoo, Andy Miller, W. Marcus Jacob, Arpith Ulmer, Craig Pearce, Roger Lawrence Livermore Natl Lab Livermore CA 94551 USA Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA Sandia Natl Labs Livermore CA 94550 USA Texas A&M Univ Dept Comp Sci College Stn TX 77843 USA

data-intensive problems challenge conventional computing architectures with demanding CPU, memory, and I/O requirements. Using benchmarks that draw on three data types - scientific imagery, unstructured text, and semantic graphs representing networks of relationships - the authors demonstrate that emerging hardware technologies to augment traditional microprocessor-based computing systems can deliver 2 to 17 times the performance of general-purpose computers on a wide range of data-intensive applications by increasing compute cycles and bandwidth and reducing latency.

关键词： data management data science GPUs FPGAs solid-state storage semantic graphs image resampling language analysis computer systems data-intensive computing

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Passive Network Performance Estimation for Large-Scale, data-intensive computing

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2011年第8期22卷 1365-1373页

作者： Kim, Jinoh Chandra, Abhishek Weissman, Jon B. Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

Distributed computing applications are increasingly utilizing distributed data sources. However, the unpredictable cost of data access in large-scale computing infrastructures can lead to severe performance bottlenecks. Providing predictability in data access is, thus, essential to accommodate the large set of newly emerging large-scale, data-intensive computing applications. In this regard, accurate estimation of network performance is crucial to meeting the performance goals of such applications. Passive estimation based on past measurements is attractive for its relatively small overhead compared to relying on explicit probing. In this paper, we take a passive approach for network performance estimation. Our approach is different from existing passive techniques that rely either on past direct measurements of pairs of nodes or on topological similarities. Instead, we exploit secondhand measurements collected by other nodes without any topological restrictions. In this paper, we present Overlay Passive Estimation of Network performance (OPEN), a scalable framework providing end-to-end network performance estimation based on secondhand measurements, and discuss how OPEN achieves cost-effective estimation in a large-scale infrastructure. Our extensive experimental results show that OPEN estimation can be applicable for replica and resource selections commonly used in distributed computing.

关键词： Network performance estimation secondhand estimation data-intensive computing replica selection resource selection

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In-Memory data Rearrangement for Irregular, data-intensive computing

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COMPUTER 2015年第8期48卷 18-25页

作者： Lloyd, Scott Gokhale, Maya Lawrence Livermore Natl Lab Livermore CA 94550 USA

The data rearrangement engine (DRE) performs in-memory data restructuring to accelerate irregular, data-intensive applications. An emulation on a field-programmable gate array shows how the DRE could improve speedup, ... 详细信息

关键词： field programmable gate arrays DRE data rearrangement engine energy consumption improvement field-programmable gate array in-memory data rearrangement in-memory data restructuring irregular-data-intensive application acceleration memory bandwidth improvement speedup improvement Computer applications Emulation Field programmable gate arrays Memory management Stacked memory 3D stacked memory FPGA emulation active memory data rearrangement engine data-intensive computing irregular applications processing in memory

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DECO: Joint Computation Scheduling, Caching, and Communication in data-intensive computing Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第3期30卷 1058-1072页

作者： Kamran, Khashayar Yeh, Edmund Ma, Qian Pinterest Inc San Francisco CA 94107 USA Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China

Driven by technologies such as IoT-enabled health care, machine learning applications at the edge, and industrial automation, mobile edge and fog computing paradigms have reinforced a general trend toward decentralized computing, where any network node can route traffic, compute tasks, and store data, possibly at the same time. In many such computing environments, there is a need to cache significant amounts of data, which may include large data sets, machine learning models, or executable code. In this work, we propose a framework for joint computation scheduling, caching, and request forwarding within such decentralized computing environments. We first characterize the stability region of a ``genie-aided'' computing network where data required by computation are instantly accessible, and develop a throughput optimal control policy for this model. Based on this, we develop a practically implementable distributed and adaptive algorithm, and show that it exhibits superior performance in terms of average task completion time, when compared to several baseline policies.

关键词： Task analysis Processor scheduling Throughput Computational modeling Edge computing Network topology Low latency communication Edge computing fog computing computation scheduling distributed computing data-intensive computing caching

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Optimizing end-to-end performance of data-intensive computing pipelines in heterogeneous network environments

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2011年第2期71卷 254-265页

作者： Wu, Qishi Gu, Yi Univ Memphis Dept Comp Sci Memphis TN 38152 USA

Supporting high-performance data-intensive computing pipelines in wide-area networks is crucial for enabling large-scale distributed scientific applications that require minimizing end-to-end delay for single-input applications or maximizing frame rate for streaming applications. We formulate and categorize the data-intensive computing pipeline mapping problems into six classes with two optimization objectives, i.e. minimum end-to-end delay and maximum frame rate, and three network constraints, i.e. no, contiguous, and arbitrary node reuse. We design a dynamic programming-based optimal solution to the problem of minimum end-to-end delay with arbitrary node reuse and prove the NP-completeness of the rest five problems, for each of which, a heuristic algorithm based on a similar optimization procedure is proposed. These heuristics are implemented and tested on a large set of simulated pipelines and networks of various scales and their performance superiority is illustrated by extensive simulation results in comparison with existing methods. (C) 2010 Elsevier Inc. All rights reserved.

关键词： data-intensive computing Pipeline Performance optimization End-to-end delay Frame rate

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