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Global-aware and multi-order context-based prefetching for high-performance processors

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE computing APPLICATIONS 2011年第4期25卷 355-370页

作者： Chen, Yong Zhu, Huaiyu Roth, Philip C. Jin, Hui Sun, Xian-He Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Univ Illinois Dept Comp Sci Urbana IL USA Univ Illinois Dept Elect & Comp Engn Urbana IL USA Oak Ridge Natl Lab Comp Sci & Math Div Future Technol Grp Oak Ridge TN USA IIT Dept Comp Sci Chicago IL 60616 USA IIT Scalable Comp Software Lab Chicago IL 60616 USA

data prefetching is widely used in high-end computing systems to accelerate data accesses and to bridge the increasing performance gap between processor and memory. Context-based prefetching has become a primary focus of study in recent years due to its general applicability. However, current context-based prefetchers only adopt the context analysis of a single order, which suffers from low prefetching coverage and thus limits the overall prefetching effectiveness. Also, existing approaches usually consider the context of the address stream from a single instruction but not the context of the address stream from all instructions, which further limits the context-based prefetching effectiveness. In this study, we propose a new context-based prefetcher called the Global-aware and Multi-order Context-based (GMC) prefetcher. The GMC prefetcher uses multi-order, local and global context analysis to increase prefetching coverage while maintaining prefetching accuracy. In extensive simulation testing of the SPEC-CPU2006 benchmarks with an enhanced CMP$im simulator, the proposed GMC prefetcher was shown to outperform existing prefetchers and to reduce the data-access latency effectively. The average Instructions Per Cycle (IPC) improvement of SPEC CINT2006 and CFP2006 benchmarks with GMC prefetching was over 55% and 44% respectively.

关键词： prefetching context-based prefetching prefetching accuracy prefetching coverage processor architectures memory hierarchy data access delay prefetch degree prefetch priority cache pollution bandwidth contention SPEC-CPU2006 CMP$im simulator PIN high-end computing data intensive computing

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A Science Driven Production Cyberinfrastructure-the Open Science Grid

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JOURNAL OF GRID computing 2011年第2期9卷 201-218页

作者： Altunay, Mine Avery, Paul Blackburn, Kent Bockelman, Brian Ernst, Michael Fraser, Dan Quick, Robert Gardner, Robert Goasguen, Sebastien Levshina, Tanya Livny, Miron McGee, John Olson, Doug Pordes, Ruth Potekhin, Maxim Rana, Abhishek Roy, Alain Sehgal, Chander Sfiligoi, Igor Wuerthwein, Frank Fermilab Natl Accelerator Lab Batavia IL 60510 USA Univ Florida Gainesville FL USA CALTECH Pasadena CA 91125 USA Brookhaven Natl Lab Upton NY 11973 USA Argonne Natl Lab Downers Grove IL USA Indiana Univ Bloomington IN USA Univ Chicago Chicago IL 60637 USA Clemson Univ Clemson SC USA Univ Wisconsin Madison WI USA RENCI Chapel Hill NC USA Lawrence Berkeley Natl Lab Berkeley CA USA Univ Calif San Diego San Diego CA 92103 USA Univ Nebraska Lincoln NE USA

This article describes the Open Science Grid, a large distributed computational infrastructure in the United States which supports many different high-throughput scientific applications, and partners (federates) with other infrastructures nationally and internationally to form multi-domain integrated distributed systems for science. The Open Science Grid consortium not only provides services and software to an increasingly diverse set of scientific communities, but also fosters a collaborative team of practitioners and researchers who use, support and advance the state of the art in large-scale distributed computing. The scale of the infrastructure can be expressed by the daily throughput of around seven hundred thousand jobs, just under a million hours of computing, a million file transfers, and half a petabyte of data movement. In this paper we introduce and reflect on some of the OSG capabilities, usage and activities.

关键词： Scientific computing Distributed computing Grids High throughput computing data intensive computing

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Multiple query scheduling for distributed semantic caches

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2010年第5期70卷 598-611页

作者： Nam, Beomseok Shin, Minho Andrade, Henrique Sussman, Alan Oracle Redwood Shores CA 94065 USA Dartmouth Coll ISTS Hanover NH 03755 USA IBM Corp TJ Watson Res Ctr Hawthorne NY 10532 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA

In distributed query processing systems, load balancing plays an important role in maximizing system throughput. When queries can leverage cached intermediate results, improving the cache hit ratio becomes as important as load balancing in query scheduling, especially when dealing with computationally expensive queries. The scheduling policies must be designed to take into consideration the dynamic contents of the distributed caching infrastructure. In this paper, we propose and discuss several distributed query scheduling policies that directly consider the available cache contents by employing distributed multidimensional indexing structures and an exponential moving average approach to predicting cache contents. These approaches are shown to produce better query plans and faster query response times than traditional scheduling policies that do not predict dynamic contents in distributed caches. We experimentally demonstrate the utility of the scheduling policies using MQO, which is a distributed, Grid-enabled, multiple query processing middleware system we developed to optimize query processing for data analysis and visualization applications. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Multiple query optimization Distributed query scheduling data intensive computing

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Performance analysis of data intensive cloud systems based on data management and replication: a survey

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DISTRIBUTED AND PARALLEL dataBASES 2016年第2期34卷 179-215页

作者： Malik, Saif Ur Rehman Khan, Samee U. Ewen, Sam J. Tziritas, Nikos Kolodziej, Joanna Zomaya, Albert Y. Madani, Sajjad A. Min-Allah, Nasro Wang, Lizhe Xu, Cheng-Zhong Malluhi, Qutaibah Marwan Pecero, Johnatan E. Balaji, Pavan Vishnu, Abhinav Ranjan, Rajiv Zeadally, Sherali Li, Hongxiang COMSATS Inst Informat Technol Islamabad Pakistan N Dakota State Univ Fargo ND 58105 USA Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China Univ Bielsko Biala Bielsko Biala Poland Univ Sydney Sydney NSW 2006 Australia Univ Dammam Dammam Saudi Arabia Chinese Acad Sci Beijing Peoples R China Wayne State Univ Detroit MI USA Qatar Univ Doha Qatar Univ Luxembourg Walferdange Luxembourg Argonne Natl Lab Lemont IL USA Pacific NW Natl Lab Richland WA 99352 USA CSIRO ICT Ctr Marsfield NSW Australia Univ Dist Columbia Washington DC 20008 USA Univ Louisville Louisville KY 40292 USA

As we delve deeper into the 'Digital Age', we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such 'data Explosions' has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users' Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource u

关键词： Replication data management Cloud computing systems Performance gradation data intensive computing

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PARALLEL PROCESSING OF MASSIVE REMOTE SENSING IMAGES IN A GPU ARCHITECTURE

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computing AND INFORMATICS 2014年第1期33卷 197-217页

作者： Liu, Peng Yuan, Tao Ma, Yan Wang, Lizhe Liu, Dingsheng Yue, Shasha Kolodziej, Joanna Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100094 Peoples R China Cracow Univ Technol Inst Comp Sci PL-31155 Krakow Poland

Profiting from the development of space remote sensing technology, the amount of remote sensing image data obtained by satellite is increasing dramatically;however, how to deal with these data quickly and efficiently has turned out to be a great computational challenge. With the rapid development of general-purpose CPU computing technology, researchers improved remote sensing applications based on GPU, and obtained good speedup. However, the current CPU parallel processes are not well adapted to the remote sensing image processing;furthermore, they have data loading, storage, and I/O problems. To solve these bottlenecks, this paper proposes three corresponding optimization strategies, and their effectiveness is confirmed by further experiments.

关键词： GPU remote sensing image processing data intensive computing

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Big data visual analytics for exploratory earth system simulation analysis

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COMPUTERS & GEOSCIENCES 2013年第Dec.期61卷 71-82页

作者： Steed, Chad A. Ricciuto, Daniel M. Shipman, Galen Smith, Brian Thornton, Peter E. Wang, Dali Shi, Xiaoying Williams, Dean N. Oak Ridge Natl Lab Climate Change Sci Inst Oak Ridge TN 37831 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA

Rapid increases in high performance computing are feeding the development of larger and more complex data sets in climate research, which sets the stage for so-called "big data" analysis challenges. However, conventional climate analysis techniques are inadequate in dealing with the complexities of today's data. In this paper, we describe and demonstrate a visual analytics system, called the Exploratory data analysis ENvironment (EDEN), with specific application to the analysis of complex earth system simulation data sets. EDEN represents the type of interactive visual analysis tools that are necessary to transform data into insight, thereby improving critical comprehension of earth system processes. In addition to providing an overview of EDEN, we describe real-world studies using both point ensembles and global Community Land Model Version 4 (CLM4) simulations. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Visualization Parallel coordinates Climate Sensitivity analysis data intensive computing data mining Statistical visualization Multivariate Big data

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Goal-based composition of scalable hybrid analytics for heterogeneous architectures

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2017年 108卷 59-73页

作者： Coetzee, P. Jarvis, S. A. Univ Warwick Dept Comp Sci Coventry W Midlands England British Lib Alan Turing Inst London England

Crafting scalable analytics in order to extract actionable business intelligence is a challenging endeavour, requiring multiple layers of expertise and experience. Often, this expertise is irreconcilably split between an organisation's engineers and subject matter domain experts. Previous approaches to this problem have relied on technically adept users with tool-specific training. Such an approach has a number of challenges: Expertise - There are few data-analytic subject domain experts with in-depth technical knowledge of compute architectures;Performance - Analysts do not generally make full use of the performance and scalability capabilities of the underlying architectures;Heterogeneity - calculating the most performant and scalable mix of real-time (on-line) and batch (off-line) analytics in a problem domain is difficult;Tools - Supporting frameworks will often direct several tasks, including, composition, planning, code generation, validation, performance tuning and analysis, but do not typically provide end-to-end solutions embedding all of these activities. In this paper, we present a novel semi-automated approach to the composition, planning, code generation and performance tuning of scalable hybrid analytics, using a semantically rich type system which requires little programming expertise from the user. This approach is the first of its kind to permit domain experts with little or no technical expertise to assemble complex and scalable analytics, for hybrid on- and off-line analytic environments, with no additional requirement for low-level engineering support. This paper describes (i) an abstract model of analytic assembly and execution, (ii) goal-based planning and (iii) code generation for hybrid on- and off-line analytics. An implementation, through a system which we call MENDELEEV, is used to (iv) demonstrate the applicability of this technique through a series of case studies, where a single interface is used to create analytics that can be run simu

关键词： data science Hybrid analytics Analytic planning Streaming analysis Hadoop data intensive computing Heterogeneous compute

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Towards unified secure on- and off-line analytics at scale

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PARALLEL computing 2014年第10期40卷 738-753页

作者： Coetzee, P. Leeke, M. Jarvis, S. Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England

data scientists have applied various analytic models and techniques to address the oft-cited problems of large volume, high velocity data rates and diversity in semantics. Such approaches have traditionally employed analytic techniques in a streaming or batch processing paradigm. This paper presents CRUCIBLE, a first-in-class framework for the analysis of large-scale datasets that exploits both streaming and batch paradigms in a unified manner. The CRUCIBLE framework includes a domain specific language for describing analyses as a set of communicating sequential processes, a common runtime model for analytic execution in multiple streamed and batch environments, and an approach to automating the management of cell-level security labelling that is applied uniformly across runtimes. This paper shows the applicability of CRUCIBLE to a variety of state-of-the-art analytic environments, and compares a range of runtime models for their scalability and performance against a series of native implementations. The work demonstrates the significant impact of runtime model selection, including improvements of between 2.3 x and 480x between runtime models, with an average performance gap of just 14x between CRUCIBLE and a suite of equivalent native implementations. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/3.0/).

关键词： data science Analytics Streaming analysis Hadoop Domain specific languages data intensive computing

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Online Scheduling and Interference Alleviation for Low-Latency, High-Throughput Processing of data Streams

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2017年第12期28卷 3553-3569页

作者： Buddhika, Thilina Stern, Ryan Lindburg, Kira Ericson, Kathleen Pallickara, Shrideep Colorado State Univ Deptartment Comp Sci Ft Collins CO 80523 USA

data Streams occur naturally in several observational settings and often need to be processed with a low latency. Streams pose unique challenges: they have no preset lifetimes, the traffic on these streams may be bursty, and data arrival rates on these streams can be quite high. Furthermore, stream processing computations are generally stateful where the outcome of processing a data stream packet depends on the state that builds up within the computation over multiple, successive rounds of execution. As the number of streams increases, stream processing computations need to be orchestrated over a collection of machines. Achieving timeliness and high throughput in such settings is a challenge. Optimal scheduling of stream processing computations is an instance of the resource constrained scheduling problem, and depending on the precise formulation of the problem can be characterized as either NP-Complete or NP-Hard. We have designed an algorithm for online scheduling of stream processing computations. Our algorithm focuses on reducing interference that adversely impacts performance of stream processing computations. Our measure of interference is based on stream packet arrivals at a particular machine, the accompanying resource utilization encompassing CPU, memory and network utilization, and the resource utilization at machines comprising the cluster. Our algorithm performs continuous, incremental detection of interference experienced by computations and performing migrations to alleviate them.

关键词： Low-latency stream processing online scheduling data intensive computing

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Decentralized Edge Clouds

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IEEE INTERNET computing 2013年第5期17卷 70-73页

作者： Chandra, Abhishek Weissman, Jon Heintz, Benjamin Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Univ Minnesota Minneapolis MN 55455 USA

Nebula is a highly decentralized cloud that uses volunteer edge resources. Here, the authors provide insight into some of its key properties and design issues. They also describe a distributed MapReduce application scenario to illustrate the benefits and trade-offs of using distributed and decentralized clouds for distributed data-intensive computing applications.

关键词： Distributed databases Computational Modeling Cloud computing Computer Science Monitoring data Models Cloud Distributed Systems data intensive computing

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