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检索条件"主题词=Data-Intensive Computing"

共 130 条记录，以下是101-110 订阅

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File Clustering Based Replication Algorithm in a Grid Environment 09

File Clustering Based Replication Algorithm in a Grid Enviro...

引用

9th IEEE/ACM International Symposium on Cluster computing and the Grid

作者： Sato, Hitoshi Matsuoka, Satoshi Endo, Toshio Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781424439355

Replication in grid file systems can significantly improve I/O performance of data-intensive applications. However most of existing replication techniques apply to individual files, which may introduce inefficient replication overheads for a large number of files. We propose a file clustering based replication algorithm for grid file systems. Our algorithm groups files according to a relationship of simultaneous accesses between files and stores replicas of the clustered files into storage nodes, to satisfy expected most of future read access times to the clustered files and replication times for individual files being minimized under the given storage capacity limitation. Our experiments on a given grid environment, 20 nodes of 5 sites, suggest that the proposed algorithm achieves accurate file clustering and efficient replica management;our clustering policy with the file cluster size limit of 5120 MB and the storage capacity limit for replicas of 10240 MB exhibits 1.58 times efficiency than the policy that never groups related files. The results also indicate that the overheads required for introducing our algorithm significantly affect I/O performance of running applications.

关键词： data-intensive computing

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On Efficient Hierarchical Storage for Big data Processing 16

On Efficient Hierarchical Storage for Big Data Processing

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16th IEEE/ACM International Symposium on Cluster, Cloud and Grid computing (CCGrid)

作者： Krish, K. R. Wadhwa, Bharti Iqbal, M. Safdar Rafique, M. Mustafa Butt, Ali R. Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA IBM Res Dublin Ireland

ISBN: (纸本)9781509024537

A promising trend in storage management for big data frameworks, such as Hadoop and Spark, is the emergence of heterogeneous and hybrid storage systems that employ different types of storage devices, e.g. SSDs, RAMDisks, etc., alongside traditional HDDs. However, scheduling data accesses or requests to an appropriate storage device is non-trivial and depends on several factors such as data locality, device performance, and application compute and storage resources utilization. To this end, we present DUX, an application-attuned dynamic data management system for data processing frameworks, which aims to improve overall application I/O throughput by efficiently using SSDs only for workloads that are expected to benefit from them rather than the extant approach of storing a fraction of the overall workloads in SSDs. The novelty of DUX lies in profiling application performance on SSDs and HDDs, analyzing the resulting I/O behavior, and considering the available SSDs at runtime to dynamically place data in an appropriate storage tier. Evaluation of DUX with trace-driven simulations using synthetic Facebook workloads shows that even when using 5.5x fewer SSDs compared to a SSD-only solution, DUX incurs only a small (5%) performance overhead, and thus offers an affordable and efficient storage tier management.

关键词： MapReduce data-intensive computing heterogeneous storage performance prediction tiered storage

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Building A Scalable Forward Flux Sampling Framework using Big data and HPC 19

Building A Scalable Forward Flux Sampling Framework using Bi...

引用

Conference on Practice and Experience in Advanced Research computing on Rise of the Machines (learning) (PEARC)

作者： DeFever, Ryan S. Hanger, Walter Sarupria, Sapna Kilgannon, Jon Apon, Amy W. Ngo, Linh B. Clemson Univ Chem & Biomol Engn Clemson SC 29634 USA West Chester Univ Comp Sci W Chester PA USA Clemson Univ Sch Comp Clemson SC USA

ISBN: (纸本)9781450372275

Forward flux sampling (FFS) is an established scientific method for sampling rare events in molecular simulations. However, as the difficulty of the scientific problem increases, the amount of data and the number of tasks required for FFS is challenging to manage with traditional scripting tools and languages for high performance computing. The SAFFIRE software framework has been developed to address these challenges. SAFFIRE utilizes Hadoop to manage a large number of tasks and data for large scale FFS simulations. The framework is shown to be highly scalable and able to support large scale FFS simulations. This enables studies of rare events in complex molecular systems on commodity cluster computing systems.

关键词： Forward Flux Sampling Hadoop data-intensive computing molecular simulations rare events

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ToMaR - A data Generator for Large Volumes of Content 14

ToMaR - A Data Generator for Large Volumes of Content

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14th IEEE/ACM International Symposium on Cluster, Cloud and Grid computing (CCGrid)

作者： Schmidt, Rainer Rella, Matthias Schlarb, Sven AIT Pune Maharashtra India Austrian Natl Lib Vienna Austria

ISBN: (纸本)9781479927845

We present ToMaR, a scalable application that supports the efficient integration of legacy applications within a MapReduce environment. The work is motivated by scenarios for scalable content processing developed in the context of the EC project SCAPE. ToMaR specifically addresses the need for extracting data sets from large volumes of binary content based on existing, content-specific applications within a scalable data management environment. This paper discusses the main functionalities of ToMaR and describes how ToMaR is utilized as part of a typical workflow. We present a real-word scenario that makes use of ToMaR for the characterization of archived web content. A workflow and experimental results which have been produced using sample content from the Web Archive Austria are discussed.

关键词： content management data-intensive computing mapreduce metadata extraction

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Harvesting Underutilized Resources to Improve Responsiveness and Tolerance to Crash and Silent Faults for data-intensive Applications 10

Harvesting Underutilized Resources to Improve Responsiveness...

引用

10th IEEE International Conference on Cloud computing (CLOUD)

作者： Ganguly, Debashis Mofrad, Mohammad H. Znati, Taieb Melhem, Rami Lange, John R. Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781538619933

Low latency is critical for emerging data-intensive real-time analytic and interactive single-wave applications. As the complexity and heterogeneity of cloud computing continue to increase, so will the frequency of errors, which will manifest themselves in unpredictable ways with a significant impact on the correctness of the computing and responsiveness of cloud services. In this paper, we propose a new data-centric computational model to improve the responsiveness of the data-intensive applications to crash faults and augment its ability to deal with silent errors to ensure computational accuracy. The basic tenet of the proposed model is a task replication scheme, which interweaves the processing of a replicated data split among multiple distributed tasks, with each task consuming data at a different offset. In the absence of a failure, the concurrent execution of the tasks ensures complete processing of the data split, with a significant reduction in the total execution time. In the case of an error, however, the remaining tasks take over the execution of the unfinished work and finish on time. The proposed scheme also guarantees timely detection and correction of silent data corruptions along with crash faults. We demonstrate the effectiveness of our scheme by extending Hadoop's MapReduce code base as a case study. Results show a performance improvement of 50% over Hadoop's Speculative Execution when dealing with crash-faults and an improvement of 33% when dealing with silent errors in case of no failure.

关键词： fault-tolerance data-intensive computing replication crash faults silent errors

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PAC: Paged Adaptive Coalescer for 3D-Stacked Memory 20

PAC: Paged Adaptive Coalescer for 3D-Stacked Memory

引用

29th International Conference on High-Performance Parallel and Distributed computing (HPDC)

作者： Wang, Xi Leidel, John D. Williams, Brody Chen, Yong Texas Tech Univ Lubbock TX 79409 USA Tactical Comp Labs Muenster TX 76252 USA

ISBN: (纸本)9781450370523

Many contemporary data-intensive applications exhibit irregular and highly concurrent memory access patterns and thus challenge the performance of conventional memory systems. Driven by an expanding need for high-bandwidth memory featuring low access latency, 3D-stacked memory devices, such as the Hybrid Memory Cube (HMC) and High Bandwidth Memory (HBM), were designed to provide significantly higher throughput as compared to standard JEDEC DDR devices. However, existing memory interfaces and coalescing models, designed for conventional DDR devices, are unable to fully exploit the bandwidth potential inherent in these new 3D-stacked memory devices. In order to remedy this disparity, we introduce in this work a novel paged adaptive coalescer (PAC) infrastructure with a scalable coalescing network for 3D-stacked memory. We present the design and simulated implementation of this approach on RISC-V embedded cores with attached HMC devices. We have carried out extensive evaluations and the results show that the proposed PAC methodology yields an average coalescing efficiency of 56.01%. Further, our evaluation results also show that the PAC reduces bank conflicts and the power consumption by 85.16% and 59.21%, respectively. Overall, PAC achieves an average performance gain of 14.35% (and up to 26.06%) across 14 test suites. These results showcase the potential of the PAC methodology as applied to architecture design for increasingly critical data-intensive algorithms and applications.

关键词： 3D-stacked memory Memory coalescing data-intensive computing

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MPI-DB, A Parallel database Services Software Library for Scientific computing

MPI-DB, A Parallel Database Services Software Library for Sc...

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18th European MPI Users Group Meeting (EuroMPI 2011)

作者： Givelberg, Edward Szalay, Alexander Kanov, Kalin Burns, Randal Johns Hopkins Univ Dept Phys & Astron Baltimore MD 21218 USA Johns Hopkins Univ Dept Phys & Astron Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9783642244483

Large-scale scientific simulations generate petascale data sets subsequently analyzed by groups of researchers, often in databases. We developed a software library, MPI-DB, to provide database services to scientific computing applications. As a bridge between CPU-intensive and data-intensive computations, MPI-DB exploits massive parallelism within large databases to provide scalable, fast service. It is built as a client-server framework, using MPI, with MPI-DB server acting as an intermediary between the user application running an MRI-DB client and the database servers. MPI-DB provides high-level objects such as multi-dimensional arrays, acting as an abstraction layer that effectively hides the database from the end user.

关键词： databases data-intensive computing software library

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Toward Millions of File System IOPS on Low-Cost, Commodity Hardware 13

Toward Millions of File System IOPS on Low-Cost, Commodity H...

引用

International Conference for High Performance computing, Networking, Storage and Analysis (SC)

作者： Zheng, Da Burns, Randal Szalay, Alexander S. Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781450323789

We describe a storage system that removes I/O bottlenecks to achieve more than one million TOPS based on a user-space file abstraction for arrays of commodity SSDs. The file abstraction refactors I/O scheduling and placement for extreme parallelism and non-uniform memory and I/O. The system includes a set-associative, parallel page cache in the user space. We redesign page caching to eliminate CPU overhead and lock-contention in non-uniform memory architecture machines. We evaluate our design on a 32 core NUMA machine with four, eight-core processors. Experiments show that our design delivers 1.23 million 512-byte read TOPS. The page cache realizes the scalable TOPS of Linux asynchronous I/O (AIO) and increases user-perceived I/O performance linearly with cache hit rates. The parallel, set-associative cache matches the cache hit rates of the global Linux page cache under real workloads.

关键词： data-intensive computing page cache optimization millions of IOPS low cost solid-state storage devices

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GEN: A database Interface Generator for HPC Programs 15

GEN: A Database Interface Generator for HPC Programs

引用

27th International Conference on Scientific and Statistical database Management (SSDBM)

作者： Quan Pham Malik, Tanu Univ Chicago Computat Inst Chicago IL 60637 USA Argonne Natl Lab Chicago IL 60637 USA

ISBN: (纸本)9781450337090

In this paper, we present GEN an interface generator that takes user-supplied C declarations and provides the necessary interface needed to load and access data from common scientific array databases such as SciDB and Rasdaman. GEN can be used for storing the output of parallel computations directly into the database and automates the previously used inefficient ingestion process which requires development of special database schemas for each computation. Further, GEN requires no modifications to existing C code and can build a working interface in minutes. We show how GEN can be used for Cosmology analysis programs to output data sets in real-time to a database and use for subsequent analysis. We show that GEN introduces modest overhead in program execution but is more efficient than writing to files and then loading. More significantly, it significantly reduces the programmatic overhead of learning new database languages.

关键词： scientific data management data-intensive computing data organization

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Effective Caching Techniques for Accelerating Pattern Matching Queries 2

Effective Caching Techniques for Accelerating Pattern Matchi...

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IEEE International Conference on Big data

作者： Fard, Arash Manda, Satya Ramaswamy, Lakshmish Miller, John A. Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9781479956661

Using caching techniques to improve response time of queries is a proven approach in many contexts. However, it is not well explored for subgraph pattern matching queries, mainly because of subtleties enforced by traditional pattern matching models. Indeed, efficient caching can greatly impact the query answering performance for massive graphs in any query engine whether it is centralized or distributed. This paper investigates the capabilities of the newly introduced pattern matching models in graph simulation family for this purpose. We propose a novel caching technique, and show how the results of a query can be used to answer the new similar queries according to the similarity measure that is introduced. Using large real-world graphs, we experimentally verify the efficiency of the proposed technique in answering subgraph pattern matching queries.

关键词： data-intensive computing subgraph pattern matching caching technique graph simulation subgraph isomorphism

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