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检索条件"主题词=Data-Intensive Computing"

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Understanding performance of distributed data-intensive applications

Understanding performance of distributed data-intensive appl...

引用

UK e-Science All Hands Meeting

作者： Miceli, Christopher Miceli, Michael Rodriguez-Milla, Bety Jha, Shantenu Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Louisiana State Univ Dept Comp Sci Baton Rouge LA 70803 USA

Grids, clouds and cloud-like infrastructures are capable of supporting a broad range of data-intensive applications. There are interesting and unique performance issues that appear as the volume of data and degree of distribution increases. New scalable data-placement and management techniques, as well as novel approaches to determine the relative placement of data and computational workload, are required. We develop and study a genome sequence matching application that is simple to control and deploy, yet serves as a prototype of a data-intensive application. The application uses a SAGA-based implementation of the All-Pairs pattern. This paper aims to understand some of the factors that influence the performance of this application and the interplay of those factors. We also demonstrate how the SAGA approach can enable data-intensive applications to be extensible and interoperable over a range of infrastructure. This capability enables us to compare and contrast two different approaches for executing distributed data-intensive applications-simple application-level data-placement heuristics versus distributed file systems.

关键词： data-intensive computing distributed computing cloud computing grid computing

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Efficient Event Correlation over Distributed Systems 17

Efficient Event Correlation over Distributed Systems

引用

17th IEEE/ACM International Symposium on Cluster, Cloud and Grid computing (CCGRID)

作者： Cheng, Long van Dongen, Boudewijn F. van der Aalst, Wil M. P. Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands

ISBN: (纸本)9781509066117

Event correlation is a cornerstone for process discovery over event logs crossing multiple data sources. The computed correlation rules and process instances will greatly help us to unleash the power of process mining. However, exploring all possible event correlations over a log could be time consuming, especially when the log is large. State-of-the-art methods based on MapReduce designed to handle this challenge have offered significant performance improvements over standalone implementations. However, all existing techniques are still based on a conventional generating-and-pruning scheme. Therefore, event partitioning across multiple machines is often inefficient. In this paper, following the principle of filtering-and-verification, we propose a new algorithm, called RF-GraP, which provides a more efficient correlation over distributed systems. We present the detailed implementation of our approach and conduct a quantitative evaluation using the Spark platform. Experimental results demonstrate that the proposed method is indeed efficient. Compared to the state-of-the-art, we are able to achieve significant performance speedups with obviously less network communication.

关键词： event correlation process mining service computing data partitioning big data data-intensive computing

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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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Towards Energy Efficient data Management in HPC: The Open Ethernet Drive Approach 1

Towards Energy Efficient Data Management in HPC: The Open Et...

引用

1st Joint International Workshop on Parallel data Storage and data intensive Scalable computing Systems (PDSW-DISCS)

作者： Kougkas, Anthony Fleck, Anthony Sun, Xian-He IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781509052165

An Open Ethernet Drive (OED) is a new technology that encloses into a hard drive (HDD or SSD) a low-power processor, a fixed-size memory and an Ethernet card. In this study, we thoroughly evaluate the performance of such device and the energy requirements to operate it. The results show that first it is a viable solution to offload data-intensive computations on the OED while maintaining a reasonable performance, and second, the energy consumption savings from utilizing such technology are significant as it only consumes 10% of the power needed by a normal server node. We propose that by using OED devices as storage servers in HPC, we can run a reliable, scalable, cost and energy efficient storage solution.

关键词： Open Ethernet Drives data Management data-intensive computing Energy Efficiency Benchmarking Performance Evaluation

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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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On Realizing Efficient Deep Learning Using Serverless computing 22

On Realizing Efficient Deep Learning Using Serverless Comput...

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22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Assogba, Kevin Arif, Moiz Rafique, M. Mustafa Nikolopoulos, Dimitrios S. Rochester Inst Technol Rochester NY 14623 USA Virginia Tech Blacksburg VA USA

ISBN: (纸本)9781665499569

Serverless computing is gaining rapid popularity as it enables quick application deployment and seamless application scaling without managing complex computing resources. Recently, it has been explored for running data-intensive, e.g., deep learning (DL), workloads for improving application performance and reducing execution cost. However, serverless computing imposes resource-level constraints, specifically fixed memory allocation and short task timeouts, that lead to job failures. In this paper, we address these constraints and develop an effective runtime framework, DiSDeL, that improves the performance of DL jobs by leveraging data splitting techniques, and ensuring that an appropriate amount of memory is allocated to containers for storing application data and a suitable timeout is selected for each job based on its complexity in serverless deployments. We implement our approach using Apache OpenWhisk and TensorFlow platforms and evaluate it using representative DL workloads to show that it eliminates DL job failures and reduces action memory consumption and total training time by up to 44% and 46%, respectively as compared to a default serverless computing framework. Our evaluation also shows that DiSDeL achieves a performance improvement of up to 29% as compared to bare-metal TensorFlow environment in a multi-tenant setting.

关键词： data-intensive computing Serverless computing Deep Learning data Parallelism OpenWhisk TensorFlow

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ToMaR - A data Generator for Large Volumes of Content 14

ToMaR - A Data Generator for Large Volumes of Content

引用

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

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

引用

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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Fast data Analysis with Integrated Statistical Metadata in Scientific datasets

Fast Data Analysis with Integrated Statistical Metadata in S...

引用

15th IEEE International Conference on Cluster computing (CLUSTER)

作者： Liu, Jialin Chen, Yong Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9781479908981

Scientific datasets and libraries, such as HDF5, ADIOS, and NetCDF, have been used widely in many data-intensive applications. These libraries have their special file formats and I/O functions to provide efficient access to large datasets. Recent studies have started to utilize indexing, subsetting, and data reorganization to manage the increasingly large datasets. In this work, we present an approach to boost the data analysis performance, namely Fast Analysis with Statistical Metadata (FASM), via data subsetting and integrating a small amount of statistics into the original datasets. The added statistical information illustrates the data shape and provides knowledge of the data distribution;therefore the original I/O libraries can utilize these statistical metadata to perform fast queries and analyses. Various subsetting schemes can affect the access pattern and the I/O performance. We present a comparison study of different subsetting schemes by focusing on three dominant factors, the shape, the concurrency, and the locality. The added statistical metadata slightly increases the original data size, and we evaluate the cost and trade-off as well. This work is the first study that utilizes statistical metadata with various subsetting schemes to perform fast queries and analyses on large datasets. The proposed FASM approach is currently evaluated with the PnetCDF on Lustre file systems, but can also be implemented with other scientific libraries. The FASM can potentially lead to a new dataset design and can have an impact on big data analysis.

关键词： high performance computing data-intensive computing big data storage systems statistical techniques FASM

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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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