Relevant bibliographies by topics / Petascale data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Petascale data'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Petascale data"

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Bethel, E. W., C. Johnson, S. Ahern, J. Bell, P.-T. Bremer, H. Childs, E. Cormier-Michel, et al. "Occam's razor and petascale visual data analysis." Journal of Physics: Conference Series 180 (July 1, 2009): 012084. http://dx.doi.org/10.1088/1742-6596/180/1/012084.

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Buren, G. Van, L. Didenko, J. Lauret, E. Oldag, and L. Ray. "Automated QA framework for PetaScale data challenges." Journal of Physics: Conference Series 331, no. 4 (December 23, 2011): 042026. http://dx.doi.org/10.1088/1742-6596/331/4/042026.

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Abbasi, Hasan, Matthew Wolf, Greg Eisenhauer, Scott Klasky, Karsten Schwan, and Fang Zheng. "DataStager: scalable data staging services for petascale applications." Cluster Computing 13, no. 3 (June 15, 2010): 277–90. http://dx.doi.org/10.1007/s10586-010-0135-6.

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Ahern, Sean. "Petascale visual data analysis in a production computing environment." Journal of Physics: Conference Series 78 (July 1, 2007): 012002. http://dx.doi.org/10.1088/1742-6596/78/1/012002.

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Saksena, Radhika S., Marco D. Mazzeo, Stefan J. Zasada, and Peter V. Coveney. "Petascale lattice-Boltzmann studies of amphiphilic cubic liquid crystalline materials in a globally distributed high-performance computing and visualization environment." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1925 (August 28, 2010): 3983–99. http://dx.doi.org/10.1098/rsta.2010.0160.

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We present very large-scale rheological studies of self-assembled cubic gyroid liquid crystalline phases in ternary mixtures of oil, water and amphiphilic species performed on petascale supercomputers using the lattice-Boltzmann method. These nanomaterials have found diverse applications in materials science and biotechnology, for example, in photovoltaic devices and protein crystallization. They are increasingly gaining importance as delivery vehicles for active agents in pharmaceuticals, personal care products and food technology. In many of these applications, the self-assembled structures are subject to flows of varying strengths and we endeavour to understand their rheological response with the objective of eventually predicting it under given flow conditions. Computationally, our lattice-Boltzmann simulations of ternary fluids are inherently memory- and data-intensive. Furthermore, our interest in dynamical processes necessitates remote visualization and analysis as well as the associated transfer and storage of terabytes of time-dependent data. These simulations are distributed on a high-performance grid infrastructure using the application hosting environment; we employ a novel parallel in situ visualization approach which is particularly suited for such computations on petascale resources. We present computational and I/O performance benchmarks of our application on three different petascale systems.
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Juric, Mario, and Tony Tyson. "LSST Data Management: Entering the Era of Petascale Optical Astronomy." Proceedings of the International Astronomical Union 10, H16 (August 2012): 675–76. http://dx.doi.org/10.1017/s174392131401285x.

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AbstractThe Large Synoptic Survey Telescope (LSST; Ivezic et al.2008, http://lsst.org) is a planned, large-aperture, wide-field, ground-based telescope that will survey half the sky every few nights in six optical bands from 320 to 1050 nm. It will explore a wide range of astrophysical questions, ranging from discovering killer asteroids, to examining the nature of dark energy. LSST will produce on average 15 terabytes of data per night, yielding an (uncompressed) data set of 200 petabytes at the end of its 10-year mission. Dedicated HPC facilities (with a total of 320 TFLOPS at start, scaling up to 1.7 PFLOPS by the end) will process the image data in near real time, with full-dataset reprocessing on annual scale. The nature, quality, and volume of LSST data will be unprecedented, so the data system design requires petascale storage, terascale computing, and gigascale communications.
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Beyer, J., M. Hadwiger, A. Al-Awami, Won-Ki Jeong, N. Kasthuri, J. W. Lichtman, and H. Pfister. "Exploring the Connectome: Petascale Volume Visualization of Microscopy Data Streams." IEEE Computer Graphics and Applications 33, no. 4 (July 2013): 50–61. http://dx.doi.org/10.1109/mcg.2013.55.

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Huang, Huang, Li-Qian Zhou, YuTong Lu, Tong Xiao, Can Leng, Chuanying Li, and Zhe Quan. "An efficient real-time data collection framework on petascale systems." Neurocomputing 361 (October 2019): 100–109. http://dx.doi.org/10.1016/j.neucom.2019.06.039.

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Baranovski, A., K. Beattie, S. Bharathi, J. Boverhof, J. Bresnahan, A. Chervenak, I. Foster, et al. "Enabling petascale science: data management, troubleshooting, and scalable science services." Journal of Physics: Conference Series 125 (July 1, 2008): 012068. http://dx.doi.org/10.1088/1742-6596/125/1/012068.

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Kosar, Tevfik, Mehmet Balman, Esma Yildirim, Sivakumar Kulasekaran, and Brandon Ross. "Stork data scheduler: mitigating the data bottleneck in e-Science." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369, no. 1949 (August 28, 2011): 3254–67. http://dx.doi.org/10.1098/rsta.2011.0148.

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In this paper, we present the Stork data scheduler as a solution for mitigating the data bottleneck in e-Science and data-intensive scientific discovery. Stork focuses on planning, scheduling, monitoring and management of data placement tasks and application-level end-to-end optimization of networked inputs/outputs for petascale distributed e-Science applications. Unlike existing approaches, Stork treats data resources and the tasks related to data access and movement as first-class entities just like computational resources and compute tasks, and not simply the side-effect of computation. Stork provides unique features such as aggregation of data transfer jobs considering their source and destination addresses, and an application-level throughput estimation and optimization service. We describe how these two features are implemented in Stork and their effects on end-to-end data transfer performance.
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Dissertations / Theses on the topic "Petascale data"

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Abbasi, Mohammad Hasan. "Data services: bringing I/O processing to petascale." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42694.

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The increasing size of high performance computing systems and the associated increase in the volume of generated data, has resulted in an I/O bottleneck for these applications. This bottleneck is further exacerbated by the imbalance in the growth of processing capability compared to storage capability, due mainly to the power and cost requirements of scaling the storage. This thesis introduces data services, a new abstraction which provides significant benefits for data intensive applications. Data services combine low overhead data movement with flexible placement of data manipulation operations, to address the I/O challenges of leadership class scientific applications. The impact of asynchronous data movement on application runtime is minimized by utilizing novel server side data movement schedulers to avoid contention related jitter in application communication. Additionally, the JITStager component is presented. Utilizing dynamic code generation and flexible code placement, the JITStager allows data services to be executed as a pipeline extending from the application to storage. It is shown in this thesis that data services can add new functionality to the application without having an significant negative impact on performance.
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Al-Awami, Ali K. "Unraveling The Connectome: Visualizing and Abstracting Large-Scale Connectomics Data." Diss., 2017. http://hdl.handle.net/10754/623401.

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We explore visualization and abstraction approaches to represent neuronal data. Neuroscientists acquire electron microscopy volumes to reconstruct a complete wiring diagram of the neurons in the brain, called the connectome. This will be crucial to understanding brains and their development. However, the resulting data is complex and large, posing a big challenge to existing visualization techniques in terms of clarity and scalability. We describe solutions to tackle the problems of scalability and cluttered presentation. We first show how a query-guided interactive approach to visual exploration can reduce the clutter and help neuroscientists explore their data dynamically. We use a knowledge-based query algebra that facilitates the interactive creation of queries. This allows neuroscientists to pose domain-specific questions related to their research. Simple queries can be combined to form complex queries to answer more sophisticated questions. We then show how visual abstractions from 3D to 2D can significantly reduce the visual clutter and add clarity to the visualization so that scientists can focus more on the analysis. We abstract the topology of 3D neurons into a multi-scale, relative distance-preserving subway map visualization that allows scientists to interactively explore the morphological and connectivity features of neuronal cells. We then focus on the process of acquisition, where neuroscientists segment electron microscopy images to reconstruct neurons. The segmentation process of such data is tedious, time-intensive, and usually performed using a diverse set of tools. We present a novel web-based visualization system for tracking the state, progress, and evolution of segmentation data in neuroscience. Our multi-user system seamlessly integrates a diverse set of tools. Our system provides support for the management, provenance, accountability, and auditing of large-scale segmentations. Finally, we present a novel architecture to render very large volumes interactively. We focus on two aspects: (1) Segmented objects are often toggled on and off by an interactive query, which makes it unfeasible to pre-compute a well-adapted space subdivision. (2) To scale to large data, culling and empty-space skipping must scale with the output size instead of the input volume. Our approach combines the advantages of object- and image-order stages of the empty-space skipping process.
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Books on the topic "Petascale data"

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1969-, Lehner Wolfgang, ed. Euro-Par 2006 workshops: Parallel processing : CoreGRID 2006, UNICORE Summit 2006, Petascale Computational Biology and Bioinformatics, Dresden, Germany, August 29-September 1, 2006 : revised selected papers. Berlin: Springer, 2007.

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Book chapters on the topic "Petascale data"

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Van Straalen, Brian, Phil Colella, Daniel T. Graves, and Noel Keen. "Petascale Block-Structured AMR Applications without Distributed Meta-data." In Euro-Par 2011 Parallel Processing, 377–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23397-5_37.

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Schumann, Till, Csaba Erő, Marc-Oliver Gewaltig, and Fabien Jonathan Delalondre. "Towards Simulating Data-Driven Brain Models at the Point Neuron Level on Petascale Computers." In Lecture Notes in Computer Science, 160–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53862-4_14.

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Evans, Ben, Lesley Wyborn, Tim Pugh, Chris Allen, Joseph Antony, Kashif Gohar, David Porter, et al. "The NCI High Performance Computing and High Performance Data Platform to Support the Analysis of Petascale Environmental Data Collections." In IFIP Advances in Information and Communication Technology, 569–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15994-2_58.

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Yıldırım, Ahmet Artu, Cem Özdoğan, and Dan Watson. "Parallel Data Reduction Techniques for Big Datasets." In Big Data, 734–56. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch034.

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Data reduction is perhaps the most critical component in retrieving information from big data (i.e., petascale-sized data) in many data-mining processes. The central issue of these data reduction techniques is to save time and bandwidth in enabling the user to deal with larger datasets even in minimal resource environments, such as in desktop or small cluster systems. In this chapter, the authors examine the motivations behind why these reduction techniques are important in the analysis of big datasets. Then they present several basic reduction techniques in detail, stressing the advantages and disadvantages of each. The authors also consider signal processing techniques for mining big data by the use of discrete wavelet transformation and server-side data reduction techniques. Lastly, they include a general discussion on parallel algorithms for data reduction, with special emphasis given to parallel wavelet-based multi-resolution data reduction techniques on distributed memory systems using MPI and shared memory architectures on GPUs along with a demonstration of the improvement of performance and scalability for one case study.
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Fiore, Sandro, Alessandro Negro, Salvatore Vadacca, Massimo Cafaro, Giovanni Aloisio, Roberto Barbera, and Emidio Giorgio. "An Architectural Overview of the GRelC Data Access Service." In Grid and Cloud Computing, 517–27. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0879-5.ch211.

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Grid computing is an emerging and enabling technology allowing organizations to easily share, integrate and manage resources in a distributed environment. Computational Grid allows running millions of jobs in parallel, but the huge amount of generated data has caused another interesting problem: the management (classification, storage, discovery etc.) of distributed data, i.e., a Data Grid specific issue. In the last decade, many efforts concerning the management of data (grid-storage services, metadata services, grid-database access and integration services, etc.) identify data management as a real challenge for the next generation petascale grid environments. This work provides an architectural overview of the GRelC DAS, a grid database access service developed in the context of the GRelC Project and currently used for production/tutorial activities both in gLite and Globus based grid environments.
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Fiore, Sandro, Alessandro Negro, Salvatore Vadacca, Massimo Cafaro, Giovanni Aloisio, and Roberto Barbera. "An Architectural Overview of the GRelC Data Access Service." In Handbook of Research on Grid Technologies and Utility Computing, 98–108. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-184-1.ch010.

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Grid computing is an emerging and enabling technology allowing organizations to easily share, integrate and manage resources in a distributed environment. Computational Grid allows running millions of jobs in parallel, but the huge amount of generated data has caused another interesting problem: the management (classification, storage, discovery etc.) of distributed data, i.e., a Data Grid specific issue. In the last decade, many efforts concerning the management of data (grid-storage services, metadata services, grid-database access and integration services etc.) identify data management as a real challenge for the next generation petascale grid environments. This work provides an architectural overview of the GRelC DAS, a grid database access service developed in the context of the GRelC Project and currently used for production/tutorial activities both in gLite and Globus based grid environments.
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Conference papers on the topic "Petascale data"

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Gibson, Garth. "Petascale data storage---Petascale data storage." In the 2006 ACM/IEEE conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1188455.1188701.

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Leung, Andrew W., and Ethan L. Miller. "Scalable full-text search for petascale file systems." In 2008 3rd Petascale Data Storage Workshop, PDSW. IEEE, 2008. http://dx.doi.org/10.1109/pdsw.2008.4811884.

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Lofstead, Jay, Fang Zheng, Scott Klasky, and Karsten Schwan. "Input/output APIs and data organization for high performance scientific computing." In 2008 3rd Petascale Data Storage Workshop, PDSW. IEEE, 2008. http://dx.doi.org/10.1109/pdsw.2008.4811881.

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Polte, Milo, Jiri Simsa, Wittawat Tantisiriroj, Garth Gibson, Shobhit Dayal, Mikhail Chainani, and Dilip Kumar Uppugandla. "Fast log-based concurrent writing of checkpoints." In 2008 3rd Petascale Data Storage Workshop, PDSW. IEEE, 2008. http://dx.doi.org/10.1109/pdsw.2008.4811882.

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Nowoczynski, Paul, Nathan Stone, Jared Yanovich, and Jason Sommerfield. "Zest Checkpoint storage system for large supercomputers." In 2008 3rd Petascale Data Storage Workshop, PDSW. IEEE, 2008. http://dx.doi.org/10.1109/pdsw.2008.4811883.

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Yu, Weikuan, Nageswara S. V. Rao, Pete Wyckoff, and Jeffrey S. Vetter. "Performance of RDMA-capable storage protocols on wide-area network." In 2008 3rd Petascale Data Storage Workshop, PDSW. IEEE, 2008. http://dx.doi.org/10.1109/pdsw.2008.4811885.

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Polte, Milo, Jiri Simsa, and Garth Gibson. "Comparing performance of solid state devices and mechanical disks." In 2008 3rd Petascale Data Storage Workshop, PDSW. IEEE, 2008. http://dx.doi.org/10.1109/pdsw.2008.4811886.

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Curry, Matthew L., Anthony Skjellum, H. Lee Ward, and Ron Brightwell. "Arbitrary dimension Reed-Solomon coding and decoding for extended RAID on GPUs." In 2008 3rd Petascale Data Storage Workshop, PDSW. IEEE, 2008. http://dx.doi.org/10.1109/pdsw.2008.4811887.

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May, John. "Pianola: A script-based I/O benchmark." In 2008 3rd Petascale Data Storage Workshop, PDSW. IEEE, 2008. http://dx.doi.org/10.1109/pdsw.2008.4811888.

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Mackey, Grant, Saba Sehrish, John Bent, Julio Lopez, Salman Habib, and Jun Wang. "Introducing map-reduce to high end computing." In 2008 3rd Petascale Data Storage Workshop, PDSW. IEEE, 2008. http://dx.doi.org/10.1109/pdsw.2008.4811889.

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Reports on the topic "Petascale data"

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Gibson, Garth, Darrell Long, Peter Honeyman, Gary Grider, William Kramer, John Shalf, Philip Roth, Evan Felix, and Lee Ward. The Petascale Data Storage Institute. Office of Scientific and Technical Information (OSTI), July 2013. http://dx.doi.org/10.2172/1120946.

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Honeyman, Peter. Petascale Data Storage Institute (Final Report). Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1176902.

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Bennett, Janine Camille, Philippe Pierre Pebay, Valerio Pascucci, Joshua Levine, Attila Gyulassy, and Maurice Rojas. Topology for Statistical Modeling of Petascale Data. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1322271.

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Pascucci, Valerio, Ajith Arthur Mascarenhas, Korben Rusek, Janine Camille Bennett, Joshua Levine, Philippe Pierre Pebay, Attila Gyulassy, David C. Thompson, and Joseph Maurice Rojas. Topology for statistical modeling of petascale data. Office of Scientific and Technical Information (OSTI), July 2011. http://dx.doi.org/10.2172/1022199.

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Gibson, Garth. PETASCALE DATA STORAGE INSTITUTE (PDSI) Final Report. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1150023.

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Pascucci, Valerio, Joshua Levine, Attila Gyulassy, and P. T. Bremer. Topology for Statistical Modeling of Petascale Data. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1347741.

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Laney, D., and H. Childs. Distributed Data-Flow for In-Situ Visualization and Analysis at Petascale. Office of Scientific and Technical Information (OSTI), March 2009. http://dx.doi.org/10.2172/1020344.

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Bethel, E. Wes, Chris Johnson, Cecilia Aragon, Oliver Rubel, Gunther Weber, Valerio Pascucci, Hank Childs, et al. DOE's SciDAC Visualization and Analytics Center for EnablingTechnologies -- Strategy for Petascale Visual Data Analysis Success. Office of Scientific and Technical Information (OSTI), October 2007. http://dx.doi.org/10.2172/932587.

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Williams, Dean. The Earth System Grid Center for Enabling Technologies (ESG-CET): Scaling the Earth System Grid to Petascale Data. Office of Scientific and Technical Information (OSTI), September 2007. http://dx.doi.org/10.2172/965467.

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Hankin, Steve. PMEL contributions to the collaboration: SCALING THE EARTH SYSTEM GRID TO PETASCALE DATA for the DOE SciDACs Earth System Grid Center for Enabling Technologies. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1042608.

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