refs.bib

@article{liu2014hello,
	Author = {Liu, Qing and Logan, Jeremy and Tian, Yuan and Abbasi, Hasan and Podhorszki, Norbert and Choi, Jong Youl and Klasky, Scott and Tchoua, Roselyne and Lofstead, Jay and Oldfield, Ron and others},
	Date-Added = {2019-08-30 17:05:47 -0400},
	Date-Modified = {2019-08-30 17:05:47 -0400},
	Journal = {Concurrency and Computation: Practice and Experience},
	Number = {7},
	Pages = {1453--1473},
	Publisher = {Wiley Online Library},
	Title = {Hello {ADIOS}: the challenges and lessons of developing leadership class {I/O} frameworks},
	Volume = {26},
	Year = {2014}}
	
@article{dominski2018,
author = {Dominski,J.  and Ku,S.  and Chang,C.-S.  and Choi,J.  and Suchyta,E.  and Parker,S.  and Klasky,S.  and Bhattacharjee,A. },
title = {A tight-coupling scheme sharing minimum information across a spatial interface between gyrokinetic turbulence codes},
journal = {Physics of Plasmas},
volume = {25},
number = {7},
pages = {072308},
year = {2018},
doi = {10.1063/1.5044707},
URL = {https://doi.org/10.1063/1.5044707},
eprint = {https://doi.org/10.1063/1.5044707}
}

@inproceedings{kress2019comparing,
  title={Comparing the efficiency of in situ visualization paradigms at scale},
  author={Kress, James and Larsen, Matthew and Choi, Jong and Kim, Mark and Wolf, Matthew and Podhorszki, Norbert and Klasky, Scott and Childs, Hank and Pugmire, David},
  booktitle={International Conference on High Performance Computing},
  pages={99--117},
  year={2019},
  organization={Springer}
}

@inproceedings{kress2020Cost,
    title={Opportunities for Cost Savings with In-transit Visualization},
    author={Kress, James and Larsen, Matthew and Choi, Jong and Kim, Mark and Wolf, Matthew and
    Podhorszki, Norbert and Klasky, Scott and Childs, Hank and Pugmire, David},
    author+an = {1=highlight},
    booktitle={ISC High Performance 2020},
    year={2020},
    organization={ISC},
    keywords={conference}
}

@inproceedings{Kress-isav15,
    author = {Kress, James and others},
    title = {Loosely Coupled In Situ Visualization: A Perspective on Why It's
    Here to Stay},
    booktitle = {Proceedings of the First Workshop on In Situ Infrastructures
    for Enabling Extreme-Scale Analysis and Visualization},
    series = {ISAV2015},
    year = {2015},
    isbn = {978-1-4503-4003-8},
    location = {Austin, TX, USA},
    pages = {1--6},
    numpages = {6},
    doi = {10.1145/2828612.2828623},
    acmid = {2828623},
    publisher = {ACM},
    address = {New York, NY, USA},
    keywords = {In situ, Loosely coupled in situ, Scientific visualization,
    Tightly coupled in situ, Visualization techniques and methodologies,
    perspective},
}

@inproceedings{paraview,
    author = {Ahrens, James and Geveci, Berk and Law, Charles},
    title = "Visualization in the ParaView Framework",
    booktitle = {The Visualization Handbook},
    editor = {Hansen, Chuck and Johnson, Chris},
    year = {2005},
    pages = {162--170},
}

@INPROCEEDINGS{escience2018,
author={J. Y. {Choi} and C. {Chang} and J. {Dominski} and S. {Klasky} and G. {Merlo} and E. {Suchyta} and M. {Ainsworth} and B. {Allen} and F. {Cappello} and M. {Churchill} and P. {Davis} and S. {Di} and G. {Eisenhauer} and S. {Ethier} and I. {Foster} and B. {Geveci} and H. {Guo} and K. {Huck} and F. {Jenko} and M. {Kim} and J. {Kress} and S. {Ku} and Q. {Liu} and J. {Logan} and A. {Malony} and K. {Mehta} and K. {Moreland} and T. {Munson} and M. {Parashar} and T. {Peterka} and N. {Podhorszki} and D. {Pugmire} and O. {Tugluk} and R. {Wang} and B. {Whitney} and M. {Wolf} and C. {Wood}},
booktitle={2018 IEEE 14th International Conference on e-Science (e-Science)},
title={Coupling Exascale Multiphysics Applications: Methods and Lessons Learned},
year={2018},
volume={},
number={},
pages={442-452},
keywords={computational complexity;natural sciences computing;parallel processing;scheduling;workflow management software;physical descriptions;physics components;fusion science scenario;code coupling capability;continuous performance monitoring;HPC resources;workflow scheduling;in-memory communications;leveraging capabilities;characteristic lengths;multiple individual scientific applications;larger scientific computational experiments;computational codes;traditional monolithic design;emerging hardware;growing computational complexity;coupling exascale multiphysics applications;science content;online analysis;situ analysis;Couplings;Computational modeling;Physics;Tokamak devices;Monitoring;Complexity theory;Mathematical model;coupling;in situ analysis;staging},
doi={10.1109/eScience.2018.00133},
ISSN={null},
month={Oct},}


@article{ainsworth2019multilevel,
  title={Multilevel Techniques for Compression and Reduction of Scientific Data---The Multivariate Case},
  author={Ainsworth, Mark and Tugluk, Ozan and Whitney, Ben and Klasky, Scott},
  journal={SIAM Journal on Scientific Computing},
  volume={41},
  number={2},
  pages={A1278--A1303},
  year={2019},
  publisher={SIAM}
}

@article{ainsworth2018multilevel,
  title={Multilevel techniques for compression and reduction of scientific data—the univariate case},
  author={Ainsworth, Mark and Tugluk, Ozan and Whitney, Ben and Klasky, Scott},
  journal={Computing and Visualization in Science},
  volume={19},
  number={5-6},
  pages={65--76},
  year={2018},
  publisher={Springer}
}
  
@article{ainsworth2019multilevel2,
  title={Multilevel techniques for compression and reduction of scientific data-quantitative control of accuracy in derived quantities},
  author={Ainsworth, Mark and Tugluk, Ozan and Whitney, Ben and Klasky, Scott},
  journal={SIAM Journal on Scientific Computing},
  volume={41},
  number={4},
  pages={A2146--A2171},
  year={2019},
  publisher={SIAM}
}
  
  
  @inproceedings{liang2018error,
  title={Error-controlled lossy compression optimized for high compression ratios of scientific datasets},
  author={Liang, Xin and Di, Sheng and Tao, Dingwen and Li, Sihuan and Li, Shaomeng and Guo, Hanqi and Chen, Zizhong and Cappello, Franck},
  booktitle={2018 IEEE International Conference on Big Data (Big Data)},
  pages={438--447},
  year={2018},
  organization={IEEE}
}

@inproceedings{tao2017significantly,
  title={Significantly improving lossy compression for scientific data sets based on multidimensional prediction and error-controlled quantization},
  author={Tao, Dingwen and Di, Sheng and Chen, Zizhong and Cappello, Franck},
  booktitle={2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
  pages={1129--1139},
  year={2017},
  organization={IEEE}
}

@inproceedings{di2016fast,
  title={Fast error-bounded lossy HPC data compression with SZ},
  author={Di, Sheng and Cappello, Franck},
  booktitle={2016 ieee international parallel and distributed processing symposium (ipdps)},
  pages={730--739},
  year={2016},
  organization={IEEE}
}

@inproceedings{liang2018efficient,
  title={An efficient transformation scheme for lossy data compression with point-wise relative error bound},
  author={Liang, Xin and Di, Sheng and Tao, Dingwen and Chen, Zizhong and Cappello, Franck},
  booktitle={2018 IEEE International Conference on Cluster Computing (CLUSTER)},
  pages={179--189},
  year={2018},
  organization={IEEE}
}

@article{lindstrom2014fixed,
  title={Fixed-rate compressed floating-point arrays},
  author={Lindstrom, Peter},
  journal={IEEE transactions on visualization and computer graphics},
  volume={20},
  number={12},
  pages={2674--2683},
  year={2014},
  publisher={IEEE}
}

@article{seward1996bzip2,
  title={bzip2 and libbzip2},
  author={Seward, Julian},
  journal={avaliable at http://www. bzip. org},
  year={1996}
}

@article{chenunderstanding,
  title={Understanding Performance-Quality Trade-offs in Scientific Visualization Workflows with Lossy Compression},
  author={Chen, Jieyang and Pugmire, David and Wolf, Matthew and Thompson, Nicholas and Logan, Jeremy and Mehta, Kshitij and Wan, Lipeng and Choi, Jong Youl and Whitney, Ben and Klasky, Scott}
}

@article{moreland2016,
author = {Moreland, Kenneth and Sewell, Christopher and Usher, William and Lo, Li-Ta and Meredith, Jeremy and Pugmire, David and Kress, James and Schroots, Hendrik and Ma, Kwan-Liu and Childs, Hank and Larsen, Matthew and Chen, Chun-Ming and Maynard, Robert and Maynard, Berk},
year = {2016},
month = {05},
pages = {48-58},
title = {VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures},
volume = {36},
journal = {IEEE Computer Graphics and Applications},
doi = {10.1109/MCG.2016.48}
}

@InCollection{HPV:VisIt,
author = {Hank Childs and Eric Brugger and Brad Whitlock and Jeremy Meredith and Sean Ahern and David Pugmire and Kathleen Biagas and Mark Miller and Cyrus Harrison and Gunther H. Weber and Hari Krishnan and Thomas Fogal and Allen Sanderson and Christoph Garth and E. Wes Bethel and David Camp and Oliver R\"{u}bel and Marc Durant and Jean M. Favre and Paul Navr\'{a}til},
title = {{VisIt: An End-User Tool For Visualizing and Analyzing Very Large Data}},
year = "2012",
pages = "357-372",
month = "Oct",
booktitle = {{High Performance Visualization--Enabling Extreme-Scale Scientific Insight}},
}

@InProceedings{kress2019binning,
author="Kress, James
and Choi, Jong
and Klasky, Scott
and Churchill, Michael
and Childs, Hank
and Pugmire, David",
editor="Yokota, Rio
and Weiland, Mich{\`e}le
and Shalf, John
and Alam, Sadaf",
title="Binning Based Data Reduction for Vector Field Data of a Particle-In-Cell Fusion Simulation",
booktitle="High Performance Computing",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="215--229",
abstract="With this work, we explore the feasibility of using in situ data binning techniques to achieve significant data reductions for particle data, and study the associated errors for several post-hoc analysis techniques. We perform an application study in collaboration with fusion simulation scientists on data sets up to 489 GB per time step. We consider multiple ways to carry out the binning, and determine which techniques work the best for this simulation. With the best techniques we demonstrate reduction factors as large as 109x with low error percentage.",
isbn="978-3-030-02465-9"
}

@article{Huebl2017,
author = {Huebl, Axel and Widera, Ren{\'{e}} and Schmitt, Felix and Matthes, Alexander and Podhorszki, Norbert and Choi, Jong Youl and Klasky, Scott and Bussmann, Michael},
doi = {10.1007/978-3-319-67630-2_2},
isbn = {9783319676296},
issn = {16113349},
journal = {Lect. Notes Comput. Sci.},
keywords = {diss,own},
mendeley-tags = {diss,own},
number = {4},
pages = {15--29},
title = {On the scalability of data reduction techniques in current and upcoming {HPC} systems from an application perspective},
volume = {10524},
year = {2017}
}

@article{HueblopenPMD,
  author = {Huebl, Axel and
            Lehe, Rémi and
            Vay, Jean-Luc and
            Grote, David P. and
            Sbalzarini, Ivo F. and
            Kuschel, Stephan and
            Sagan, David and
            P{\'e}rez, Fr{\'e}d{\'e}ric and
            Koller, Fabian and
            Bussmann, Michael},
  title= {{openPMD}: A meta data standard for particle and mesh based data.},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.591699},
  url = {https://www.openPMD.org}
}

@inproceedings {libsim,
booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
editor = {Torsten Kuhlen and Renato Pajarola and Kun Zhou},
title = {{Parallel In Situ Coupling of Simulation with a Fully Featured Visualization System}},
author = {Whitlock, Brad and Favre, Jean M. and Meredith, Jeremy S.},
year = {2011},
publisher = {The Eurographics Association},
ISSN = {1727-348X},
ISBN = {978-3-905674-32-3},
DOI = {10.2312/EGPGV/EGPGV11/101-109}
}

@article{Childs2010,
  author    = {Hank Childs and
               David Pugmire and
               Sean Ahern and
               Brad Whitlock and
               Mark Howison and
               Prabhat and
               Gunther H. Weber and
               E. Wes Bethel},
  title     = {Extreme Scaling of Production Visualization Software on Diverse Architectures},
  journal   = {{IEEE} Computer Graphics and Applications},
  volume    = {30},
  number    = {3},
  pages     = {22--31},
  year      = {2010},
  url       = {https://doi.org/10.1109/MCG.2010.51},
  doi       = {10.1109/MCG.2010.51},
  timestamp = {Wed, 14 Nov 2018 10:45:17 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/cga/ChildsPAWHPWB10},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{farthing2006data,
  title={Data Management at JET with a look forward to ITER},
  author={Farthing, JW and Budd, T and Capel, AJ and Cook, N and Edwards, AM and Felton, R and Griph, FS and Jones, EM},
  booktitle={International Conference on Accelerator and Large Experimental Physics Control Systems},
  year={2006}
}

@article{yun2010development,
  title={Development of KSTAR ECE imaging system for measurement of temperature fluctuations and edge density fluctuations},
  author={Yun, GS and Lee, Woochang and Choi, MJ and Kim, JB and Park, HK and Domier, CW and Tobias, B and Liang, T and Kong, X and Luhmann Jr, NC and others},
  journal={Review of Scientific Instruments},
  volume={81},
  number={10},
  pages={10D930},
  year={2010},
  publisher={American Institute of Physics}
}

@inproceedings{choi2013icee,
  title={Icee: Wide-area in transit data processing framework for near real-time scientific applications},
  author={Choi, Jong Y and Wu, Kesheng and Wu, Jacky C and Sim, Alex and Liu, Qing G and Wolf, Matthew and Chang, C and Klasky, Scott},
  booktitle={4th SC Workshop on Petascale (Big) Data Analytics: Challenges and Opportunities in conjunction with SC13},
  volume={11},
  year={2013}
}


@article{Logan2020,
title = {Extending the Publish/Subscribe Abstraction for High-Performance I/O and Data Management at Extreme Scale},
author = {Logan, Jeremy and Ainsworth, Mark and Atkins, Chuck and Chen, Jieyang and Choi, Jong Youl and Gu, Junmin and Kress, James M. and Eisenhauer, Greg and Geveci, Berk and Godoy, William and Kim, Mark B. and Kurc, Tahsin and Liu, Qing and Mehta, Kshitij V. and Ostrouchov, George and Podhorszki, Norbert and Pugmire, David and Suchyta, Eric D. and Thompson, Nicolas and Tugluk, Ozan and Wan, Lipeng and Wang, Ruonan and Whitney, Ben and Wolf, Matthew D. and Wu, Kesheng and Klasky, Scott A.},
abstractNote = {The Adaptable I/O System (ADIOS) represents the culmination of substantial investment in Scientific Data Management, and it has demonstrated success for several important extreme-scale science cases. However, looking towards the exascale and beyond, we see the development of yet more stringent data management requirements that require new abstractions. Therefore, there is an opportunity to attempt to connect the traditional realms of HPC I/O optimization with the Database / Data Management community. As such, in this paper we offer some specific examples from our ongoing work in managing data structures, services, and performance at the extreme scale for scientific computing. Using the publish/subscribe model afforded by ADIOS, we demonstrate a set of services that connect data format, metadata, queries, data reduction, and high-performance delivery. The resulting publish/subscribe framework facilitates connection to on-line workflow systems to enable the dynamic capabilities that will be required for exascale science.},
doi = {},
journal = {Bulletin of the IEEE Technical Committee on Data Engineering},
issn = {1053-1238},
number = 1,
volume = 43,
place = {United States},
year = {2020},
month = {3}
}


@article{osti_1468120,
title = {A View from ORNL: Scientific Data Research Opportunities in the Big Data Age},
author = {Klasky, Scott A. and Wolf, Matthew D. and Ainsworth, Mark and Atkins, Chuck and Choi, Jong Youl and Eisenhauer, Greg and Geveci, Berk and Godoy, William F. and Kim, Mark B. and Kress, James M. and Kurc, Tahsin M. and Liu, Qing Gary and Logan, Jeremy S. and Maccabe, Arthur Barney and Mehta, Kshitij V. and Ostrouchov, George and Parashar, Manish and Podhorszki, Norbert and Pugmire, Dave and Suchyta, Eric D. and Wan, Lipeng and Wang, Ruonan},
abstractNote = {One of the core issues across computer and computational science today is adapting to, managing, and learning from the influx of "Big Data". In the commercial space, this problem has led to a huge investment in new technologies and capabilities that are well adapted to dealing with the sorts of human-generated logs, videos, texts, and other large-data artifacts that are processed and resulted in an explosion of useful platforms and languages (Hadoop, Spark, Pandas, etc.). However, translating this work from the enterprise space to the computational science and HPC community has proven somewhat difficult, in part because of some of the fundamental differences in type and scale of data and timescales surrounding its generation and use. We describe a forward-looking research and development plan which centers around the concept of making Input/Output (I/O) intelligent for users in the scientific community, whether they are accessing scalable storage or performing in situ workflow tasks. Much of our work is based on our experience with the Adaptable I/O System (ADIOS 1.X), and our next generation version of the software ADIOS 2.X [1].},
doi = {10.1109/ICDCS.2018.00136},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {7}
}

@article{Godoy2020,
  doi = {10.1016/j.softx.2020.100561},
  url = {https://doi.org/10.1016/j.softx.2020.100561},
  year = {2020},
  month = jul,
  publisher = {Elsevier {BV}},
  volume = {12},
  pages = {100561},
  author = {William F. Godoy and Norbert Podhorszki and Ruonan Wang and Chuck Atkins and Greg Eisenhauer and Junmin Gu and Philip Davis and Jong Choi and Kai Germaschewski and Kevin Huck and Axel Huebl and Mark Kim and James Kress and Tahsin Kurc and Qing Liu and Jeremy Logan and Kshitij Mehta and George Ostrouchov and Manish Parashar and Franz Poeschel and David Pugmire and Eric Suchyta and Keichi Takahashi and Nick Thompson and Seiji Tsutsumi and Lipeng Wan and Matthew Wolf and Kesheng Wu and Scott Klasky},
  title = {{ADIOS} 2: The Adaptable Input Output System. A framework for high-performance data management},
  journal = {{SoftwareX}}
}