Advertisement

Supporting High-Performance and High-Throughput Computing for Experimental Science

  • E. A. HuertaEmail author
  • Roland Haas
  • Shantenu Jha
  • Mark Neubauer
  • Daniel S. Katz
Review
  • 17 Downloads

Abstract

The advent of experimental science facilities—instruments and observatories, such as the Large Hadron Collider, the Laser Interferometer Gravitational Wave Observatory, and the upcoming Large Synoptic Survey Telescope —has brought about challenging, large-scale computational and data processing requirements. Traditionally, the computing infrastructure to support these facility’s requirements were organized into separate infrastructure that supported their high-throughput needs and those that supported their high-performance computing needs. We argue that to enable and accelerate scientific discovery at the scale and sophistication that is now needed, this separation between high-performance computing and high-throughput computing must be bridged and an integrated, unified infrastructure provided. In this paper, we discuss several case studies where such infrastructure has been implemented. These case studies span different science domains, software systems, and application requirements as well as levels of sustainability. A further aim of this paper is to provide a basis to determine the common characteristics and requirements of such infrastructure, as well as to begin a discussion of how best to support the computing requirements of existing and future experimental science facilities.

Keywords

HPC HTC LIGO CMS ATLAS Blue Waters Titan OSG Containers 

Notes

Acknowledgements

This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the State of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. We thank Brett Bode, Greg Bauer, Jeremy Enos, HonWai Leong and William Kramer for useful interactions. On behalf of all authors, the corresponding author states that there is no conflict of interest.

References

  1. 1.
    Thain D, Tannenbaum T, Livny M (2005) Distributed computing in practice: the Condor experience. Concurr Pract Exp 17(2–4):323CrossRefGoogle Scholar
  2. 2.
    Pordes R, Petravick D, Kramer B, Olson D, Livny M, Roy A, Avery P, Blackburn K, Wenaus T, Würthwein F et al (2007) The open science grid. J Phys Conf Ser 78:012057 (IOP Publishing) Google Scholar
  3. 3.
    Abbott BP, Abbott R, Abbott TD, Abernathy MR, Acernese F, Ackley K, Adams C, Adams T, Addesso P, Adhikari RX et al (2016) GW150914: the advanced LIGO detectors in the era of first discoveries. Phys Rev Lett 116(13):131103.  https://doi.org/10.1103/PhysRevLett.116.131103 ADSMathSciNetCrossRefGoogle Scholar
  4. 4.
    Aasi J et al (2015) Advanced LIGO class. Quantum Gravity 32(7):074001.  https://doi.org/10.1088/0264-9381/32/7/074001 CrossRefGoogle Scholar
  5. 5.
    Einstein A (1915) Die Feldgleichungen der Gravitation Königlich preussische akademie der wissenschaften Zu Berlin. Sitzungberichte 1915:844.  https://doi.org/10.1002/andp.19163540702 CrossRefzbMATHGoogle Scholar
  6. 6.
    Einstein A (1916) Näherungsweise Integration der Feldgleichungen der Gravitation Königlich preussische akademie der wissenschaften Zu Berlin. Sitzungberichte 1916:688.  https://doi.org/10.1002/andp.19163540702 CrossRefzbMATHGoogle Scholar
  7. 7.
    Einstein A (1918) Über Gravitationswellen Königlich preussische akademie der wissenschaften Zu Berlin. Sitzungberichte 1918:154.  https://doi.org/10.1002/andp.19163540702 CrossRefGoogle Scholar
  8. 8.
    Acernese F et al (2015) Advanced Virgo: a second-generation interferometric gravitational wave detector. Class Quantum Gravity 32(2):024001.  https://doi.org/10.1088/0264-9381/32/2/024001 (for the Virgo Collaboration) ADSCrossRefGoogle Scholar
  9. 9.
    Abbott BP, Abbott R, Abbott TD, Abernathy MR, Acernese F, Ackley K, Adams C, Adams T, Addesso P, Adhikari RX et al (2016) Observation of gravitational waves from a binary black hole merger. Phys Rev Lett 116(6):061102.  https://doi.org/10.1103/PhysRevLett.116.061102 ADSMathSciNetCrossRefGoogle Scholar
  10. 10.
    Abbott BP, Abbott R, Abbott TD, Abernathy MR, Acernese F, Ackley K, Adams C, Adams T, Addesso P, Adhikari RX et al (2016) GW151226: observation of gravitational waves from a 22-solar-mass binary black hole coalescence. Phys Rev Lett 116(24):241103.  https://doi.org/10.1103/PhysRevLett.116.241103 ADSCrossRefGoogle Scholar
  11. 11.
    Abbott BP, Abbott R, Abbott TD, Abernathy MR, Acernese F, Ackley K, Adams C, Adams T, Addesso P, Adhikari RX et al (2017) GW170104: observation of a 50-solar-mass binary black hole coalescence at redshift 0.2. Phys Rev Lett 118:221101.  https://doi.org/10.1103/PhysRevLett.118.221101
  12. 12.
    Abbott BP, Abbott R, Abbott TD, Acernese F, Ackley K, Adams C, Adams T, Addesso P, Adhikari RX, Adya VB et al (2017) GW170814: a three-detector observation of gravitational waves from a binary black hole coalescence. Phys Rev Lett 119(14):141101.  https://doi.org/10.1103/PhysRevLett.119.141101 ADSCrossRefGoogle Scholar
  13. 13.
    Abbott BP, Abbott R, Abbott TD, Acernese F, Ackley K, Adams C, Adams T, Addesso P, The LIGO Scientific Collaboration, the Virgo Collaboration et al (2017) GW170608: observation of a 19-solar-mass binary black hole coalescence. arXiv:1711.05578 [astro-ph.HE]
  14. 14.
    The LIGO Scientific Collaboration, the Virgo Collaboration, et al (2018) GWTC-1: a gravitational-wave transient catalog of compact binary mergers observed by LIGO and Virgo during the first and second observing runs. arXiv:1811.12907
  15. 15.
    Abbott BP, Abbott R, Abbott TD, Acernese F, Ackley K, Adams C, Adams T, Addesso P, Adhikari RX, Adya VB et al (2017) GW170817: observation of gravitational waves from a binary neutron star inspiral. Phys Rev Lett 119(16):161101.  https://doi.org/10.1103/PhysRevLett.119.161101 ADSCrossRefGoogle Scholar
  16. 16.
    Abbott BP, Abbott R, Abbott TD, Acernese F, Ackley K, Adams C, Adams T, Addesso P, Adhikari RX, Adya VB et al (2017) Multi-messenger observations of a binary neutron star merger. Astrophys J Lett 848:L12.  https://doi.org/10.3847/2041-8213/aa91c9 ADSCrossRefGoogle Scholar
  17. 17.
    Abbott BP, Abbott R, Abbott TD, Acernese F, Ackley K, Adams C, Adams T, Addesso P, Adhikari RX, Adya VB et al (2017) Estimating the contribution of dynamical ejecta in the kilonova associated with GW170817. Astrophys J Lett 850:L39.  https://doi.org/10.3847/2041-8213/aa9478 ADSCrossRefGoogle Scholar
  18. 18.
    Smarr L (1979) Geometry of a black hole collision. R Soc Lond Proc Ser A 368:15.  https://doi.org/10.1098/rspa.1979.0109 ADSCrossRefGoogle Scholar
  19. 19.
    Hobill DW, Smarr LL (1989) Supercomputing and numerical relativity: a look at the past, present and future supercomputing and numerical relativity: a look at the past, present and future. Cambridge Univerty Press, Cambridge, pp 1–17Google Scholar
  20. 20.
    Anninos P, Hobill D, Seidel E, Smarr L, Suen WM (1993) Collision of two black holes. Phys Rev Lett 71:2851.  https://doi.org/10.1103/PhysRevLett.71.2851 ADSCrossRefGoogle Scholar
  21. 21.
    Matzner RA, Seidel HE, Shapiro SL, Smarr L, Suen WM, Teukolsky SA, Winicour J (1995) Geometry of a black hole collision. Science 270:941.  https://doi.org/10.1126/scien.270.5238.941 ADSCrossRefGoogle Scholar
  22. 22.
    Pretorius F (2005) Evolution of binary black-hole spacetimes. Phys Rev Lett 95(12):121101.  https://doi.org/10.1103/PhysRevLett.95.121101 ADSMathSciNetCrossRefGoogle Scholar
  23. 23.
    Baker JG, Centrella J, Choi DI, Koppitz M, van Meter J (2006) Gravitational-wave extraction from an inspiraling configuration of merging black holes. Phys Rev Lett 96(11):111102.  https://doi.org/10.1103/PhysRevLett.96.111102 ADSCrossRefGoogle Scholar
  24. 24.
    Campanelli M, Lousto CO, Marronetti P, Zlochower Y (2006) Accurate evolutions of orbiting black-hole binaries without excision. Phys Rev Lett 96(11):111101.  https://doi.org/10.1103/PhysRevLett.96.111101 ADSCrossRefGoogle Scholar
  25. 25.
    Nakamura T, Oohara K, Kojima Y (1987) General relativistic collapse to black holes and gravitational waves from black holes. Prog Theor Phys Suppl 90:1.  https://doi.org/10.1143/PTPS.90.1 ADSMathSciNetCrossRefGoogle Scholar
  26. 26.
    Shibata M, Nakamura T (1995) Evolution of three-dimensional gravitational waves: harmonic slicing case. Phys Rev D 52:5428.  https://doi.org/10.1103/PhysRevD.52.5428 ADSMathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Baumgarte TW, Shapiro SL (1998) Numerical integration of Einstein’s field equations. Phys Rev D 59(2):024007.  https://doi.org/10.1103/PhysRevD.59.024007 ADSMathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Pollney D, Reisswig C, Schnetter E, Dorband N, Diener P (2011) High accuracy binary black hole simulations with an extended wave zone. Phys Rev D 83(4):044045.  https://doi.org/10.1103/PhysRevD.83.044045 ADSCrossRefGoogle Scholar
  29. 29.
    Wardell B, Hinder I, Bentivegna E (2016) Simulation of GW150914 binary black hole merger using the Einstein toolkit.  https://doi.org/10.5281/zenodo.155394
  30. 30.
    Löffler F, Faber J, Bentivegna E, Bode T, Diener P, Haas R, Hinder I, Mundim BC, Ott CD, Schnetter E, Allen G, Campanelli M, Laguna P (2012) The Einstein Toolkit: a community computational infrastructure for relativistic astrophysics. Class Quantum Gravity 29(11):115001.  https://doi.org/10.1088/0264-9381/29/11/115001 ADSCrossRefzbMATHGoogle Scholar
  31. 31.
    Ansorg M, Brügmann B, Tichy W (2004) A single-domain spectral method for black hole puncture data. Phys Rev D 70:064011.  https://doi.org/10.1103/PhysRevD.70.064011 ADSCrossRefGoogle Scholar
  32. 32.
    Diener P, Dorband EN, Schnetter E, Tiglio M (2007) New, efficient, and accurate high order derivative and dissipation operators satisfying summation by parts, and applications in three-dimensional multi-block evolutions. J Sci Comput 32:109.  https://doi.org/10.1007/s10915-006-9123-7 MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Schnetter E, Hawley SH, Hawke I (2004) Evolutions in 3-D numerical relativity using fixed mesh refinement. Class Quantum Gravity 21:1465.  https://doi.org/10.1088/0264-9381/21/6/014 ADSMathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Thornburg J (2004) A fast apparent-horizon finder for 3-dimensional cartesian grids in numerical relativity. Class Quantum Gravity 21:743.  https://doi.org/10.1088/0264-9381/21/2/026 ADSMathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Blue Waters (2018). Blue Waters, Sustained petascale computing. https://bluewaters.ncsa.illinois.edu/blue-waters
  36. 36.
    Kramer W, Butler M, Bauer G, Chadalavada K, Mendes C (2015) Blue waters parallel I/O storage sub-system. In: Prabhat, Q Koziol (ed) High performance parallel I/O. CRC Publications, Taylor and Francis Group, Routledge, Boca Raton, FLGoogle Scholar
  37. 37.
    Jones MD, White JP, Innus M, DeLeon RL, Simakov N, Palmer JT, Gallo SM, Furlani TR, Showerman M, Brunner R, Kot A, Bauer G, Bode B, Enos J, Kramer W (2017) Workload analysis of blue waters. arXiv:1703.00924v1
  38. 38.
    Etienne ZB, Paschalidis V, Haas R, Mösta P, Shapiro SL (2015) IllinoisGRMHD: an open-source, user-friendly GRMHD code for dynamical spacetimes. Class Quantum Gravity 32(17):175009.  https://doi.org/10.1088/0264-9381/32/17/175009
  39. 39.
    Haas R, Ott CD, Szilagyi B, Kaplan JD, Lippuner J, Scheel MA, Barkett K, Muhlberger CD, Dietrich T, Duez MD, Foucart F, Pfeiffer HP, Kidder LE, Teukolsky SA (2016) Simulations of inspiraling and merging double neutron stars using the spectral Einstein code. Phys Rev D 93(12):124062.  https://doi.org/10.1103/PhysRevD.93.124062 ADSCrossRefGoogle Scholar
  40. 40.
    Mösta P, Mundim BC, Faber JA, Haas R, Noble SC, Bode T, Löffler F, Ott CD, Reisswig C, Schnetter E (2014) GRHydro: a new open-source general-relativistic magnetohydrodynamics code for the Einstein toolkit. Class Quantum Gravity 31(1):015005.  https://doi.org/10.1088/0264-9381/31/1/015005 ADSCrossRefzbMATHGoogle Scholar
  41. 41.
    Kidder LE, Field SE, Foucart F, Schnetter E, Teukolsky SA, Bohn A, Deppe N, Diener P, Hébert F, Lippuner J, Miller J, Ott CD, Scheel MA, Vincent T (2017) SpECTRE: a task-based discontinuous Galerkin code for relativistic astrophysics. J Comput Phys 335:84.  https://doi.org/10.1016/j.jcp.2016.12.059 ADSMathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Mroué AH, Scheel MA, Szilágyi B, Pfeiffer HP, Boyle M, Hemberger DA, Kidder LE, Lovelace G, Ossokine S, Taylor NW, Zenginoğlu A, Buchman LT, Chu T, Foley E, Giesler M, Owen R, Teukolsky SA (2013) Catalog of 174 binary black hole simulations for gravitational wave astronomy. Phys Rev Lett 111(24):241104.  https://doi.org/10.1103/PhysRevLett.111.241104 ADSCrossRefGoogle Scholar
  43. 43.
    Bohé A, Shao L, Taracchini A, Buonanno A, Babak S, Harry IW, Hinder I, Ossokine S, Pürrer M, Raymond V, Chu T, Fong H, Kumar P, Pfeiffer HP, Boyle M, Hemberger DA, Kidder LE, Lovelace G, Scheel MA, Szilágyi B (2017) Improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors. Phys Rev D 95(4):044028.  https://doi.org/10.1103/PhysRevD.95.044028 ADSCrossRefGoogle Scholar
  44. 44.
    Husa S, Khan S, Hannam M, Pürrer M, Ohme F, Forteza XJ, Bohé A (2016) Frequency-domain gravitational waves from nonprecessing black-hole binaries. I. New numerical waveforms and anatomy of the signal. Phys Rev D 93(4):044006.  https://doi.org/10.1103/PhysRevD.93.044006
  45. 45.
    Khan S, Husa S, Hannam M, Ohme F, Pürrer M, Forteza XJ, Bohé A (2016) Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era. Phys Rev D 93(4):044007.  https://doi.org/10.1103/PhysRevD.93.044007
  46. 46.
    Taracchini A, Buonanno A, Pan Y, Hinderer T, Boyle M, Hemberger DA, Kidder LE, Lovelace G, Mroué AH, Pfeiffer HP, Scheel MA, Szilágyi B, Taylor NW, Zenginoglu A (2014) Effective-one-body model for black-hole binaries with generic mass ratios and spins. Phys Rev D 89(6):061502.  https://doi.org/10.1103/PhysRevD.89.061502 ADSCrossRefGoogle Scholar
  47. 47.
    Veitch J, Raymond V, Farr B, Farr W, Graff P, Vitale S, Aylott B, Blackburn K, Christensen N, Coughlin M, Del Pozzo W, Feroz F, Gair J, Haster CJ, Kalogera V, Littenberg T, Mandel I, Pitkin O’Shaughnessy R, M, Rodriguez C, Röver C., Sidery T, Smith R, Van Der Sluys M, Vecchio A, Vousden W, Wade L, (2015) Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library. Phys Rev D 91(4):042003.  https://doi.org/10.1103/PhysRevD.91.042003
  48. 48.
    Usman SA, Nitz AH, Harry IW, Biwer CM, Brown DA, Cabero M, Capano CD, Dal Canton T, Dent T, Fairhurst S, Kehl MS, Keppel D, Krishnan B, Lenon A, Lundgren A, Nielsen AB, Pekowsky LP, Pfeiffer HP, Saulson PR, West M, Willis JL (2016) The PyCBC search for gravitational waves from compact binary coalescence. Class Quantum Gravity 33(21):215004.  https://doi.org/10.1088/0264-9381/33/21/215004 ADSCrossRefGoogle Scholar
  49. 49.
    Cannon K, Cariou R, Chapman A, Crispin-Ortuzar M, Fotopoulos N, Frei M, Hanna C, Kara E, Keppel D, Liao L, Privitera S, Searle A, Singer L, Weinstein A (2012) Toward early-warning detection of gravitational waves from compact binary coalescence. Astrophys J 748:136.  https://doi.org/10.1088/0004-637X/748/2/136 ADSCrossRefGoogle Scholar
  50. 50.
    Englert F, Brout R (1964) Broken symmetry and the mass of gauge vector mesons. Phys Rev Lett 13:321.  https://doi.org/10.1103/PhysRevLett.13.321 ADSMathSciNetCrossRefGoogle Scholar
  51. 51.
    Higgs P (1964) Broken symmetries, massless particles and gauge fields. Phys Lett 12(2):132.  https://doi.org/10.1016/0031-9163(64)91136-9 ADSCrossRefGoogle Scholar
  52. 52.
    Higgs PW (1964) Broken symmetries and the masses of gauge bosons. Phys Rev Lett 13:508.  https://doi.org/10.1103/PhysRevLett.13.508 ADSMathSciNetCrossRefGoogle Scholar
  53. 53.
    Guralnik GS, Hagen CR, Kibble TWB (1964) Global conservation laws and massless particles. Phys Rev Lett 13:585.  https://doi.org/10.1103/PhysRevLett.13.585 ADSCrossRefGoogle Scholar
  54. 54.
    Higgs PW (1966) Spontaneous symmetry breakdown without massless bosons. Phys Rev 145:1156.  https://doi.org/10.1103/PhysRev.145.1156 ADSMathSciNetCrossRefGoogle Scholar
  55. 55.
    Kibble TWB (1967) Symmetry breaking in non-abelian gauge theories. Phys Rev 155:1554.  https://doi.org/10.1103/PhysRev.155.1554 ADSCrossRefGoogle Scholar
  56. 56.
    Einstein A (1952) Does the inertia of a body depend upon its energy-content? The principle of relativity. Dover Books on Physics, pp 67–71Google Scholar
  57. 57.
    LHC Higgs Cross Section Working Group, S. Dittmaier et al (2011) Handbook of LHC Higgs cross sections: 1. Inclusive observables. arXiv:1101.0593 [hep-ph]
  58. 58.
    LHC Higgs Cross Section Working Group, Dittmaier S et al (2012) Handbook of LHC Higgs cross sections: 2. Differential distributions. arXiv:1201.3084 [hep-ph]
  59. 59.
    The LHC Higgs Cross Section Working Group, Heinemeyer S et al (2013) Handbook of LHC Higgs cross sections: 3. Higgs properties. arXiv: 1307.1347 [hep-ph]
  60. 60.
    de Florian D, Grojean C, Maltoni F, Mariotti C, Nikitenko A, Pieri M, Savard P, Schumacher M, Tanaka R, Aggleton R et al (2016) Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector. arXiv: 1610.07922 [hep-ph]
  61. 61.
    Aad G et al (2012) Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC ATLAS Collaboration. Phys Lett B 716:1.  https://doi.org/10.1016/j.physletb.2012.08.020 ADSCrossRefGoogle Scholar
  62. 62.
    Chatrchyan S et al (2012) Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC CMS Collaboration. Phys Lett B 716:30.  https://doi.org/10.1016/j.physletb.2012.08.021 ADSCrossRefGoogle Scholar
  63. 63.
    Ahmad QR et al (2001) Measurement of the rate of \(\nu _e+d \rightarrow p+p+e^-\) interactions produced by \(^8B\) solar neutrinos at the Sudbury Neutrino Observatory. Phys Rev Lett 87:071301.  https://doi.org/10.1103/PhysRevLett.87.071301 ADSCrossRefGoogle Scholar
  64. 64.
    Fukuda Y et al (1998) Evidence for oscillation of atmospheric neutrinos. Phys Rev Lett 81:1562.  https://doi.org/10.1103/PhysRevLett.81.1562 ADSCrossRefGoogle Scholar
  65. 65.
    Collaboration ATLAS (2008) The ATLAS experiment at the CERN Large Hadron Collider. JINST 3:S08003.  https://doi.org/10.1088/1748-0221/3/08/S08003
  66. 66.
    Jenos J (2016) Understanding blue waters topology and the topology aware scheduler. In: 2016 blue waters symposium. https://bluewaters.ncsa.illinois.edu/liferay-content/document-library/201620Symposium/enos_topology.pdf
  67. 67.
    Weitzel D, Bockelman B, Brown DA, Couvares P, Würthwein F, Fajardo Hernandez E (2017) Data access for LIGO on the OSG. arXiv:1705.06202
  68. 68.
    Huerta EA, Haas R, Fajardo E, Katz DS, Anderson S, Couvares P, Willis J, Bouvet T, Enos J, Kramer WTC, Leong HW, Wheeler D (2017) BOSS-LDG: a novel computational framework that brings together blue waters, Open Science Grid, shifter and the LIGO data grid to accelerate gravitational wave discovery. In: IEEE 13th international conference on e-science (e-Science).  https://doi.org/10.1109/eScience.2017.47
  69. 69.
    Belkin M, Haas R, Arnold GW, Leong HW, Huerta EA, Lesny D, Neubauer M (2018) Container solutions for HPC systems: a case study of using shifter on blue waters. arXiv:1808.00556
  70. 70.
    Sfiligoi I, Bradley DC, Holzman B, Mhashilkar P, Padhi S, Wurthwein F (2009) The pilot way to grid resources using glide. In: WMS in computer science and information engineering, 2009 WRI world congress on IEEE, vol 2, pp 428–432Google Scholar
  71. 71.
    Zhang W (2008) Abstract Linux virtual server for scalable network services. http://www.linuxvirtualserver.org/software/ktcpvs/ktcpvs.html
  72. 72.
    Deelman E, Singh G, Su MH, Blythe J, Gil Y, Kesselman C et al (2005) Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci Program 13(3):219Google Scholar
  73. 73.
  74. 74.
    James Clark (2018) LIGO-Rucio. https://github.com/astroclark/ligo-rucio
  75. 75.
    Abbott BP, Abbott R, Abbott TD, Acernese F, Ackley K, Adams C, Adams T, Addesso P, Adhikari RX, Adya VB et al (2017) Gravitational waves and gamma-rays from a binary neutron star merger: GW170817 and GRB 170817A. Astrophys J Lett 848:L13.  https://doi.org/10.3847/2041-8213/aa920c ADSCrossRefGoogle Scholar
  76. 76.
    PyCBC (2018) Free and open software to study gravitational waves. https://pycbc.org/
  77. 77.
  78. 78.
    Thapa S, Gardner RW, Herner K, Hufnagel D, Lesny D, Rynge M (2018) Homogenizing OSG and XSEDE: providing access to XSEDE allocations through OSG infrastructure. In: Proceedings of the practice and experience on advanced research computing, PEARC ’18. ACM, New York, pp 15:1–15:7.  https://doi.org/10.1145/3219104.3219157
  79. 79.
    Fajardo E (2018) OSG on blue waters, comet and jetstream. https://indico.fnal.gov/event/12973/session/25/contribution/9/material/slides/0.pdf
  80. 80.
  81. 81.
    Novotny J, Tuecke S, Welch V (2001) An online credential repository for the grid: MyProxy. In: Proceedings 10th IEEE international symposium on high performance distributed computing, pp 104–111.  https://doi.org/10.1109/HPDC.2001.945181
  82. 82.
    Maeno T, De K, Wenaus T, Nilsson P, Stewart GA, Walker R, Stradling A, Caballero J, Potekhin M, Smith D (2011) Overview of ATLAS PanDA workload management. J. Phys Conf Ser 331:072024.  https://doi.org/10.1088/1742-6596/331/7/072024 CrossRefGoogle Scholar
  83. 83.
    ATLAS (2018) ATLAS CONNECT virtual cluster service. https://connect.usatlas.org/
  84. 84.
    Jayatilaka B, Levshina T, Rynge M, Sehgal C, Slyz M (2016) The OSG open facility: a sharing ecosystem. J Phys Conf Ser 664:03.  https://doi.org/10.1088/1742-6596/664/3/032016 CrossRefGoogle Scholar
  85. 85.
    Oleynik D, Panitkin S, Turilli M, Angius A, Oral S, De K, Klimentov A, Wells JC, Jha S (2017) High-throughput computing on high-performance platforms: a case study. In: IEEE 13th International Conference on e-Science (e-Science), pp 295–304.  https://doi.org/10.1109/eScience.2017.43
  86. 86.
    Katz DS (2017) AIMES Final Technical Report. https://www.osti.gov/servlets/purl/1341733
  87. 87.
    CERN (2017) The PanDA production and distributed analysis system. https://twiki.cern.ch/twiki/bin/view/PanDA/PanDA
  88. 88.
    Maeno T, De K, Klimentov A, Nilsson P, Oleynik D, Panitkin S, Petrosyan A, Schovancova J, Vaniachine A, Wenaus T, Yu D (2014) Evolution of the ATLAS PanDA workload management system for exascale computational science. J Phys Conf Ser 513:032062.  https://doi.org/10.1088/1742-6596/513/3/032062 CrossRefGoogle Scholar
  89. 89.
    Science Collaboration LSST, Abell PA, Allison J, Anderson SF, Andrew JR, Angel JRP, Armus L, Arnett D, Asztalos SJ, Axelrod TS et al (2009) LSST Science Book. Version 2. arXiv: 0912.0201
  90. 90.
    Collaboration Dark Energy Survey et al (2016) The Dark Energy Survey: more than dark energy—an overview. MNRAS 460:1270.  https://doi.org/10.1093/mnras/stw641 ADSCrossRefGoogle Scholar
  91. 91.
    Mohr JJ, Armstrong R, Bertin E, Daues G, Desai S, Gower M, Gruendl R, Hanlon W, Kuropatkin N, Lin H, Marriner J, Petravic D, Sevilla I, Swanson M, Tomashek T, Tucker D, Yanny B (2012) The dark energy survey data processing and calibration system. In: Software and cyberinfrastructure for astronomy II. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol 8451, p 84510D.  https://doi.org/10.1117/12.926785
  92. 92.
    NCSA (2018) Illinois Campus Cluster Program. https://campuscluster.illinois.edu
  93. 93.
    Sunayama T, Padmanabhan N, Heitmann K, Habib S, Rangel E (2016) Efficient construction of mock catalogs for baryon acoustic oscillation surveys. J Cosmol Astropart Phys 2016:051.  https://doi.org/10.1088/1475-7516/2016/05/051 CrossRefGoogle Scholar
  94. 94.
    Li N, Gladders MD, Rangel EM, Florian MK, Bleem LE, Heitmann K, Habib S, Fasel P (2016) PICS: simulations of strong gravitational lensing in galaxy clusters. Astrophys J 828:54.  https://doi.org/10.3847/0004-637X/828/1/54 ADSCrossRefGoogle Scholar
  95. 95.
    DESI Collaboration (2016) The DESI experiment part I: science, targeting, and survey design. arXiv:1611.00036
  96. 96.
    Lawrence E, Heitmann K, Kwan J, Upadhye A, Bingham D, Habib S, Higdon D, Pope A, Finkel H, Frontiere N (2017) The Mira-Titan universe. II. Matter power spectrum emulation. Astrophys J 847:50  https://doi.org/10.3847/1538-4357/aa86a9
  97. 97.
    Emberson JD, Frontiere N, Habib S, Heitmann K, Larsen P, Finkel H, Pope A (2018) The borg cube simulation: cosmological hydrodynamics with CRK-SPH. arXiv:1811.03593
  98. 98.
    Frontera (2018) Next-generation Supercomputer at TACC. https://nsf.gov/news/news_summ.jsp?cntn_id=296431
  99. 99.
    Towns J, Cockerill T, Dahan M, Foster I, Gaither K, Grimshaw A, Hazlewood V, Lathrop S, Lifka D, Peterson GD, Roskies R, Scott JR, Wilkins-Diehr N (2014) XSEDE: accelerating scientific discovery computing. Sci Eng 16(5):62.  https://doi.org/10.1109/MCSE.2014.80 CrossRefGoogle Scholar
  100. 100.
    N. Wilkins-Diehr, S. Sanielevici, J. Alameda, J. Cazes, L. Crosby, M. Pierce, R. Roskies (2015) An overview of the XSEDE extended collaborative support program. In: High performance computer applications—6th international conference, ISUM. Revised Selected Papers, Communications in Computer and Information Science, vol 595. Springer, Berlin, pp 3–13.  https://doi.org/10.1007/978-3-319-32243-8_1
  101. 101.
    George D, Huerta EA (2018) Deep neural networks to enable real-time multimessenger astrophysics. Phys Rev D 97:044039.  https://doi.org/10.1103/PhysRevD.97.044039 ADSCrossRefGoogle Scholar
  102. 102.
    George D, Huerta EA (2018) Deep neural networks to enable real-time multimessenger astrophysics. Phys Lett B 778:64.  https://doi.org/10.1016/j.physletb.2017.12.053 ADSCrossRefGoogle Scholar
  103. 103.
    Rebei A, Huerta EA, Wang S, Habib S, Haas R, Johnson D, George D (2018) Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers. arXiv:1807.09787
  104. 104.
    Shen H, George D, Huerta EA, Zhao Z (2017) Denoising gravitational waves using deep learning with recurrent denoising autoencoders. arXiv: 1711.09919
  105. 105.
    George D, Shen H, Huerta EA (2018) Classification and unsupervised clustering of LIGO data with deep transfer learning. Phys Rev D 97:101501.  https://doi.org/10.1103/PhysRevD.97.101501 ADSCrossRefGoogle Scholar
  106. 106.
    Huerta EA, George D, Zhao Z, Allen G (2018) Real-time regression analysis with deep convolutional neural networks. arXiv:1805.02716
  107. 107.
    Guest D, Cranmer K, Whiteson D (2018) Deep learning and its application to LHC physics. Annu Rev Nucl Part Sci 68:161.  https://doi.org/10.1146/annurev-nucl-101917-021019 ADSCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • E. A. Huerta
    • 1
    Email author
  • Roland Haas
    • 2
  • Shantenu Jha
    • 3
  • Mark Neubauer
    • 4
  • Daniel S. Katz
    • 5
  1. 1.National Center for Supercomputing Applications & Department of AstronomyUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.Brookhaven National Laboratory and RutgersThe State University of New JerseyPiscatawayUSA
  4. 4.Department of Physics, National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  5. 5.National Center for Supercomputing Applications & Department of Computer Science, & Department of Electrical and Computer Engineering & School of Information SciencesUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations