Login

HSF WLCG Virtual Workshop

19โ€“24 Nov 2020
Europe/Zurich timezone

Contact

Session

Software

Software session
19 Nov 2020, 16:00

Conveners

Software: Introduction

  • Michel Jouvin (Universitรฉ Paris-Saclay (FR))
  • Graeme A Stewart (CERN)

Software: Other Communities

  • Graeme A Stewart (CERN)
  • Michel Jouvin (Universitรฉ Paris-Saclay (FR))

Software: R&D Activities

  • Michel Jouvin (Universitรฉ Paris-Saclay (FR))
  • Graeme A Stewart (CERN)

Software: R&D Activities

  • Teng Jian Khoo (Humboldt University of Berlin (DE))
  • David Lange (Princeton University (US))

Software: Training

  • David Lange (Princeton University (US))
  • Teng Jian Khoo (Humboldt University of Berlin (DE))

Software: Event Generation

  • Efe Yazgan (National Taiwan University (TW))
  • Josh McFayden (University of Sussex)
  • Andrea Valassi (CERN)

Software: Detector Simulation I

  • Jonathan Madsen
  • Philippe Canal (Fermi National Accelerator Lab. (US))
  • Gloria Corti (CERN)
  • Witold Pokorski (CERN)

Software: Detector Simulation II

  • Gloria Corti (CERN)
  • Philippe Canal (Fermi National Accelerator Lab. (US))
  • Witold Pokorski (CERN)
  • Jonathan Madsen

Software: Diverse R&D (Coffee break is internal - see detailed view)

  • David Lange (Princeton University (US))
  • Teng Jian Khoo (Humboldt University of Berlin (DE))
  • Michel Jouvin (Universitรฉ Paris-Saclay (FR))
  • Graeme A Stewart (CERN)

Presentation materials

There are no materials yet.

  1. 108. Workshop Introduction
    Michel Jouvin (Universitรฉ Paris-Saclay (FR))
    19/11/2020, 16:00
  2. 33. Future Trends in Nuclear Physics SW and Computing
    Dr Markus Diefenthaler (Jefferson Lab)
    19/11/2020, 16:10

    Report from the recent workshop

    Go to contribution page
  3. 34. PyHEP
    Eduardo Rodrigues (University of Liverpool (GB))
    19/11/2020, 16:40
  4. 35. CERN R&D on Spack and the Turnkey Stack
    Valentin Volkl (University of Innsbruck (AT))
    19/11/2020, 17:30
  5. 37. Training: Activities
    Samuel Ross Meehan (CERN)
    19/11/2020, 18:00
  6. 38. Training: Community Building
    Kilian Lieret, Kilian Lieret (Ludwig Maximilian University Munich)
    19/11/2020, 18:25
  7. 55. Introduction
    Josh McFayden (University of Sussex)
    20/11/2020, 16:00
  8. 53. MC@NLO-Delta
    Stefano Frixione (CERN)
    20/11/2020, 16:10
  9. 54. Neural Resampler
    Ben Nachman (Lawrence Berkeley National Lab. (US))
    20/11/2020, 16:40
  10. 68. Progress on porting MadGraph5_aMC@NLO to GPUs
    Stefan Roiser (CERN)
    20/11/2020, 17:20
    Generator Session Talk
  11. 56. PDF/Vegas-Flow
    Juan M. Cruz Martรญnez (University of Milan)
    20/11/2020, 17:50
  12. 42. Geant4
    Marc Verderi (Centre National de la Recherche Scientifique (FR))
    23/11/2020, 16:00
  13. 70. Discussion
    23/11/2020, 16:20
  14. 43. Requirements from HEP experiments
    Marilena Bandieramonte (University of Pittsburgh (US))
    23/11/2020, 16:30
  15. 71. Discussion
    23/11/2020, 16:50
  16. 14. Machine Learning for Detector Simulation
    Kevin Pedro (Fermi National Accelerator Lab. (US))
    23/11/2020, 17:00
    Simulation Sesssion Talk

    Full detector simulations using Geant4 are highly accurate but computationally intensive, while existing fast simulation techniques may not provide sufficient accuracy for all purposes. Machine learning offers potential paths to achieve both high speed and high accuracy. This may be especially important to address the computational challenges posed by the HL-LHC. Ongoing efforts from both...

    Go to contribution page
  17. 73. Discussion
    23/11/2020, 17:20
  18. 39. AdePT
    Andrei Gheata (CERN)
    23/11/2020, 17:50
  19. 74. Discussion
    23/11/2020, 18:05
  20. 40. Celeritas
    Dr Seth Johnson (Oak Ridge National Laboratory)
    23/11/2020, 18:10
  21. 76. Discussion
    23/11/2020, 18:25
  22. 41. Using Geant4 and Opticks to simulate liquid Aron TPC's
    Hans-Joachim Wenzel (Fermi National Accelerator Lab. (US))
    23/11/2020, 18:30
  23. 78. Discussion
    23/11/2020, 18:45
  24. 44. General Discussion
    23/11/2020, 18:50
  25. 109. Introduction
    David Lange (Princeton University (US)), Graeme A Stewart (CERN), Michel Jouvin (Universitรฉ Paris-Saclay (FR)), Teng Jian Khoo (Humboldt University of Berlin (DE))
    24/11/2020, 16:00
  26. 6. Phoenix Event Display
    Edward Moyse (University of Massachusetts (US))
    24/11/2020, 16:05
    Short Talk

    Visualising HEP experiment data is vital for physicists trying to debug their reconstruction software, to examine detector geometry or to understand physics analyses, and also for outreach and publicity purposes. Traditionally experiments used in-house applications which required installation (often as part of a much larger experiment specific framework). In recent years, web-based...

    Go to contribution page
  27. 57. Discussion
    24/11/2020, 16:15
  28. 18. High-throughput data analysis with modern ROOT interfaces
    Dr Enrico Guiraud (EP-SFT, CERN), Mr Vincenzo Eduardo Padulano (Valencia Polytechnic University (ES)), Mr Stefan Wunsch (KIT - Karlsruhe Institute of Technology (DE))
    24/11/2020, 16:22
    Short Talk

    With the upcoming start of LHC Run III and beyond, HEP data analysis is facing a large increase in average input dataset sizes. At the same time, balancing analysis software complexity with the need to extract as much performance as possible from the latest HPC hardware is still often difficult.
    Recent developments in ROOT significantly lower the energy barrier for the development of...

    Go to contribution page
  29. 62. Discussion
    24/11/2020, 16:32
  30. 10. bamboo: easy and efficient analysis with python and RDataFrame
    Pieter David (Universite Catholique de Louvain (UCL) (BE))
    24/11/2020, 16:39
    Short Talk

    The bamboo analysis framework [1] allows to write simple declarative analysis code (it effectively implements a domain-specific language embedded in python), and runs it efficiently using RDataFrame (RDF) - or viewed differently: it introduces a set of tools to efficiently generate large RDF computation graphs from a minimal amount of user code (in python), e.g. a simple way to specify...

    Go to contribution page
  31. 58. Discussion
    24/11/2020, 16:49
  32. 2. Analysis Description Language for LHC-type analyses
    Gokhan Unel (University of California Irvine (US)), Sezen Sekmen (Kyungpook National University (KR)), Harry Prosper (Florida State University (US))
    24/11/2020, 16:56
    Lightning Talk

    Physicists aiming to perform an LHC-type analysis today are facing a number of challenges: intense computing knowledge is needed at programming level to implement the relevant algorithm, and at system level to interact with the ever evolving sets of analysis frameworks for interfacing with the analysis object information. Moreover, the ambiguity concerning the configuration of the overall...

    Go to contribution page
  33. 59. Discussion
    24/11/2020, 17:01
  34. 9. podio - latest developments and new features of a flexible EDM toolkit
    Thomas Madlener (Deutsches Elektronen-Synchrotron (DESY))
    24/11/2020, 17:06
    Lightning Talk

    Creating efficient event data models (EDMs) for high energy physics (HEP) experiments is a non-trivial task. Past approaches, employing virtual inheritance and possibly featuring deep object-hierarchies, have shown to exhibit severe performance limitations. Additonally, the advent of multi-threading and heterogenous computing poses further constraints on how to efficiently implement EDMs and...

    Go to contribution page
  35. 60. Discussion
    24/11/2020, 17:11
  36. 7. Use of auto-differentiation within the ACTS tookit
    Mr Huth Benjamin (University of Regensburg)
    24/11/2020, 17:35
    Short Talk

    The use of first and higher order differentiation is essential for many parts of track reconstruction: either as part of the transport of track parameters through the detector, in several linearization applications, and for establishing the detector alignment. While in general those derivations are well known, they can be complex to derive and even more difficult to be validated. The latter is...

    Go to contribution page
  37. 61. Discussion
    24/11/2020, 17:45
  38. 21. Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures
    Sophie Berkman (Fermi National Accelerator Laboratory)
    24/11/2020, 17:52
    Lightning Talk

    Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming more challenging as the detectors increase in size to reach their physics goals. Liquid argon time projection chamber (TPC) neutrino experiments are planned to grow by 100 times in the next decade relative to...

    Go to contribution page
  39. 63. Discussion
    24/11/2020, 17:57
  40. 11. GPU-accelerated machine learning inference for offline reconstruction and analysis workflows in neutrino experiments
    Tingjun Yang (Fermi National Accelerator Lab. (US))
    24/11/2020, 18:02
    Short Talk

    Future neutrino experiments like DUNE represent big-data experiments that will acquire petabytes of data per year. Processing this amount of data itself is a significant challenge. In recent years, however, the use of deep learning applications in the reconstruction and analysis of data acquired by LArTPC-based experiments has grown substantially. This will impose an even bigger amount of...

    Go to contribution page
  41. 64. Discussion
    24/11/2020, 18:12
  42. 13. GPU-based tracking with Acts
    Xiaocong Ai (DESY)
    24/11/2020, 18:19
    Short Talk

    At future hadron colliders such as the High-Luminosity LHC(HL-LHC), tens of thousands of particles can be produced in a single event, which results in a very challenging tracking environment. The estimated CPU resources required by the event processing at the HL-LHC could well exceed the available resources. To mitigate this problem, modern tracking software tends to gain performance by taking...

    Go to contribution page
  43. 65. Discussion
    24/11/2020, 18:29
  44. 1. Investigating Portable Heterogeneous Solutions with Fast Calorimeter Simulation
    Vincent Pascuzzi (Lawrence Berkeley National Lab. (US)), Dr Charles Leggett (Lawrence Berkeley National Lab (US))
    24/11/2020, 18:36
    Short Talk

    Physicists at the Large Hadron Collider (LHC), near Geneva,
    Switzerland, are preparing their experiments for the high
    luminosity (HL) era of proton-proton collision data-taking. In
    addition to detector hardware research and development for
    upgrades necessary to cope with the more than two-fold increase
    in instantaneous luminosity, physicists are investigating
    potential heterogeneous...

    Go to contribution page
  45. 66. Discussion
    24/11/2020, 18:46
  46. 67. Closing Remarks
    David Lange (Princeton University (US)), Graeme A Stewart (CERN), Michel Jouvin (Universitรฉ Paris-Saclay (FR)), Teng Jian Khoo (Humboldt University of Berlin (DE))
    24/11/2020, 18:53
  47. 80. Discussion
  48. 79. Discussion
  49. 77. Discussion
  50. 75. Discussion
  51. 72. Discussion
  52. 69. Discussion
  53. 81. introduction
  54. 36. Training Activities in HSF
  55. 3. VegasFlow: accelerating Monte Carlo simulation across multiple devices
    Dr Juan M. Cruz Martรญnez (University of Milan)
    Generator Session Talk

    We present VegasFlow, a new software for fast evaluation of high dimensional integrals based on Monte Carlo integration using Google's TensorFlow library.
    VegasFlow enables developers to delegate all complicated aspects of hardware and platform implementation to the underlying library so they can focus on the problem at hand.
    VegasFlow automatically offloads the integration algorithm and...

    Go to contribution page
Building timetable...