HSF WLCG Virtual Workshop

19 Nov 2020, 16:00 → 24 Nov 2020, 20:00 Europe/Zurich

The second edition of the HSF-WLCG virtual workshop series brings us together again to review progress and discuss plans in the key areas of software and computing for HEP.

This workshop will be run with parallel software and computing tracks.

Please note that workshop sessions will be RECORDED and the video made public afterwards.

HSF sessions are being uploaded to YouTube now and you will also find the recording of each talk attached in Indico.

The document summarising outcomes and follow-ups from the workshop is now available.

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For software we plan sessions on:

• Software plenary covering a wide spectrum of HSF activity
• A focus session on event generation
• A focus session on detector simulation, co-organised with Geant4
• An open session, for software R&D where we invite your contributions

HSF Sessions Notebook

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The computing sessions will focus on storage and its evolution towards the HL-LHC needs. 

WLCG Session Notebook  

Organisers:

• Julia Andreeva, CERN
• Simone Campana, CERN
• Philippe Canal, Fermilab
• Ian Collier, STFC
• Gloria Corti, CERN
• Jose Flix Molina, CIEMAT/PIC
• Alessandra Forti, University of Manchester
• Michel Jouvin, IJCLab
• Teng Jian Khoo, Humbold University / University of Innsbruck
• David Lange, Princeton University
• Jonathan Madsen, LBNL
• Josh McFayden, University of Sussex
• Helge Meinhard CERN
• Maarten Litmaath, CERN
• Witek Pokorski, CERN
• Oxana Smirnova, Lund University
• Graeme A Stewart, CERN
• Andrea Valassi, CERN
• Mattias Wadestein, Umea
• Efe Yazgan, National Taiwan University

HSF Session Notebook

HSF Sessions on YouTube

HSF Workshop Follow-Ups.pdf

Post-workshop Survey

WLCG session notebook

Workshop Photos

• 2020.11 HSF workshop - group photo.jpg
• 2020.11 WLCG-HSF workshop - group photo.jpg
• 2020.11 WLCG workshop - group photo.jpg

Thursday 19 November

Computing: Development in the experiments and implications on storage evolution

1 Introduction

Speaker: Simone Campana (CERN)

Recording

WLCG-Workshop-Intro-Nov-2020.pdf

2 ALICE

Speaker: Latchezar Betev (CERN)

Recording

WLCG_workshop_Nov_2020_ALICE.pdf

3 ATLAS

Speakers: Alessandro Di Girolamo (CERN), Alessandro Di Girolamo (CERN)

ATLAS WLCG HSF 20201124(1).pdf

Recording

4 CMS

Speakers: Danilo Piparo (CERN), James Letts (Univ. of California San Diego (US))

Recording

WLCG_Workshop_201119.pdf

17:15 Coffee and cake

5 LHCb

Speaker: Christophe Haen (CERN)

lhcb_pov.pdf

Recording

6 Belle II

Speaker: Silvio Pardi (Universita e sezione INFN di Napoli (IT))

2020-11-19 Belle-II- WLCG_HSF-v1.0.pdf

Recording

7 DUNE

Speaker: Steven Timm (Fermi National Accelerator Lab. (US))

Recording

WLCG_Workshop_2020.pptx

18:30 Coffee and cake

8 JUNO and other experiments at IHEP

Speaker: Lu Wang (Computing Center,Institute of High Energy Physics, CAS)

Data Storage of JUNO and other IHEP experiments.pdf

Data Storage of JUNO and other IHEP experiments.pptx

Recording

9 Discussion

Audio recording [MP3]

Software: Introduction

Conveners: Graeme A Stewart (CERN), Michel Jouvin (Université Paris-Saclay (FR))

10 Workshop Introduction

Speaker: Michel Jouvin (Université Paris-Saclay (FR))

00_Introduction.mp4

HSF November Virtual Workshop Introduction.pdf

YouTube Recording

Software: Other Communities

Conveners: Graeme A Stewart (CERN), Michel Jouvin (Université Paris-Saclay (FR))

11 Future Trends in Nuclear Physics SW and Computing

Report from the recent workshop

Speaker: Dr Markus Diefenthaler (Jefferson Lab)

01_Nuclear_Physics.mp4

TRENDS2020-Report.pdf

YouTube Recording

16:30 Discussion

Software: R&D Activities

Conveners: Graeme A Stewart (CERN), Michel Jouvin (Université Paris-Saclay (FR))

12 PyHEP

Speaker: Eduardo Rodrigues (University of Liverpool (GB))

02_PyHEP.mp4

EduardoRodrigues_2020-11-19_HSF-WLCG-Workshop.pdf

YouTube Recording

17:00 Discussion

17:10 Group Photo (please switch on your camera!)

17:12 Coffee and Cake

Sorry, you have to bring your own... try a jammy dodger!

Software: R&D Activities

Conveners: David Lange (Princeton University (US)), Teng Jian Khoo (Humboldt University of Berlin (DE))

13 CERN R&D on Spack and the Turnkey Stack

Speaker: Valentin Volkl (University of Innsbruck (AT))

03_Spack_Key4hep.mp4

2020-11-19-HSF-Spack.pdf

YouTube Recording

17:50 Discussion

Software: Training

Conveners: David Lange (Princeton University (US)), Teng Jian Khoo (Humboldt University of Berlin (DE))

14 Training: Activities

Speaker: Samuel Ross Meehan (CERN)

04_Training_HSF.mp4

HSFTraining_WLCGWorkshop_191120.pdf

YouTube Recording

18:15 Discussion

15 Training: Community Building

Speakers: Kilian Lieret, Kilian Lieret (Ludwig Maximilian University Munich)

05_Training_Communities.mp4

201119_hsf_wlcg_training_community_building.pdf

YouTube Recording

18:40 Discussion

Friday 20 November

Computing: Site input

16 Storage and data management experience at CNAF

Speakers: Lucia Morganti, Vladimir Sapunenko (INFN-CNAF (IT)), Dr Vladimir Sapunenko (INFN-CNAF)

Recording

Storage and Data management experience at CNAF.pdf

17 RAL : Experience with using Erasure Coding and cost projections vs Replicated Storage

Speaker: Alastair Dewhurst (Science and Technology Facilities Council STFC (GB))

ErasureCoding20201120.pdf

ErasureCoding20201120.pptx

Recording

18 KIT: Storage Setup and Evolution at the GridKa Tier-1 Site

Speakers: Jan Erik Sundermann (Albert-Ludwigs-Universitaet Freiburg (DE)), Jan Erik Sundermann (Karlsruhe Institute of Technology (KIT))

20201120_-_WLCG_Storage_Workshop.pdf

Recording

19 PIC: Storage studies for CMS

Speaker: Carlos Perez Dengra (PIC-CIEMAT)

HSF_WCLG 2020 - Storage and data management experience at PIC.pdf

Reccording

20 UK T2 storage evolution

Speaker: Samuel Cadellin Skipsey

Recording

UK Tier-2 Storage Evolution

“UK Tier-2 Storage Evolution”.pdf

16:15 Coffee and cake

21 FR T2 : Concerns about DPM operations during Run3

Speaker: David Bouvet (IN2P3/CNRS (FR))

Concerns about DPM operations during Run 3.pdf

Recording

22 Production pilot of Swiss ATLAS federated storage with DPM and ARC caches

Speakers: Francesco Giovanni Sciacca (Universitaet Bern (CH)), Gianfranco Sciacca

ATLAS-CH-StorageFederation-20201120-WLCG.pdf

Recording

23 ALPAMED: Measuring data access performances within federated DPM storage

Speaker: Stephane Jezequel (LAPP-Annecy CNRS/USMB (FR))

ALPAMED_HSF_Nov2020.pdf

Recording

24 US CMS T2: Storage experience

Speakers: Frank Wuerthwein (Univ. of California San Diego (US)), Frank Wuerthwein (UCSD)

Recording

wlcg-storage-11292020.pdf

25 US ATLAS T2 : Storage experience

Speaker: Mark Sosebee (University of Texas at Arlington (US))

Recording

WLCG-storage-workshop-US-Tier2s.pdf

26 Storage Evolution at NET2 with NESE

Speaker: Saul Youssef (Boston University (US))

Recording

WLCG.pdf

18:00 Cofee and cake

27 FNAL : Storage experience

Speakers: Bo Jayatilaka (Fermi National Accelerator Lab. (US)), David Alexander Mason (Fermi National Accelerator Lab. (US))

FermilabStorage111920.pdf

Recording

slides

28 BNL : Storage experience

Speaker: Hironori Ito (Brookhaven National Laboratory (US))

BNL Storage HSF 2020 Nov .pdf

Recording

29 TRIUMF : Status and experience with disk,tape

Speaker: Simon Liu (TRIUMF (CA))

Recording

TRIUMF_Storage_Status_Experience_disk_tape.pdf

Software: Event Generation

Conveners: Andrea Valassi (CERN), Efe Yazgan (National Taiwan University (TW)), Josh McFayden (University of Sussex)

30 Introduction

Speaker: Josh McFayden (University of Sussex)

2020-11-20_HSFGen_Intro_mcfayden.pdf

Josh.mp4

YouTube Recording

31 MC@NLO-Delta

Speaker: Stefano Frixione (CERN)

mcdelta.HSF.pdf

Stefano.mp4

YouTube Recording

16:28 Discussion

32 Neural Resampler

Speaker: Ben Nachman (Lawrence Berkeley National Lab. (US))

Ben.mp4

HSFFall2020.pdf

YouTube Recording

16:58 Discussion

17:10 Coffee and Cake

33 Progress on porting MadGraph5_aMC@NLO to GPUs

Speaker: Stefan Roiser (CERN)

20201120-WLCGHSF_WS-MadgraphGPU.pdf

Stefan.mp4

YouTube Recording

17:38 Discussion

34 PDF/Vegas-Flow

Speaker: Juan M. Cruz Martínez (University of Milan)

juancruzmartinez.pdf

Juan.mp4

YouTube Recording

18:08 Discussion

Saturday 21 November

16:00 It's the weekend, take a break!

Sunday 22 November

16:00 It's the weekend, take a break!

Monday 23 November

Computing: Site session 2

35 KISTI : Tapeless archive experience

Speaker: Sang Un Ahn (Korea Institute of Science & Technology Information (KR))

Recording

tapeless_archive_asu.pdf

36 BNL : Experience with tape storage

Speakers: Shigeki Misawa (Brookhaven National Laboratory (US)), Shigeki Misawa (Brookhaven National Laboratory)

Recording

WLCG_TapevsDisk.pdf

37 Discussion

Recording

15:45 Coffe and cake

Computing: Storage technologies

38 dCache roadmap

Speaker: Mr Tigran Mkrtchyan (DESY)

dcache-roadmap.pdf

Recording

39 CTA roadmap

Speaker: Michael Davis (CERN)

CTA_Roadmap.pdf

Recording

40 StoRM roadmap

Speaker: Andrea Ceccanti (Universita e INFN, Bologna (IT))

Recording

StoRM-Evolution-WLCG-Workshop-2020-v2.pdf

41 DPM roadmap

Speakers: Fabrizio Furano (CERN), Petr Vokac (Czech Technical University in Prague (CZ)), Petr Vokac (Czech Technical University)

Recording

StatusDPMDynafed2020_wlcg.pdf

wlcg_hsf_dpm_vokac_20201123.pdf

17:20 Coffee and cake

42 EOS roadmap

Speaker: Andreas Joachim Peters (CERN)

EOS WLCG 2020 Short.pdf

Recording

43 XRootD roadmap

Speakers: Andrew Bohdan Hanushevsky (SLAC National Accelerator Laboratory (US)), Andrew Hanushevsky (Unknown), Andrew Hanushevsky (STANFORD LINEAR ACCELERATOR CENTER)

HSF-WLCG-XRootD-Roadmap.pdf

HSF-WLCG-XRootD-Roadmap.pptx

Recording

44 ECHO roadmap

Speaker: Tom Byrne (STFC)

Echo_roadmap_upload.pdf

Echo_roadmap_upload.pptx

Recording

45 Discussion

Recording

Software: Detector Simulation I

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

46 Geant4

Speaker: Marc Verderi (Centre National de la Recherche Scientifique (FR))

12_Geant4.mp4

Geant4-MarcVerderi.pdf

YouTube Recording

47 Discussion

48 Requirements from HEP experiments

Speaker: Marilena Bandieramonte (University of Pittsburgh (US))

13_LHC_Experiment_Requiremnents.mp4

HSFWorkshop_HEPExperimentsRequirements.pdf

YouTube Recording

49 Discussion

50 Machine Learning for Detector Simulation

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 inside and outside the LHC experimental collaborations will be presented. Challenges and opportunities will also be discussed.

Speaker: Kevin Pedro (Fermi National Accelerator Lab. (US))

14_Machine_Learning_for_Detector_Simulation.mp4

ml4sim hsf nov 23 2020.pdf

YouTube Recording

51 Discussion

17:30 Coffee and Cake

Sorry, you have to get this yourself - why not try a mint tea?

Software: Detector Simulation II

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

52 AdePT

Speaker: Andrei Gheata (CERN)

15_AdePT.mp4

AdePT_HSF_nov2020.pdf

YouTube Recording

53 Discussion

54 Celeritas

Speaker: Dr Seth Johnson (Oak Ridge National Laboratory)

16_Celeritas.mp4

johnson-celeritas.pdf

YouTube Recording

55 Discussion

56 Using Geant4 and Opticks to simulate liquid Aron TPC's

Speaker: Hans-Joachim Wenzel (Fermi National Accelerator Lab. (US))

17_Using_Geant4_and_Opticks_to_simulate_LAr_TPCs.mp4

HSFOpticks.pdf

HSFOpticks.pdf

HSFOpticks.pptx

YouTube Recording

57 Discussion

58 General Discussion

Tuesday 24 November

Computing: Analysis Facilities and implications on storage

59 GSI Analysis Facility

Speakers: Mohammad Al-Turany (CERN), Thorsten Kollegger (GSI - Helmholtzzentrum fur Schwerionenforschung GmbH (DE))

20201124-GSI-AnalysisFacility-v001.pdf

Recording

60 U.S. CMS managed Analysis Facilities

Speaker: Oksana Shadura (University of Nebraska Lincoln (US))

Recording

U.S. CMS Managed Analysis Facilities-1.pdf

61 Discussion

Recording

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

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

62 Introduction

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

Day 4 Introduction (aka the beginning of the end).pdf

63 Phoenix Event Display

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 event/geometry displays have started to appear, which dramatically lower the entry-barrier to use, but typically were still per-experiment.

Phoenix was adopted as part of the HSF visualisation activity: a TypeScript-based event display framework, using the popular three.js library for rendering. It is experiment agnostic by design, with shared common tools (such as custom menus, controls, propagators) and the ability to add experiment specific extensions. It consists of two packages: a plain TypeScript core library (phoenix-event-display) and an Angular application (a React example is also provided in the documentation).The core library can be adapted for any experiment with some simple steps. It has been selected for Google Summer of Code the last two years, and is ATLAS’ officially supported web-event display. This talk will focus on the status, as well as recent developments, such as WebXR prototypes, interface improvements and the Runge-Kutta propagator.

Speaker: Edward Moyse (University of Massachusetts (US))

18_Phoenix_Event_Display.mp4

Keynote with videos

Phoenix WLCGHSF 2020.pdf

YouTube Recording

64 Discussion

65 High-throughput data analysis with modern ROOT interfaces

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 high-throughput data analysis applications. This was achieved through a unique combination of ingredients: a high-level and high-performance analysis framework; just-in-time compilation of C++ code for efficient I/O and usability enhancements; automatic generation of Python bindings; transparent offloading of computations to distributed computation engines such as Spark.
The resulting simplified data analysis model has enabled a whole range of R&D activities that are expected to deliver further acceleration, such as context-aware caching.
This talk will provide an overview of recent developments in ROOT as an engine for high-throughput data analysis and how it is employed in several existing real-world usecases.

Speakers: Dr Enrico Guiraud (EP-SFT, CERN), Mr Vincenzo Eduardo Padulano (Valencia Polytechnic University (ES)), Mr Stefan Wunsch (KIT - Karlsruhe Institute of Technology (DE))

19_High-throughput_data_analysis_with_modern_ROOT_interfaces.mp4

High-throughput analysis with modern ROOT.pdf

YouTube Recording

66 Discussion

67 bamboo: easy and efficient analysis with python and RDataFrame

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 selections and outputs, automatically filling a set of histograms with different systematic variations of some input variables.
It is currently being used for several analyses on the full CMS Run2 dataset, and thus provides an example of a very analysis description language-like approach that is compatible with the practical needs of modern HEP data analysis (different types of corrections, machine learning inference, user-provided extensions, combining many input samples and scaling out to a batch cluster etc.).

[1] https://cp3.irmp.ucl.ac.be/~pdavid/bamboo/

Speaker: Pieter David (Universite Catholique de Louvain (UCL) (BE))

20_bamboo:_easy_and_efficient_analysis_with_python_and_RDataFrame.mp4

bamboo_HSFWLCGWorkshop_20201124.pdf

YouTube Recording

68 Discussion

69 Analysis Description Language for LHC-type analyses

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 computing environment impairs the reproduction of previous results. To overcome at least some of these difficulties, we propose the utilization of an Analysis Description Language (ADL), a domain specific, declarative language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing framework. Such a language decouples the computer intense aspects such as data access from the actual physics algorithm. It would therefore benefit both the experimental and phenomenological communities by facilitating the design, validation, combination, reproduction, interpretation and overall communication of the analysis contents. It would also help to preserve the analyses beyond the lifetimes of experiments or analysis software.
This presentation aims to introduce the ADL concept and summarize the current efforts to make it realistically usable in LHC analyses. In particular, the work that has been ongoing to develop transpiler and interpreter systems adl2tnm and CutLang, to implement various example analyses as well as documentation and validation efforts will be presented.

Speakers: Gokhan Unel (University of California Irvine (US)), Sezen Sekmen (Kyungpook National University (KR)), Harry Prosper (Florida State University (US))

21_Analysis_Description_Language_for_LHC-type_analyses.mp4

ADL-HSF.pdf

YouTube Recording

70 Discussion

71 podio - latest developments and new features of a flexible EDM toolkit

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 the corresponding I/O layer. podio is a c++ toolkit for the creation of EDMs with a fast and efficient I/O layer using plain-old-data (POD) structures wherever possible. Physicist users are provided with a high-level interface of lightweight handle classes. The podio code generator that produces all the necessary c++ code from a high-level description in YAML files has recently been completely reworked to improve maintainability and extensibility. We will briefly discuss the new implementation and present, as a first use case, how it has been used to introduce an additional I/O backend based on SIO, a simple binary I/O library that is also used in LCIO. We will further discuss our first implementation of providing access to metadata, i.e. data that does not fit into the EDM itself. Finally, we will show how all of these capabilities are put to use in EDM4hep, the EDM for the Key4hep project.

Speaker: Thomas Madlener (Deutsches Elektronen-Synchrotron (DESY))

22_podio_-_latest_developments_and_new_features_of_a_flexible_EDM_toolkit.mp4

podio_hsf_wlcg_nov20.pdf

YouTube Recording

72 Discussion

17:16 Coffee and Cake

73 Use of auto-differentiation within the ACTS tookit

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 often done with numerical cross checking using a Ridder's algorithm or similar approaches. The vast development of machine learning application in the last years has also renewed interest in algorithmic differentiation techniques, that uses compiler or runtime techniques to compute exact derivates from function expressions, surpassing the precision achievable via standard numerical differerntiation based on finite differerences.
ACTS is a common track reconstruction toolkit that aims to preserve the tack reconstruction software from the LHC era and at the same time prepares a R&D testbed for further algorithm and technology research. We present the successful inclusion of the auto-diff library into the ACTS propagation and track based alignment modules that serves as a complimentary way to calculate transport jacobians and alignment derivatives: the implementation within the ACTS software is shown, and the validation and CPU time comparison with respect to the implemented analytical or numerically determined expressions are given.

Speaker: Mr Huth Benjamin (University of Regensburg)

23_Use_of_auto-differentiation_within_the_ACTS_tookit.mp4

autodiff_in_acts.pdf

YouTube Recording

74 Discussion

75 Reconstruction for Liquid Argon TPC Neutrino Detectors Using Parallel Architectures

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 currently operating experiments, and modernization of liquid argon TPC reconstruction code, including vectorization and multi-threading, will help to mitigate this challenge. The liquid argon TPC hit finding algorithm used across multiple experiments, through the LArSoft framework, has been vectorized and multi-threaded. This increases the speed of the algorithm up to 200 times within a standalone version on Intel architectures. This new version of the hit finder has been incorporated back into LArSoft so that it can be used by experiments. To fully take advantage of this implemented parallelism, an experiment workflow is being developed to run LArSoft at a high performance computing center. This will be used to produce samples as part of a central processing campaign.

Speaker: Sophie Berkman (Fermi National Accelerator Laboratory)

24_Reconstruction_for_Liquid_Argon_TPC_Neutrino_Detectors_Using_Parallel_Architectures.mp4

sberkman_201124_HSF.pdf

YouTube Recording

76 Discussion

77 GPU-accelerated machine learning inference for offline reconstruction and analysis workflows in neutrino experiments

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 strain on the computing requirements of these experiments since the CPU-based systems used to run offline processing are not well suited to the task of deep learning inference. To address this problem, we adopt an "as a Service" model where the inference task is provided as a web service. We demonstrate the feasibility of this approach by testing it on the full reconstruction chain of ProtoDUNE using fully simulated data, where the GPU-based inference server is hosted on the Google Cloud Platform. We present encouraging results from our tests that include detailed studies of scaling behavior. Based on these results, the "as a Service" approach shows great promise as a solution for the growing computing needs of future neutrino experiments which are associated with deep-learning inference tasks.

Speaker: Tingjun Yang (Fermi National Accelerator Lab. (US))

25_GPU-accelerated_machine_learning_inference_for_offline_reconstruction_and_analysis_workflows_in_neutrino_experiments.mp4

GPUaaS.pdf

GPUaaS.pdf

GPUaaS.pptx

YouTube Recording

78 Discussion

79 GPU-based tracking with Acts

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 advantage of modern computing techniques on hardware such as multi-core CPUs or GPUs with the capability to process many threads in parallel.

The Acts (A Common Tracking Software) project encapsulates the current ATLAS tracking software into an experiment-independent toolkit designed for modern computing architectures. It provides a set of high-level track reconstruction tools agnostic to the details of the detector and magnetic field configuration. Particular emphasis is placed on thread-safety of the code in order to support concurrent event processing with context-dependent detector conditions, such as detector alignments or calibrations. Acts also aims to be a research and development platform for studying innovative tracking techniques and exploiting modern hardware architectures. The multi-threaded event processing on multi-core is supported by using the Intel Thread Building Block (TBB) library. It also provides plugins for heterogeneous computing, such as CUDA and SYCL/oneAPI, and contains example code that could be offloaded to a GPUs, for instance, the Acts seed finder.

In this talk, I will present a summary of the R&D activities to explore parallelism and acceleration of elements of track reconstruction using GPUs, such as the GPU-based seed finding, geometry navigation and Kalman fitting, based on the Acts software. The strategies of GPUs implementation will be shown. Both the achieved performance and the encountered difficulties will be discussed.

Speaker: Xiaocong Ai (DESY)

26_GPU-based_tracking_with_Acts.mp4

ACTS_WLCG_GPU_xiaocong.pdf

YouTube Recording

80 Discussion

81 Investigating Portable Heterogeneous Solutions with Fast Calorimeter Simulation

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 computing solutions to address CPU
limitations that could be detrimental to an otherwise successful
physics program.

At the dawn of supercomputers employing a wide range of
architectures and specifications, it is crucial that experiments'
software be much as possible abstracted away from the underlying
hardware implementation in order to utilize the vast array of
these machines. New developments in application programming
interfaces (APIs) aim to be architecture-independent, providing
the ability to write single-source codes that can be compiled for
virtually any hardware. In this talk, we present the details of
our work on a cross-platform software prototyping with Kokkos, a
single source, performant parallel C++ API that provides hardware
backends for wide range of parallel architectures, including
NVIDIA, AMD, Intel, OpenMP and pThreads, and SYCL, an abstraction
layer whose specification is defined by the Khronos Group and
members from industry-leading entities such as Intel. Using
ATLAS’s new fast calorimeter simulations codes, FastCaloSim, as a
testbed, we evaluate Kokkos and SYCL in terms of its
heterogeneity and its performance with respect to other parallel
computing APIs.

Speakers: Vincent Pascuzzi (Lawrence Berkeley National Lab. (US)), Dr Charles Leggett (Lawrence Berkeley National Lab (US))

2020.11.24_HSF_FCS.pdf

27_Investigating_Portable_Heterogeneous_Solutions_with_Fast_Calorimeter_Simulation.mp4

YouTube Recording

82 Discussion

83 Closing Remarks

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

Computing: HPC/cloud storage integration

84 NERSC experience

Speakers: Wahid Bhimji, Wahid Bhimji (Lawrence Berkeley National Lab. (US))

2020-11 WLCG NERSC Wahid.pdf

Google slides

Recording

85 CMS Experience with CINECA

Speakers: Daniele Spiga (Universita e INFN, Perugia (IT)), Dr Tommaso Boccali (INFN Sezione di Pisa), Tommaso Boccali (INFN Sezione di Pisa, Universita' e Scuola Normale Superiore, P)

CINECA CMS WLCG_HSF Nov 2020 - final.pdf

CINECA CMS WLCG_HSF Nov 2020 - final.pptx

Recording

86 Discussion

17:30 Cofee and cake

Computing: Discussion of the WS summary draft and future activities

87 Discussion of the WS summary draft and future activities

Summary Document

Recording

WLCGstorageWS.pdf