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Dr. Ian BirdCERNLHC Computing Project Leader3rd April 2017
Worldwide Distributed Computing for the LHC
The Large Hadron Collider (LHC)
A new frontier in Energy & Data volumes: LHC experiments generate 50 PB/year
HNSciCloud; 3 April 2017
~700 MB/s
~10 GB/s
>1 GB/s
>1 GB/s
Tier-1: permanent storage, re-processing, analysis
Tier-0 (CERN and Hungary): data recording, reconstruction and distribution
Tier-2: Simulation,end-user analysis
> 2 million jobs/day
~750k CPU cores
600 PB of storage
~170 sites, 42 countries
10-100 Gb links
WLCG:An International collaboration to distribute and analyse LHC data
Integrates computer centres worldwide that provide computing and storage resource into a single infrastructure accessible by all LHC physicists
The Worldwide LHC Computing Grid
HNSciCloud; 3 April 2017
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WLCG MoU Signatures2017:- 63 MoU’s- 167 sites; 42 countries
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Optical Private NetworkSupport T0 – T1 transfers& T1 – T1 trafficManaged by LHC Tier 0 and Tier 1 sites
Networks
Up to 340 Gbps transatlantic
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Asia North America
South America
Europe
LHCOne: Overlay networkAllows NREN’s to manage HEP traffic on general purpose networkManaged by NREN collaboration
Data in 2016
HNSciCloud; 3 April 2017
2016: 49.4 PB LHC data/ 58 PB all experiments/ 73 PB total
ALICE: 7.6 PBATLAS: 17.4 PBCMS: 16.0 PBLHCb: 8.5 PB
11 PB in July
180 PB on tape800 M files
Data distribution
HNSciCloud; 3 April 2017 [email protected] 8
Global transfer rates increased to 30-40 GB/s (>2 x Run1)
Increased performance everywhere:- Data acquisition >10PB / month- Data transfer rates > 35 GB/s globally
Several Tier 1s increased OPN network bandwidth to CERN to manage new data rates;GEANT has deployed additional capacity for LHC
Monthly traffic growth on LHCONE
Regular transfers of >80 PB/month with ~100 PB/month during July-October(many billions of files)
Worldwide computing
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Peak delivery:18M core-days/month(~580k cores permanently)
CERN Facilities today
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2017:• 225k cores 325k• 150 PB raw 250 PB
2017-18/19• Upgrade internal
networking capacity
• Refresh tape infrastructure
Provisioning services Moving towards Elastic Hybrid IaaS model:• In house resources at full
occupation• Elastic use of
commercial & public clouds
• Assume “spot-market” style pricing
OpenStack Resource Provisioning
(>1 physical data centre)
HTCondor
Public Cloud
VMsContainersBare Metal and HPC
(LSF)
Volunteer Computing
IT & Experiment ServicesEnd Users CI/CD
APIsCLIsGUIs
Experiment Pilot Factories
HNSciCloud; 3 April 2017
CERN cloud procurementsSince ~2014, series of short CERN procurement projects of increasing scale and complexity
HNSciCloud; 3 April 2017 [email protected] 12
Future Challenges
BFirst run LS1 Second run LS2 Third run LS3 HL-LHC…
FCC?2013 2014 2015 2016 2017 201820112010 2012 2019 2023 2024 2030?20212020 2022 …2025
CPU:• x60 from 2016
Data:• Raw 2016: 50 PB 2027: 600 PB• Derived (1 copy): 2016: 80 PB 2027: 900 PB
HNSciCloud; 3 April 2017
Raw data volume for LHC increases exponentially and with it processing and analysis load
Technology at ~20%/year will bring x6-10 in 10-11 years Estimates of resource needs at HL-LHC x10 above what is realistic to expect from
technology with reasonably constant cost
HL-LHC computing cost parameters
HNSciCloud; 3 April 2017
Core AlgorithmsInfrastructureSoftware PerformanceParameters
Business of the experiments: amount of Raw data, thresholds; Detector design long term computing cost implications
Business of the experiments: reconstruction, and simulation algorithms
Performance/architectures/memory etc.;Tools to support: automated build/validationCollaboration with externals – via HSF
New grid/cloud models; optimize CPU/disk/network; economies of scale via clouds, joint procurements etc.
HEP Data cloudStorage and compute
Cloud users:Analysis
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Possible Model for future HEP computing infrastructure
Simulation resources
Conclusions WLCG has been very successful in providing the
global computing environment for physics at the LHC
Engagement and contributions of the worldwide community have been essential for that
LHC upgrades over the coming decade will give new challenges and opportunities Technology will change our computing models
HNSciCloud; 3 April 2017