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Extending the ATLAS PanDA Workload Management System For New Big Data Applications. XXIV International Symposium on Nuclear Electronics and Computing. Alexei Klimentov Brookhaven National Laboratory. Main topics. Introduction Large Hadron Collider at CERN ATLAS experiment

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alexei klimentov brookhaven national laboratory

Extending the ATLAS PanDA Workload Management System For New Big Data Applications

XXIV International Symposium on Nuclear Electronics and Computing

Alexei Klimentov

Brookhaven National Laboratory

September 12, 2013, Varna

main topics
Main topics
  • Introduction
    • Large Hadron Collider at CERN
    • ATLAS experiment
  • ATLAS Computing Model and Big Data Experiment Computing Challenges
  • PanDA :
    • Workload Management System for Big Data

NEC 2013

enter a new era in fundamental science
Enter a New Era in Fundamental Science

The Large Hadron Collider (LHC), one of the largest and truly global scientific projects ever built, is the most exciting turning point in particle physics.





Exploration of a new energy frontier

Proton-proton and Heavy Ion collisions

at ECM up to 14 TeV

LHC ring:

27 km circumference




Korea and CERN / July 2009


Proton-Proton Collisions at the LHC

  • LHC delivered billions of collision events to the experiments from proton-proton and proton-lead collisions in the Run 1 period (2009-2013)

→ collisions every50 ns

= 20 MHz crossing rate

  • 1.6 x 1011 protons per bunch
  • atLpk ~ 0.8x1034/cm2/s

≈ 35 pp interactions per crossing – pile-up

→ ≈ 109 pp interactions per second !!!

  • in each collision

≈ 1600 charged particlesproduced

  • enormous challenge for the detectors and for
  • data collection/storage/analysis
  • Raw data rate from LHC detector : 1PB/s
  • This translates to Petabytes of data recorded world-wide (Grid)
  • The challenge how to process and analyze the data and produce timely physics results was substantial, but at the end resulted in a great success


Candidate Higgs decay

to four electrons recorded

by ATLAS in 2012.

our task
Our Task

We use experimentsto inquire about what“reality” (nature) does


We intend to fill this gap

  • The goal is to understandin the most general; that’susually also the simplest.
      • - A. Eddington


ATLAS Physics Goals

  • Explore high energy frontier of particle physics
  • Search for new physics
    • Higgs boson and its properties
    • Physics beyond Standard Model – SUSY, Dark Matter, extra dimensions, Dark Energy, etc
  • Precision measurements of Standard Model parameters

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atlas experiment at cern
ATLAS Experiment at CERN
  • A ThoroidalLHC ApparatuSis one of the six particle detectors experiments at Large Hadron Collider (LHC) at CERN
  • One of two multi-purpose detectors
  • The project involves more than 3000 scientists and engineers from 38 countries
  • ATLAS has 44 meters long and 25 meters in diameter, weighs about 7,000 tons. It is about half as big as the Notre Dame Cathedral in Paris and weighs the same as the Eiffel Tower or a hundred 747 jets

ATLAS Collaboration

3000 scientists

174 Universities and Labs

From 38 countries

More than 1200 students



6 Floors

Big Data Workshop. Knoxville, TN

atlas big data experiment
ATLAS . Big Data Experiment

150 million sensors deliver data …

Big Data Workshop

some numbers from atlas
Some numbers from ATLAS
  • data
    • Rate of events streaming out from High-Level Trigger farm ~400 Hz
    • each event has a size of the order of 1.5MB
    • about 107 events in total per day
    • will have roughly 170 “physics” days per year
    • thus about 109evts/year, a few Pbyte
  • “prompt” processing
    • Reco time per event on std. CPU: < 30 sec (on CERN batch node)
    • increases with pileup (more combinatorics in the tracking)
  • simulating a few billions of events
    • are mostly done at computing centers outside CERN
    • Simulation very CPU intensive
  • ~4 million lines of code (reconstruction and simulation)
    • ~1000 software developers on ATLAS

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reduce the data volume in stages
Reduce the data volume in stages

Higgs Selection using the Trigger

Level 1:

Not all information

available, coarse


Level 2:

Reconstruct events

Improved ability to reject


Level 3:

High quality reconstruction

algorithms, using information

from all detectors

400 Hz

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atlas high level trigger part
ATLAS High-Level Trigger (Part)

Total of 15,000 cores in 1,500 machines

Reduce data volume in stages

Select ONLY ‘interesting’ events

Initial data rate (50 ns) :

40 000 000 events/s

Selected and stored

400 events/s


Disk Buffer

We really do throw away 99.9999% of LHC data before writing it to persistent storage

Big Data in High Energy Physics

data analysis chain
Data Analysis Chain


  • Have to collect data from many channels on many sub-detectors (millions)
  • Decide to read out everything or throw event away (Trigger)
  • Build the event (put info together)
  • Store the data
  • Reconstruct data
  • Analyze them
    • Start with the output of reconstruction
    • Apply event selection based on reconstructed objects quantities
    • Estimate efficiency of selection
    • Estimate background after selection
    • Make plots
  • do the same with a simulation
    • correct data for detector effects
  • Make final plots
    • Compare data and theory
  • Two main types of physics analysis at LHC
    • Searching for new particles
    • Making precision measurements
  • Searches statistically limited
    • More data is the way of improving the search
    • If don’t see anything new set limits on what you have excluded
  • Precision measurements
    • Precision often limited by the systematic uncertainties
    • Precision measurements of Standard Model parameters allows important tests of the consistency of the theory



ATLAS Data Challenge

The ATLAS Data Challenge

  • 800,000,000 proton-proton interactions per second
  • 0.0002 Higgs per second
  • ~150,000,000 electronic channels

Starting from this event…

We are looking for this “signature”

Selectivity: 1 in 1013

Like looking for 1 person in a thousand world populations

Or for a needle in 20 million haystacks!

data volume and data storage
Data Volume and Data Storage
  • 1 event size 1.5 MByte x Rate 400 Hz
    • Taking into account LHC duty cycle
      • Order of 3 PBytes per year per experiment
  • ATLAS total data storage 130+ PetaBytes distributed between O(100) computing centers
  • “New” physics is rare and interesting events are like single drop from the Jet d’Eau

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big data in the arts and humanities
Big Data in the arts and humanities

Letter of Benjamin Franklin to Lord Kames, April 11, 1767. Franklin warned British official what would happen if the English kept trying to control the colonists by imposing taxes, like the Stamp Act. He warned that they would revolt.

The political, scientific and literary papers of Franklin comprise approx. 40 volumes containing approx. 100,000 documents.

big data in the arts and humanities1
Big Data in the arts and humanities

George W. Bush Presidential Library:

200 million e-mails

4 million photographs

A.Prescott slide

big data has arrived at an almost unimaginable scale
Big Data Has Arrived at an Almost Unimaginable Scale

Library of congress

Business e-mails sent per year

3000 PBytes

Content Uploaded

to Facebook each year.

182 PBytes

ATLAS Managed

Data Volume

130 PBytes


ATLAS Annual

Data Volume

30 PBytes

LHC Annual

15 PBytes


Google search index

98 PBytes


15 PBytes

Health Records

30 PBytes

US census

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atlas computing challenges
ATLAS Computing Challenges
  • A lot of data in a highly distributed environment.
    • Petabytes of data to be treated and analyzed
    • ATLAS Detector generates about 1PB of raw data per second – most filtered out in real time by the trigger system
    • Interesting events are recorded for further reconstruction and analysis
    • As of 2013 ATLAS manages ~130 PB of data, distributed world-wide to O(100) computing centers and analyzed by O(1000) physicists
    • Expected rate of data influx into ATLAS Grid ~40 PB of data per year in 2015
  • Very large international collaboration
    • 174 Institutes and Universities from 38 countries
    • Thousands of physicists analyze the data

ATLAS uses grid computing paradigm to organize distributed resources

A few years ago ATLAS started Cloud Computing RnD project to explore virtualization and clouds

    • Experience with different cloud platforms : Commercial (Amazon, Google), Academic, National

Now we are evaluating how high-performance and super-computers can be used for data processing and analysis

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atlas computing model
ATLAS Computing Model
  • The LHC experiments rely on distributed computing resources
    • World LHC Computing Grid – a global solution, based on Grid technologies/middleware
    • Tiered structure
      • Tier0 (CERN), 11 Tier1s, 140 Tier2s
      • Capacity
        • 350,000 CPU cores
        • 200 PB of disk space
        • 200 PB of tape space
    • In ATLAS sites grouped into clouds for organizational reasons
    • Possible communications
      • Optical private network
        • T0:T1
        • T1:T1
      • National network
        • T1-T2
    • Restricted communications
      • Inter-cloud T1:T2
      • Inter-cloud T2:T2

K.De slide

Alexei Klimentov

atlas data volume
ATLAS Data Volume

ATLAS data volume on the Grid sites (multiple replicas)


131.5 PBytes

Derived data

Raw data




End Run 1

Data Distribution patterns are discipline dependent.

(ATLAS RAW data volume ~3 PB/year, ATLAS Data Volume on Grid sites 131.5 PBytes)

how does 3 pb become 130 pb
How does 3 PB become 130+ PB
  • Duplicate the raw data (not such a bad idea)
  • Add a similar volume of simulated data (essential)
  • Make a rich set of derived data products (some of them larger than the raw data)
  • Re-create the derived data products whenever the software has been significantly improved (several times a year) and keep the old versions for quite a while
  • Place up to 15§ copies of the derived data around the world so that when you “send jobs to the data” you can send it almost anywhere
  • Do the math!

§ now far fewer copies due to demand-driven temporary replication – but much more reliance on wide-area networks

processing the experiment big data
Processing the Experiment Big Data
  • The simplest solution in processing LHC data is using data affinity for the jobs
    • Data is staged to the site where the compute resources are located and data access by analysis code from local, site-resident storage
    • However
      • In distributed computing environment we don’t have enough disk space to host all our data at every Grid site
        • Thus we distribute (pre-place) our data across our sites
      • The popularity of data sets is difficult predict in advance
        • Thus computing capacity at a site might not match the demand for certain data sets
    • Different approaches are being implemented
      • Dynamic or/and on demand data replication
        • Dynamic : if certain data is popular over Grid (i.e. processed or/and analyzed often) make additional copies on other Grid sites (up to 15 replicas)
        • On-demand : User can request local or additional data copy
      • Remote access
        • The popular data can be accessed remotely
      • Both approaches have the underlying scenario that puts the WAN between the data and the executing analysis code

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panda production and data analysis system
PanDA – Production and Data Analysis System
  • ATLAS computational resources are managed by PanDA Workload Management System (WMS)
  • PanDA project was started in fall of 2005 by BNL and UTA groups
    • Production and Data Analysis system
    • An automated yet flexible workload management system which can optimally make distributed resources accessible to all users
      • Adopted as the ATLAS wide WMS in 2008 (first LHC data in 2009) for all computing applications. Adopted by AMS in 2012, in pre-production by CMS.
  • Through PanDA, physicists see a single computing facility that is used to run all data processing for the experiment, even though data centers are physically scattered all over the world.
    • PanDA is flexible
      • Insulates physicists from hardware, middleware and complexities of underlying systems
      • In adapting to evolving hardware and network configuration
  • Major groups of PanDA jobs
    • Central computing tasks are automatically scheduled and executed
    • Physics groups production tasks, carried out by group of physicists of varying size are also processed by PanDA
    • User analysis tasks
  • Now successfully manages O(102) sites, O(105) cores, O(108) jobs per year, O(103) users

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panda philosophy
PanDA Philosophy
  • PanDA Workload Management System design goals
    • Deliver transparency of data processing in a distributed computing environment
    • Achieve high level of automation to reduce operational effort
    • Flexibility in adapting to evolving hardware, computing technologies and network configurations
    • Scalable to the experiment requirements
    • Support diverse and changing middleware
    • Insulate user from hardware, middleware, and all other complexities of the underlying system
    • Unified system for central Monte-Carlo production and user data analysis
      • Support custom workflow of individual physicists
    • Incremental and adaptive software development

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panda atlas workload management system
PanDA. ATLAS Workload Management System

Production managers

Data Management System (DQ2)

PanDA server




production job












(Production DB)














ARC Interface









Worker Nodes

atlas distributed computing
ATLAS Distributed Computing.

Number of concurrently running PanDA jobs (daily average). Aug 2012- Aug 2013



MC Simulation

150k ->

  • Includes central production and data (re)processing, user and group analysis on WLCG Grid
  • Running on ~100,000 cores worldwide, consuming at peak 0.2 petaflops
  • Available resources fully used/stressed
atlas distributed computing1
ATLAS Distributed Computing.

Number of completed PanDA jobs (daily average. Max 1.7M jobs/day).

Aug 2012- Aug 2013



1M ->

Jobs types

atlas distributed computing2
ATLAS Distributed Computing.

Data Transfer Volume in TB (weekly average). Aug 2012- Aug 2013


CERN to BNL data transfer

time .An average 3.7h

to export data from CERN

6 PBytes ->

panda s success
PanDA’s Success

PanDA was able to cope with increasing LHC luminosity and ATLAS data taking rate

Adopted to evolution in ATLAS computing model

Two leading experiments in HEP and astro-particle physics(CMS and AMS) has chosen PanDA as workload management system for data processing and analysis.

ALICE is interested in PanDA evaluation for Grid MC Production and Lеadership Computing Facilities.

PanDA was chosen as a core component of Common Analysis Framework by CERN-IT/ATLAS/CMS project

PanDA was cited in the document titled “Fact sheet: Big Data across the Federal Government” prepared by the Executive Office of the President of the United States as an example of successful technology already in place at the time of the “Big Data Research and Development Initiative” announcement

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evolving panda for advanced scientific computing
Evolving PanDA for Advanced Scientific Computing
  • Proposal titled “Next Generation Workload Management and Analysis System for BigData” – Big PanDA was submitted to ASCR DoE in April 2012.
  • DoE ASCR and HEP funded project started in Sep 2012.
    • Generalization of PanDA as meta application, providing location transparency of processing and data management, for HEP and other data-intensive sciences, and a wider exascale community.
      • Other efforts
        • PanDA : US ATLAS funded project
        • Networking : Advance Network Services
  • There are three dimensions to evolution of PanDA
    • Making PanDA available beyond ATLAS and High Energy Physics
    • Extending beyond Grid (Leadership Computing Facilities, Clouds, University clusters)
    • Integration of network as a resource in workload management

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big panda work plan
“Big PanDA” work plan
  • Factorizing the code
    • Factorizing the core components of PanDA to enable adoption by a wide range of exascale scientific communities
  • Extending the scope
    • Evolving PanDA to support extreme scale computing clouds and Leadership Computing Facilities
  • Leveraging intelligent networks
    • Integrating network services and real-time data access to the PanDA workflow
  • 3 years plan
    • Year 1. Setting the collaboration, define algorithms and metrics
    • Year 2. Prototyping and implementation
    • Year 3. Production and operations

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big panda factorizing the core
“Big PanDA”. Factorizing the Core
  • Evolving PanDA pilot
    • Until recently the pilot has been ATLAS specific, with lots of code only relevant for ATLAS
      • To meet the needs of the Common Analysis Framework project, the pilot is being refactored
    • Experiments as plug-ins
      • Introducing new experiment specific classes, enabling better organization of the code
      • E.g. containing methods for how a job should be setup, metadata and site information handling etc, that is unique to each experiment
      • CMS experiment classes have been implemented
    • Changes are being introduced gradually, to avoid affecting current production
  • PanDA instance @Amazon Elastic Compute Cloud (EC2)
    • Port PanDA database back to mySQL
    • VO independent
    • It will be used as a test-bed for non-LHC experiments
        • PanDA Instance with all functionalities is installed and running at EC2. Database migration from Oracle to MySQL is finished. The instance is VO independent.
          • LSST MC production is the first use-case for the new instance
    • Next step will be refactoring PanDA monitoring package

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big panda extending the scope
“Big PanDA”. Extending the scope
  • Why we are looking for opportunistic resources ?
    • More demanding LHC environment after 2014
      • Higher energy, more complex collisions…
      • We plan to record, process and analyze more data
        • Physics motivated (Higgs coupling measurement, Physics beyond Standard Model – SUSY, Dark Matter, extra dimensions, Dark Energy, etc)
    • The demands on computing resources to accommodate the Run2 physics needs increase
      • HEP now risks to compromise physics because of lack of computing resources$.
        • Has not been true for ~20 years
    • Several venues to explore in the next years
      • Optimizing/changing our workflows, both on analysis and on the Grid
      • High-Performance Computing
      • Leadership Computing Facilities
      • Research and Commercial Clouds
      • “ATLAS@home”
  • Common characteristic to the opportunistic resources: we have to be agile in how we use them
    • Quick onto them (software, data and workloads) when they become available
    • Quick off them when they’re about to disappear
    • Robust against their disappearing under us with no notice
    • Use them until they disappear – don’t allow holes with unused cycles, fill them with fine grained workloads

$ I.Bird(WLCG Leader) presentation at HPC and super-computing workshop for Future Science Applications (BNL, Jun 2013)

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Spikes in demand for computational resources

Can significantly exceed available ATLAS Grid resources

Lack of resources slows down pace of discovery

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“Big PanDA”. Extending the scope

  • Compute Engine (GCE) preview project
    • Google allocated additional resources for ATLAS for free
      • ~5M cpu hours, 4000 cores for about 2 month, (original preview allocation 1k cores)
  • Resources are organized as HTCondor based PanDA queue
    • Centos 6 based custom built images, with SL5 compatibility libraries to run ATLAS software
    • Condor head node, proxies are at BNL
    • Output exported to BNL SE
  • Work on capturing the GCE setup in Puppet
  • Transparent inclusion of cloud resources into ATLAS Grid
  • The idea was to test long term stability while running a cloud cluster similar in size to Tier 2 site in ATLAS
  • Intended for CPU intensive Monte-Carlo simulation workloads
  • Planned as a production type of run. Delivered to ATLAS as a resource and not as an R&D platform.
  • We also tested high performance PROOF based analysis cluster

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running panda on google compute engine
Running PanDA on Google Compute Engine

Failed and Finished Jobs

reached throughput of 15K jobs per day

most of job failures occurred during start up and scale up phase

  • We ran for about 8 weeks (2 weeks were planned for scaling up)
  • Very stable running on the Cloud side. GCE was rock solid.
  • Most problems that we had were on the ATLAS side.
  • We ran computationally intensive jobs
    • Physics event generators, Fast detector simulation,Full detector simulation
  • Completed 458,000 jobs, generated and processed about 214 M events

Alexei Klimentov

big panda for leadership computing facilities
“Big PanDA” for Leadership Computing Facilities
  • Expanding PanDA from Grid to Leadership Class Facilities (LCF) will require significant changes in our system
  • Each LCF is unique
    • Unique architecture and hardware
    • Specialized OS, “weak” worker nodes, limited memory per WN
    • Code cross-compilation is typically required
    • Unique job submission systems
    • Unique security environment
  • Pilot submission to a worker node is typically not feasible
  • Pilot/agent per supercomputer or queue model
  • Tests on BlueGene at BNL and ANL. Geant4 port to BG/P
  • PanDA project at Oak-Ridge National Laboratory LCF Titan
leadership computing facilities titan
Leadership Computing Facilities. Titan

Slide from Ken Read

Big Data Workshop

big panda project on ornl lcf
“Big PanDA” project on ORNL LCF
  • Get experience with all relevant aspects of the platform and workload
    • job submission mechanism
    • job output handling
    • local storage system details
    • outside transfers details
    • security environment
    • adjust monitoring model
  • Develop appropriate pilot/agent model for Titan
    • Collaboration between ANL, BNL, ORNL, SLAC, UTA, UTK
    • Cross-disciplinary project - HEP, Nuclear Physics , High-Performance Computing

“Big PanDA”/ORNL Common Project.

ATLAS PanDA Coming to Oak-Ridge Leadership Computing Facilities


“Big PanDA”at ORNL

PanDA & multicore jobs scenario


PanDAon Oak-Ridge Leadership Computing Facilities

      • PanDA deployment at OLCF was discussed and agreed, including AIMS project component
  • Cyber-Security issues were discussed both for the near and longer term.
  • Discussion with OLCF Operations
  • ROOT based analysis is tested
  • Payloads for TITAN CE (followed by discussion in ATLAS)

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adding network awareness to panda
Adding Network Awareness to PanDA
  • LHC Computing model for a decade was based on MONARC model
    • Assumes poor networking
      • Connections are seen as not sufficient or reliable
        • Data needs to be preplaced. Data comes from specific places
    • Hierarchy of functionality and capability
      • Grid sites organization in “clouds” in ATLAS
      • Sites have specific functions
      • Nothing can happen utilizing remote resources on the time of running job
    • Canonical HEP strategy : “Jobs go to data”
      • Data are partitioned between sites
        • Some sites are more important (get more important data) than others
        • Planned replicas
          • A dataset (collection of files produced under the same conditions and the same SW) is a unit of replication
      • Data and replica catalogs are needed to broker jobs
      • Analysis job requires data from several sites triggers data replication and consolidation at one site or job splitting on several jobs running on all sites
        • A data analysis job must wait for all its data to be present at the site
          • The situation can easily degrade into a complex n-to-m matching problem

There was no need to consider network as a resource in WMS in static data distribution scenario

  • New networking capabilities and initiatives in the last 2 years (like LHCONE)
    • Extensive standardized monitoring from network performance monitoring (perfSONAR)
    • Traffic engineering capabilities
      • Rerouting of high impact flows onto separate infrastructure
    • Intelligent networking
        • Virtual Network On Demand
        • Dynamic circuits

…and dramatic changes in computing models

    • From strict hierarchy of connections becomes more of a mesh
    • Data access over wide area
    • “no division” in functionality between sites

We would like to benefit from new networking capabilities and to integrate networking services with PanDA. We start to consider network as a resource on similar way as for CPUs and data storage

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network as a resource
Network as a resource
  • Optimal site selection to run PanDA jobs
    • Take network capability into account in jobs assigning and task brokerage
      • Assigned -> Activated jobs workflow
        • Number of assigned jobs depend on number of running jobs – can we use network status to adjust rate up/down?
        • Jobs are reassigned if transfer times out (fixed duration) – can knowledge of network status help reduce the timeout?
      • Task brokerage
        • Free disk space in Tier1
        • Availability of input dataset (a set of files)
        • The amount of CPU resources = the number of running jobs in the cloud (static information system is not used)
        • Downtime at Tier1
        • Already queued tasks with equal or higher priorities
        • High priority task can jump over low priority tasks
    • Can knowledge of network help
        • Can we consider availability of network as a resource, like we consider storage and CPU resources?
        • What kind of information is useful?
        • Can we consider similar (highlighted )factors for networking?

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intelligent network services and panda
Intelligent Network Services and PanDA
  • Quick re-run a prior workflow (bug found in reconstruction algo)
    • Site A has enough job slots but no input data
    • Input data are distributed between sites B,C and D, but sites have a backlog of jobs
    • Jobs may be sent to site A and at the same time virtual circuits to connect sites B,C,D to site will be built. VNOD will make sure that such virtual circuits have sufficient bandwidth reservation.
      • Or data can be accessed remotely (if connectivity between sites is reliable and this information is available from perfSONAR)
        • In canonical approach data should be replicated to site A
  • HEP computing is often described as an example of parallel workflow. It is correct on the scale of worker node (WN) . WN doesn’t communicate with other WN during job execution. But the large scale global workflow is highly interconnected, because each job typically doesn’t produce an end result in itself. Often data produced by a job serve as input to a next job in the workflow. PanDA manages workflow extremely well (1M jobs/day in ATLAS). The new intelligent services will allow to dynamically create the needed data transport channels on demand.

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intelligent network services and panda1
Intelligent Network Services and PanDA



In BigPanDA we will use information on how much bandwidth is available and can be reserved before data movement will be initiated

In Task Definition user will specify data volume to be transferred and deadline by which task should be completed. The calculations of (i) how much bandwidth to reserve, (ii) when to reserve, and (iii) along what path to reserve will be carried out by Virtual Network On Demand (VNOD).

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  • The ATLAS experiment Distributed Computing and Software performance was a great success in LHC Run 1
    • The challenge how to process and analyze the data and produce timely physics results was substantial, but at the end resulted in a great success
  • ASCR gave us a great opportunity to evolve PanDA beyond ATLAS and HEP and to start BigPanDA project
  • Project team was set up
  • The work on extending PanDA to LCF has started
  • Large scale PanDA deployments on commercial clouds are already producing valuable results
  • Strong interest in the project from several experiments (disciplines) and scientific centers to have a joined project.

Many thanks to J.Boyd, K.De, B.Kersevan, T.Maeno, R.Mount, P.Nilsson, D.Oleynik, S.Panitkin, A.Petrosyan, A.Prescott, K.Read, T.Wenausfor slides and materials used in this talk

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