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Computer science in astrophysics. My involvement in DEISA: ... JRA2 Cosmology. (EPCC leader) JRA3 Plasma Physics. (RZG leader) JRA4 Life Science. ... – PowerPoint PPT presentation

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1
The DEISA HPC Grid for Astrophysical Applications
Claudio Gheller CINECA (c.gheller_at_cineca.it)
2
Disclaimer
My background Computer science in
astrophysics My involvement in DEISA Support to
scientific extreme computing projects (DECI) Im
not A systems espert A networking expert
3
Conclusions
DEISA is not Grid computing It is (super) super
computing
4
The DEISA project overview
What is DEISA (Distributed European
Infrastructure for Super-computing Applications)
is a consortium of leading national EU
supercomputing centres Goals deploy and operate
a persistent, production quality, distributed
supercomputing environment with continental
scope. When The Project is funded by European
Commission May 2004 - April 2008. It has been
re-funded (DEISA2) May 2008 April 2010
5
The DEISA project drivers
  • Support High Performance Computing.
  • Integrate the Europes most powerful
    supercomputing systems.
  • Enable scientific discovery across a broad
    spectrum of science and technology.
  • Best exploitation of the resources both at site
    level and European level
  • Promote openness and usage of standards

6
The DEISA project what is NOT
  • DEISA is not a middleware development project.
  • DEISA, actually, is not a Grid it does not
    support Grid computing. Rather it supports
    Cooperative Computing.

7
The DEISA project core partners
BSC, Barcelona Supercomputing Centre,
Spain CINECA, Consorzio Interuniversitario,
Italy CSC, Finnish Information Technology Centre
for Science, Finland EPCC/HPCx, University of
Edinburgh and CCLRC, UK ECMWF, European Centre
for Medium-Range Weather Forecast, UK FZJ,
Research Centre Juelich, Germany HLRS, High
Performance Computing Centre Stuttgart,
Germany LRZ, Leibniz Rechenzentrum Munich,
Germany RZG, Rechenzentrum Garching of the Max
Planck Society, Germany IDRIS, Institut du
Développement et des Resources en Informatique
Scientifique CNRS, France SARA, Dutch National
High Performance Computing, Netherlands
8
The DEISA project Project Organization
Three activity areas Networking management,
coordination and dissemination Service
Activities running the infrastructure Joint
Research Activities porting and running
scientific applications on the DEISA
infrastructure
9
Deisa Activities, some (maybe too many) details
(1)
  • Service Activities
  • Network Operation and Support. (FZJ leader).
    Deployment and operation of a gigabit per second
    network infrastructure for an European
    distributed supercomputing platform.
  • Data Management with Global file systems. (RZG
    leader). Deployment and operation of global
    distributed file systems, as basic building
    blocks of the "inner" super-cluster, and as a way
    of implementing lobal data management in a
    heterogeneous Grid.
  • Resource Management. (CINECA leader). Deployment
    and operation of global scheduling services for
    the European super cluster, as well as for its
    heterogeneous Grid extension.
  • Applications and User Support. (IDRIS leader).
    Enabling the adoption by the scientific community
    of the distributed supercomputing infrastructure,
    as an efficient instrument for the production of
    leading computational science.
  • Security. (SARA leader). Providing
    administration, authorization and authentication
    for a heterogeneous cluster of HPC systems, with
    special emphasis on single sign-on

10
Deisa Activities, some (maybe too many) details
(2)
  • Scientific Applications Activities
  • JRA1 Material Science.
  • (RZG leader)
  • JRA2 Cosmology.
  • (EPCC leader)
  • JRA3 Plasma Physics.
  • (RZG leader)
  • JRA4 Life Science.
  • (IDRIS leader)
  • JRA5 Industry.
  • (CINECA leader)
  • JRA6 Coupled Applications.
  • (IDRIS leader)
  • JRA7 Access to Resources in Heterogeneous
    Environments.
  • (EPCC leader)

The DEISA Extreme Computing Initiative (DECI) See
http//www.deisa.org/applications
11
JRA2 Cosmological Applications
  • Goals
  • to avail the Virgo Consortium of the most
    advanced features of Grid computing by porting
    their production applications
  • GADGET and FLASH
  • to make an effective use of the DEISA
    infrastructure
  • to lay the foundations of a Theoretical Virtual
    Observatory
  • Leaded by EPCC which works in close partnership
    with the Virgo Consortium
  • JRA2 managed jointly by Gavin Pringle
    (EPCC/DEISA) and Carlos Frenk (co-PI of both
    Virgo and VirtU)
  • work progressed after gathering clear user
    requirements from Virgo Consortium.
  • requirements and results published as public
    DEISA deliverables.

12
Current DEISA status
  • variety of systems connected via GEANT/GEANT2
    (Premium IP)
  • centres contribute 5 to 10 of CPU cycles to
    DEISA
  • running projects selected from the DEISA Extreme
    Computing Initiative (DECI) calls

Premium IP is a service that offers network
priority over other traffic on GÉANT. Premium IP
traffic takes priority over all other services .
13
DEISA HPC systems
14
DEISA technical hints software stack
  • UNICORE is the grid glue
  • not built on Globus
  • EPCC developing UNICORE command-line interface
  • Other components
  • IBMs General Parallel File System
  • multiclusterGPFS can span different systems over
    a WAN
  • recent developments for Linux as well as AIX
  • IBMs Load Leveler for job scheduling
  • Multicluster Load Leveler can re-route batch
    jobs to different machines
  • also available on Linux

15
DEISA model
  • large parallel jobs running on a single
    supercomputer
  • network latency between machines not a
    significant issue
  • jobs submitted ideally - via UNICORE, in
    practice via Load Leveler
  • re-routed where appropriate to remote resources
  • Single-Sign-On access via GSI-SSH
  • GPFS absolutely crucial to this model
  • jobs have access to data no matter where they run
  • no source code changes required
  • standard fread/fwrite(or READ/WRITE) calls to
    Unix files
  • also have a Common Production Environment
  • defines a common set of environment variables
  • defined locally to map to appropriate resources
  • Eg DEISA_WORK will point to local workspace

16
Running ideally on DEISA
  • Fill all the gaps
  • restart/continue jobs on any machine from file
    checkpoints
  • no need to recompile application program
  • no need to manually stage data
  • multi-step jobs running on multiple machines
  • easy access to data for post-processing after a
    run

17
Running on DEISA Load Leveler
18
Running ideally on DEISA Unicore
19
GPFS Multicluster
HPC systems mount /deisa/sitename users
read/write directly from/to these file
systems /deisa/idr /deisa/cne /deisa/rzg /deisa/fz
j /deisa/csc
20
DEISA Common Production Environment (DCPE)
  • DCPE what is it?
  • both a set of software (the software stack) and a
    generic interface to access the software (based
    on the Modules tool)
  • Required to both offer a common interface to the
    users and to hide the differences between local
    installations
  • Essential feature for job migration inside
    homogeneous super-clusters
  • The DCPE includes
  • shells (Bash and Tcsh),
  • compilers (C, C, Fortran and Java),
  • libraries (for numerical analysis, data
    formatting, etc.),
  • tools (debuggers, profilers, editors, development
    tools),
  • applications.

21
Modules Framework
  • Modules tool chosen because it was well known by
    many sites and many users
  • Public domain software
  • Tcl implementation used
  • Modules
  • offer a common interface different software
    components on different computers,
  • to hide different names and configurations
  • to manage individually each software and load
    only those required into the user environment,
  • for each user to change the version of each
    software independently of the others,
  • for each user to switch independently between the
    current default version of a software to another
    one (older or newer).

22
The HPC users vision
Initial vision Full Distributed computing
Task2
Task3
Task1
23
The HPC users visions
Impossible!!!!
Initial vision Full Distributed computing
Task2
Task3
Task1
24
The HPC users vision
Jump computing
Task
Task
25
The HPC users vision
Jump computing
Difficult HPC applications are HPC
applications!!! Fine tuned on the architectures
Task
Task
26
So what
Jump computing is useful to reduce queue waiting
times. Find the gap and fill it can work,
better on homogeneous systems
27
So what
Single image filesystem is a great solution!!!!!
(even if moving data)
28
So what
Usual Grid solution requires to learn new stuff
Often scientists are not willing to DEISA rely
on Load Leveler (or other common scheduling
systems) same scripts, same commands you are
used to!!! However, only IBM systems support
LL The Common Production Environment offers a
shared (and friendly) set of tools to the
users. However, compromises must be accepted
29
Summing up
Growing up, DEISA is moving away from a
Grid. In order to fulfill the needs of HPC
users, it is trying to become a huge
supercomputer. On the other hand, DEISA2 must
lead to a service infrastructure and users
expectations MUST be matched (no more time for
experiments)
30
DECI enabling Science to DEISA
  • Identification, deployment and operation of a
    number of  flagship  applications requiring the
    infrastructure services, in selected areas of
    science and technology.
  • European Call for proposals in May - June every
    year. Applications are selected on the basis of
    scientific excellence, innovation potential and
    relevance criteria, with the collaboration of the
    HPC national evaluation committees.
  • DECI users are supported by the Applications Task
    Force (ATASKF), whose objective is to enable and
    deploy the Extreme Computing applications.

31
LFI-SIM DECI Project (2006)
Principal Investigator(s) Fabio Pasian (INAF- O.A.T.), Hannu Kurki-Suonio (Univ. of Helsinki)
Leading Institution INAF -O.A Trieste and Univ. of Helsinki
Partner Institution(s) INAF-IASF Bologna, Consejo Superior de Investigaciones Cientificas (Instituto de Fisica de Cantabria), Max-Planck Institut für Astrophysik Garching, SISSA Trieste, University of Milano, University Tor Vergata Rome
DEISA Home Site CINECA
  • Planck (useless) overview
  • Planck is the 3rd generation space mission for
    the mapping and the analysis of the microwave
    sky its unprecedented combination of sky and
    frequency coverage, accuracy, stability and
    sensitivity is designed to achieve the most
    efficient detection of the Cosmic Microwave
    Background ( CMB ) in both temperature and
    polarisation. In order to achieve the ambitious
    goals of the mission, unanimously acknowledged by
    the scientific community to be of the highest
    importance, data processing of extreme accuracy
    is needed.

32
Need of simulations in Planck
  • NOT the typical DECI-HPC project !!!
  • Simulations are used to
  • assess likely science outcomes
  • set requirements on instruments in order to
    achieve the expected scientific results
  • test the performance of data analysis algorithms
    and infrastructure
  • help understanding the instrument and its noise
    properties
  • analyze known and unforeseen systematic effects
  • deal with known physics and new physics.
  • Predicting the data is fundamental to understand
    them.

33
instrument parameters
Simulation pipeline
NEED OF HUGE COMPUTATIONAL RESOURCES GRID can be
a solution!!!
34
Planck DEISA
  • DEISA was expected to be used to
  • simulate many times the whole mission of Plancks
    LFI instrument, on the basis of different
    scientific and instrumental hypotheses
  • reduce, calibrate and analyse the simulated data
    down to the production of the final products of
    the mission, in order to evaluate the impact of
    possible LFI instrumental effects on the quality
    of the scientific results, and consequently to
    refine appropriately the data processing
    algorithms.

35
Outcomes
  • Planck simulations are essential to get the best
    possible understanding of the mission and to have
    a conscious expectation of the unexpected
  • They also allow to properly plan Data Processing
    Centre resources
  • The usage of the EGEE grid resulted to be more
    suitable for such project since it provides fast
    access to small/medium computing resources. Most
    of the Planck pipeline is happy with such
    resources!!!
  • However DEISA was useful to produce massive sets
    of simulated data and to perform and test the
    data processing steps which requires large
    computing resources (lots of coupled processors,
    large memories, large bandwidth)
  • Interoperation between the two grid
    infrastructures (possibly based on the G-Lite
    middleware) is expected in the next years
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