HPC 2008
High Performance Computing
and grids
An International
Advanced Research Workshop
June 30th –
Final Programme
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Programme
Committee
Organizing Committee
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Sponsors
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HEWLETT PACKARD |
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IBM |
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MICROSOFT |
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NEC |
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SUN |
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INTEL |
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Altair
Engineering |
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ENEA Italian National Agency for New
Technologies, Energy and the Environment |
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CINECA |
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SPACI Southern
Partnership for Advanced Computational Infrastructures |
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DataDirect Networks |
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ClusterVision |
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FZJ Juelich
Supercomputing Center |
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Nice |
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SiCortex |
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IEEE Computer
Society |
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TABOR
COMMUNICATIONS HPCWire, GridToday |
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Speakers David Abramson Clayton, Vic Avner Algom The Israeli
Association of Grid Technologies Mehiddin Al-Baali Dept. of Mathematics and Statistics Sultan Qaboos University Muscat OMAN Giovanni Aloisio Marcos Athanasoulis Frank Baetke Global HPC Technology Hewlett Packard Toine Beckers DataDirect Networks Inc. Pete Beckman Maths & Computer Science Division Patrizia Beraldi Dept. of Electronics, Informatics and Systems University of Calabria Rende, Cosenza ITALY John R. Boisseau The
Marian Bubak Academic
Computer Centre CYFRONET Krakow POLAND Franck Cappello Laboratoire de Recherche en Informatique INRIA Orsay Umit Catalyurek Department of Biomedical Informatics The Charlie Catlett Maths and Computer Science Division and Chicago, IL U.S.A. Kihyeon Cho e-Science Division KISTI - Daejon KOREA Antonio Congiusta NICE ITALY Tim David Centre for Martijn De Vries Jack Dongarra Innovative
Computing Laboratory Computer
Science Dept. Giovanni Erbacci System and Technology Department CINECA - Inter-University Consortium Casalecchio di Reno ITALY Sandro Fiore University of Salento Lecce ITALY Ian Foster Math & Computer Science Div. Argonne National Laboratory Argonne, IL and Dept. of Computer Science The Geoffrey Fox Community
Grid Computing Laboratory Alan Gara Blue Gene Supercomputers IBM Wolfgang Gentzsch DEISA Distributed European Infrastructure for
Supercomputing Applications and Stephan Gillich Intel - HPC EMEA Lucio Grandinetti Dept. of Electronics, Informatics and Systems University of Calabria Rende, Cosenza ITALY Atul Gurtu Tata Institute of Fundamental Research Mumbai Rick Hetherington Microelectronics Sun Microsystems, Inc André Höing Electrical Engineering and Computing Science Weiwu Hu Institute of Computing Technology Chris Jesshope Informatics Institute Faculty of Science William Johnston Computational Research Division Carl Kesselman Information Sciences Institute Marina del Ray, Los Angeles, CA Thomas Lippert John von Neumann-Institute for Computing (NIC) FZ Jülich Miron Livny
Ignacio Llorente Distributed
Systems Architecture Group Universidad
Complutense de Madrid Madrid SPAIN Fabrizio
Magugliani Sicortex EMEA Maynard, MA Satoshi Matsuoka Department
of Mathematical and Computing Sciences Tokyo
Institute of Technology Mirco Mazzucato INFN - Istituto Nazionale di Fisica Nucleare Paul formerly Caltech and Barton Miller Computer Sciences Dept. Per Öster CSC – Finnish IT Center for Science Espoo FINLAND Marcelo Pasin École Normale Supérieure de Lyon Laboratoire de l’informatique du parallélisme Lyon Robert Pennington Urbana, IL U.S.A. Daniel Reed Microsoft
Research Redmond,
Seattle formerly and Renaissance
Computing Institute Yves Robert Ecole
Normale Supérieure de Lyon Anatoly Sachenko Department
of Information Computing Systems and Control Ternopil Rizos Sakellariou Takayuki Sasakura NEC HPCE Alex Shafarenko Department of Computer Science Hatfield Mark Silberstein Technion-Israel Institute of Technology Haifa ISRAEL Derek Simmel Pittsburgh Supercomputing Center Pittsburgh, PA U.S.A. Peter Sloot Faculty of Science Amsterdam NETHERLANDS Achim Streit Jülich Supercomputing Centre (JSC) at
Forschungszentrum Jülich Domenico Talia Dept. of Electronics, Informatics and Systems University of Calabria Rende, Cosenza ITALY Abderezak Touzene AL-Khod Anne Trefethen Paolo Trunfio Dept. of Electronics, Informatics and Systems University of Calabria Rende, Cosenza ITALY Jeffrey Vetter Computer Science and Maths
Division and Georgia Institute of Technology Atlanta, GA U.S.A. |
Workshop Agenda
MONDAY, June 30
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Session |
Time |
Speaker/Activity |
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9.00
– 9.10 |
Welcome Address |
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State of the Art and
Future scenarios of HPC and Grid |
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9.10
– 9.45 |
J. Dongarra |
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9.45
– 10.15 |
I. Foster “Towards
an Open Analytics Environment” |
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10.15
– 10.45 |
D. Reed |
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10.45
- 11.15 |
A. Gara “Present and future challenges as we architect for the
Exascale” |
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11.15
– 11.45 |
COFFEE BREAK |
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11.45– 12.15 |
A. Trefethen |
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12.15 – 12.45 |
“The Evolution of Research and Education Networks and
their Essential Role in Modern Science” |
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12.45 – 13.00 |
Concluding Remarks |
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Emerging
Computer Systems and Solutions |
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17.00
– 17.25 |
F. Baetke |
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17.25
– 17.50 |
S. GILLICH “Intel - Delivering Leadership HPC Technology Today and
Tomorrow” |
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17.50
– 18.15 |
t. sasakura “NEC’s HPC
Strategy - Consistency and Innovation” |
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18.15
– 18.45 |
COFFEE BREAK |
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18.45
– 19.10 |
t. beckers “High Performance Storage Solutions from DataDirect
Networks” |
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19.10–
19.35 |
M. DE VRIES |
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19.35
– 20.00 |
F. MAGUGLIANI |
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20.00
– 20.10 |
Concluding Remarks |
TUESDAY, July 1
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Session |
Time |
Speaker/Activity |
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Advances in HPC Technology and Systems 1 |
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9.00
– 9.25 |
W. HU “The Godson-3 multi-core CPU and its application in High Performance
Computers” |
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9.25
– 9.50 |
R. Hetherington |
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9.50
– 10.15 |
C. Jesshope “Managing resources dynamically in SVP - from many-core to
Grid” |
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10.15
– 10.40 |
A. Shafarenko |
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10.40
– 11.05 |
F. CAPPELLO “Fault Tolerance for PetaScale
Systems: Current Knowledge, Challenges and Opportunities” |
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11.05
– 11.35 |
COFFEE BREAK |
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11.35
– 12.00 |
P. Beckman “The
Path to Exascale Computing” |
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12.00
– 12.25 |
S. Matsuoka |
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12.25
– 12.50 |
J. Vetter “HPC Interconnection Networks – The Key to Exascale
Computing” |
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12.50
– 13.00 |
Concluding Remarks |
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Advances in HPC Technology and Systems 2 |
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16.30
– 17.00 |
J. Boisseau “Deployment Experiences, Performance Observations, and Early
Science Results on Ranger” |
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17.00
– 17.25 |
R. PENNINGTON “NCSA Blue Waters: Preparing for the Sustained Petascale System” |
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17.25
– 17.50 |
T. LIPPERT |
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17.50
– 18.15 |
B. MILLER |
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18.15
– 18.45 |
COFFEE BREAK |
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18.45
– 20.00 |
PANEL DISCUSSION
1: “EXASCALE COMPUTING” Chairman: P.
Messina Co-organizers: P. Beckman,
P. Messina Panelists: P. Beckman, A. Gara, D.
Reed, S. Matsuoka, R. Vetter |
WEDNESDAY, July 2
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Session |
Time |
Speaker/Activity |
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Grid Technology and Systems 1 |
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9.00
– 9.25 |
M. Livny “Old problems never die – managing the multi-programming mix” |
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9.25
– 9.50 |
D. Abramson “Active Data: Blurring the distinction between data and
computation” |
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9.50
– 10.15 |
D. Talia “Using Peer-to-Peer Dynamic Querying in Grid Information
Services” |
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10.15
– 10.40 |
Y. ROBERT “Algorithms and scheduling techniques for clusters and grids” |
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10.40
– 11.05 |
R. SAKELLARIOU “Feedback control for efficient autonomic solutions on
the Grid” |
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11.05
– 11.35 |
COFFEE BREAK |
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11.35
– 12.00 |
C. Catlett |
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12.00
– 12.25 |
A. Algom “From Grid Computing to Cloud Computing - The evolution of the
Grid Marketplace” |
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12.25
– 12.50 |
I. Llorente |
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12.50
– 13.00 |
Concluding Remarks |
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Grid Technology
and Systems 2 |
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17.00
– 17.25 |
M. PASIN “Network resource reservation and virtualization for grid
applications” |
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17.25
– 17.50 |
A. TOUZENE “A Performance Based Distribution Algorithm for Grid
Computing |
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17.50
– 18.15 |
C. KESSELMAN “Applications
of Grid Technology to Health Care Systems” |
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18.15
– 18.45 |
COFFEE BREAK |
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18.45
– 20.30 |
Panel discussion 2: “FROM
GRIDS TO CLOUD SERVICES” Organizer: C. CATLETT Panelists: Avner Algom, Pete Beckman, Charlie Catlett, Ignacio Llorente, Satoshi Matsuoka |
THURSDAY, July 3
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Session |
Time |
Speaker/Activity |
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Infrastructures, Instruments, Products, Solutions
for High Performance Computing and Grids |
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9.00
– 9.25 |
G. FOX |
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9.25
– 9.50 |
A. HÖING “Summary-based Distributed Semantic Database for Resource and
Service |
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9.50
– 10.15 |
A. STREIT |
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10.15
– 10.40 |
W. GENTZSCH |
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10.40
– 11.05 |
M. SILBERSTEIN “Superlink-online - delivering
the power of GPUs, clusters and |
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11.05
– 11.35 |
COFFEE BREAK |
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11.35
– 12.00 |
M. BUBAK “Building collaborative applications for system-level science” |
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12.00
– 12.25 |
D. SIMMEL |
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12.25
– 12.50 |
A. CONGIUSTA |
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12.50 – 13.00 |
Concluding Remarks |
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National and International Grid
Infrastructures and Projects |
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17.00
– 17.25 |
D. ABRAMSON |
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17.25
– 17.50 |
K. CHO |
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17.50
– 18.15 |
A. GURTU |
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18.15
– 18.45 |
COFFEE BREAK |
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18.45
– 19.10 |
A. SACHENKO |
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19.10–
19.35 |
P. ÖSTER |
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19.35
– 20.00 |
M. MAZZUCATO “Italian
Grid Infrastructure” |
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20.00
– 20.10 |
Concluding Remarks |
FRIDAY, July 4
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Session |
Time |
Speaker/Activity |
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Challenging Applications of HPC and Grids |
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9.00
– 9.25 |
M. ATHANASOULIS “Building Shared High Performance Computing
Infrastructure for the Biomedical Sciences” |
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9.25
– 9.50 |
P. SLOOT “ViroLab: Distributed Decision
Support in a virtual laboratory for
infectious diseases” |
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9.50
– 10.15 |
U. CATALYUREK “Processing of Large-Scale Biomedical Images on a Cluster
of Multi-Core CPUs and GPUs” |
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10.15
– 10.40 |
T. DAVID “A
Heterogeneous Computing Model for a Grand Challenge Problem” |
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10.40
– 11.05 |
L. GRANDINETTI – P. BERALDI |
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11.05
– 11.35 |
COFFEE BREAK |
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11.35
– 12.00 |
G. ALOISIO – S. FIORE |
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12.00
– 12.25 |
G. ERBACCI “An advanced HPC
infrastructure in Italy for challenging scientific applications” |
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12.25
– 12.50 |
K. CHO |
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12.50
– 13.00 |
Concluding Remarks |
ABSTRACTS
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Scheduling for Numerical Linear Algebra Library at Scale Jack Dongarra Innovative
Computing Laboratory Computer
Science Dept. University
of Tennessee In this
talk we will look at some of the issues numerical library developers are
facing when using manycore systems with millions of
threads of execution. |
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Clouds and ManyCore: The Revolution Daniel A. Reed Microsoft Research Redmond, As Yogi Berra famously noted, “It’s hard to make predictions,
especially about the future.” Without doubt, though, scientific discovery,
business practice and social interactions are moving rapidly from a world of
homogeneous and local systems to a world of distributed software, virtual
organizations and cloud computing infrastructure. In science, a tsunami of new experimental
and computational data and a suite of increasingly ubiquitous sensors pose
vexing problems in data analysis, transport, visualization and collaboration.
In society and business, software as a service and cloud computing are
empowering distributed groups. Let’s
step back and think about the longer term future. Where is the technology
going and what are the research implications?
What architectures are appropriate for 100-way or 1000-way multicore designs?
How do we build scalable infrastructure? How do we develop and support
software? What is the ecosystem of
components in which they will operate? How do we optimize performance, power
and reliability? Do we have ideas and
vision or are we constrained by ecosystem economics and research funding
parsimony? Biographical Sketch Daniel A. Reed is Microsoft’s Scalable and Multicore
Computing Strategist, responsible for re-envisioning the data center of the future.
Previously, he was the Chancellor’s Eminent Professor at UNC Chapel
Hill, as well as the Director of the Renaissance Computing Institute (RENCI)
and the Chancellor’s Senior Advisor for Strategy and Innovation for UNC
Chapel Hill. Dr. Reed is a member of
President Bush’s Council of Advisors on Science and Technology (PCAST) and a
former member of the President’s Information Technology Advisory Committee
(PITAC). He recently chaired a review
of the federal networking and IT research portfolio, and he is chair of the
board of directors of the Computing Research Association. He was previously Head of the Department of Computer Science at the
University of Illinois at Urbana-Champaign (UIUC). He has also been Director
of the National Center for Supercomputing
Applications (NCSA) at UIUC, where he also led National Computational Science
Alliance. He was also one of the principal investigators and chief architect
for the NSF TeraGrid. He received his PhD in computer science in
1983 from Purdue University. |
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Present and future challenges as
we architect for the Exascale Alan Gara Dept. of Computer Science In this
presentation current trends toward achieving Petascale
computing are examined. These current trends will be contrasted with what is
needed to reach the Exascale. Possible directions
and critical enabling technologies will be discussed. |
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Effective computing on heterogeneous platforms Anne Trefethen We have entered an era where at every scale of
computing - desktop, high-performance and distributed - we need to deal with
heterogeneity. Systems are made up of multicore chips and accelerators in an assortment of
hardware architectures and software environments. This has created a complexity for
scientific application developers and algorithm developers alike. Our focus is on effective algorithms and
environments across these scales to support efficient scientific application
development. |
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The Evolution of Research and Education Networks William E. Johnston Senior Scientist and Energy Sciences Network (ESnet)
Department Head Lawrence Berkeley National Laboratory In the
past 15 years there has been a remarkable increase in the volume of data that
must be analyzed in world-wide collaborations in order to accomplish the most
advanced science and a corresponding increase in network bandwidth,
deployment, and capabilities to meet these needs. Further, these changes have
touched all aspects of science including, in addition to data analysis,
remote conduct of experiments and multi-component distributed computational
simulation. Terabytes
of data from unique and very expensive instruments must be collaboratively
analyzed by the many science groups involved in the experiments. The highly
complex, long-running simulations needed to accurately represent macro-scale
phenomenon such as the climate, stellar formation, in-vivo cellular
functioning in complex organisms, etc., all involve building applications that
incorporate and use components that are located at the home institutions of
many different scientific groups. The
volume of traffic in research and education networks has increased
exponentially since about 1990. Virtually all of this increase – demonstrably
so in the past five years – is due to increased use of the network for moving
vast quantities of data among scientific instruments and widely distributed
analysis systems, and among supercomputers and remote analysis centers. Further, this data movement is no longer
optional for science: Increasingly large-scale science is dependent on
network-based data movement in order for the science to be successful. Modern
science approaches require that networks provide not only high bandwidth, but
also advanced services. Scheduled and on-demand bandwidth enables connection
and simultaneous operation of instruments, local compute clusters,
supercomputers, and large storage systems. Low latency, high bandwidth,
secure circuits interconnect components of simulations running on systems
scattered around the country and internationally. Comprehensive, global
monitoring and reporting that allow distributed workflow systems to know
exactly how end-to-end paths that transit many different networks are
performing. At the same time, the network must provide a level of reliability
that is commensurate with the billion dollar instrument systems, scarce
supercomputers, and the hundreds of collaborating scientific groups being
interconnected that is typical of large-scale science. In this
talk I will look at how network architectures, technologies, and services
have evolved over the past 15 years to meet the needs of science that now
uses sophisticated distributed systems as an integral part of the process of
doing science. One result of this is that the R&E community has some
unique communications requirements and some of the most capable networks in
the world to satisfy those requirements. I will also look at the projected
requirements for science over the next 5 to 10 years and how the R&E
networks must further expand and evolve to meet these future requirements. |
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Grids, Clouds and HPC: Opportunities and Challenges Dr.
Frank Baetke - Global HPC Technology Program
Manager New trends in the HPC area can be derived
from increasing growth-rates at the lower end of the market, specifically at
the workgroup and departmental level, and from concepts which are based on the
original promises of computational grids. Those trends combined with the ever
increasing demand for even higher
component densities and higher energy efficiency generate additional
challenges: examples of new products will be shown which specifically address
those issues. |
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Intel - Delivering Leadership HPC
Technology Today and Tomorrow Stephan Gillich Director HPC EMEA Intel We are excited
about the opportunity that lies in front of us as our |
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High Performance
Storage Solutions from DataDirect Networks Toine Beckers DataDirect Networks Inc., With the growing needs for High Performance
Computing clusters (from GFlops to TFlops and even PFlops systems)
in many application fields also the need for more and more data storage capacity
increases as well. This often leads to complex, difficult to manage storage
solutions. With the Silicon Storage Appliance products from DataDirect Networks an easy to manage, scalable and high
performance solution is provided which is becoming widely accepted in the
High Performance Computing Community. |
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Next-Generation Cluster Management with ClusterVisionOS Martijn De Vries Setting up and managing a large cluster can
be a challenging task without In this presentation, various aspects of the ClusterVisionOS cluster |
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Green Scalable High Performance Supercomputing Fabrizio Magugliani EMEA
Business Development Director Sicortex As CPU speeds have reached a point where
simply increasing the clock |
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The Godson-3 multi-core CPU and its application in High Performance
Computers Weiwu Hu, Xianggao,
Yunji Chen Institute
of Computing Technology, Chinese Academy of Sciences Godson-3
is a multi-core processor based on the 64-bit superscalar Godson-2 CPU core.
It takes a scalable CMP architecture in which processors and global addressed
L2 cache modules are connected in a distributed way and coherence of multiple
L1 copies of the same L2 block is maintained with a directory-based cache
coherence protocol. The
Godson-2 CPU core is a four-issue, out-of-order execution CPU which runs the
MIPS64 instruction set. The latest Godson Godson-3
adopts two-dimension mesh topology. Each node in the mesh include an 8*8
crossbar which connects four processor cores, four shared L2-cache banks and
four adjacent nodes in the East, South, West and North. A 2*2 mesh network
can connect a 16-core processor, and a 4*4 mesh network can connect a 64-core
processor. The distributed on-chip L2 cache modules are globally addressed.
Each cache block of L1 cache has a fixed L2 cache home node in which the
cache directory is maintained by directory-based cache coherence protocol.
Each node has one (or more) DDR2 memory controller. IO controllers are
connected through free crossbar ports of boundary nodes. Based on
the Godson-3 architecture, several product chips are defined and will be
physically implemented. The 4-core Godson-3 chip is designed and fabricated
based on 65nm STMicro CMOS technology. It includes
one 4-core node, 4MB L2 cache, two DDR2/3 ports, two HT1.0 ports, two PCIE
ports, one PCI port and one LPC port. It will be taped out in first half of
2008. One
important application of Godson-3 is the low cost high performance computers
(HPC). Based on Godson-3, the design of one national PetaFLOPS
HPC and one personal TeraFLOPs HPC are planed. This
presentation will introduces the HPC plans based on the Godson-3 multi-core
processor. |
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Aggressively
Threaded Systems: A Wise Choice for HPC Rick Hetherington Niagara
technology, in its infancy, targeted the commercial computing market. These
throughput workloads were not very computationally intensive but demanded
memory subsystems that provided high bandwidth and high capacity. The
second and third generations of NIagara added
greatly increased computation capability to the processing cores while continuing
to focus on high throughput. The
result is a set of products that efficiently deliver high levels of
computational throughput. This talk
will discuss the UltraSparc T2 and T2+ processor
designs as well as an analysis of their behavior
while executing 'technical' workloads. |
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Managing resources dynamically in SVP – from many-core to Grid Chris Jesshope Professor
of Computer Systems Architecture Our
computer systems are becoming pervasive and ubiquitous. It is now |
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Nondeterministic Coordination
using S-Net Prof
Alex Shafarenko Department
of Computer Science Coordination languages have been used for many years
in order to separate computation and concurrency/communication, that is
coordination, concerns. Despite that, a typical coordination language
intrudes into the computational part of the code even though it provides
some abstract projection of those distributed computing realities. As a
result, units of an application program become barely readable in isolation,
without having the "big picture" in mind --- and that big picture
in turn is overburdened with interface details. We believe that the reason why coordination has
these problems is that true separation between computation and concurrency
concerns is only possible using a nondeterministic glue. Indeed deterministic
coordination abstracts application code as a state-transition system,
introducing synchonization over and above the inimum needed for correct functioning of the application
code. Nondeterministic coordination, which we describe in this paper,
leans towards loose, data-flow-style composition using asynchronous
computational structures --- and synchronisers where necessary to ensure
that the correct data sets are worked on by fully encapsulated
application code units. The paper will present a coordination language
S-Net, developed and implemented by the authors. The language is very compact, only using 4 combinators acting on user-defined boxes to create
hierarchical networks of asynchronously communicating components. The boxes
are written in a conventional language and use a conventional stream
interface for output, while the input comes as a standard parameter list. We expect ordinary engineers to be able to provide
these components. There is only one special box which the user cannot
create and which comes with the S-Net language: the synchrocell.
The significant expressive power of coordination in such a small language
is achieved by using a sophisticated type system with subtyping, which influences the network
"wiring" provided by the combinators.
The coordination program is thus a large algebraic formula using the combinators, or several such formulae, and it is
written by a concurrency engineer who needs no detailed knowledge of the application
domain. Concurrency and self-adaptivity
of S-Net is helped by the fact that user-defined boxes are assumed to be without
persistent state, i.e. after the output stream has been flushed
and the box terminates, all local state is destroyed, so that the
next invocation of the box can take place at a different location in
the distributed system. Synchrocells
retain their state between invocations but they do not perform
computations and consequently consume no computing power. In conclusion, we will briefly dwell on the recent
success in applying S-Net to a signal processing problem in radar systems
industry at Thales Research & Technology,
France. |
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Fault Tolerance for PetaScale Systems: Current Knowledge, Challenges and
Opportunities Franck Cappello INRIA The
emergence of PetaScale systems reinvigorates the
community interest about how to manage failures in such systems and ensure
that large applications successfully complete. Existing results for several
key mechanisms associated with fault tolerance in HPC platforms will be
presented during this talk. Most of
these key mechanisms come from the distributed system theory. Over the
last decade, they have received a lot of attention from the community and
there is probably little to gain by trying to optimize them again. We will describe
some of the latest findings in this domain. Unfortunately,
despite their high degree of optimization, existing approaches do not fit
well with the challenging evolutions of large scale systems. There is room
and even a need for new approaches. Opportunities may come from different
origins like adding hardware dedicated to fault tolerance or relaxing some of
the constraints inherited from the pure distributed system theory. We will
sketch some of these opportunities and their associated limitations. |
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Ultra Low Power HPC --- scaling
supercomputing by three orders of Satoshi
Matsuoka Tokyo
Institute of Technology Low power
supercomputing as represented by various power efficient architectures such
as IBM BlueGene and power aware methods are
starting to receive considerable attention in the light of global agenda to
reduce energy consumption and also to alleviate increasing heat density
problems. Our new project, Ultra Low-Power HPC, greatly extend this horizon
by taking the innovative approaches to fundamentally slash energy consumption
of supercomputing by up to 3 orders of magnitude in 10 years. This is
achieved by the comprehensive use of new energy-efficient hardware devices
and power-saving algorithms that are modeled and
optimized in a systemwide fashion. Early results
from the project are exhibiting good results in achieving 10-100 times energy
efficiency, mostly by the use of acceleration and new memory device technologies. |
|
HPC Interconnection Networks – The
Key to Exascale Computing Jeffrey Vetter Oak Ridge National Laboratory and
Georgia Institute of Technology Interconnection
networks play a critical role in the design of next generation HPC
architectures and the performance of important applications. Despite the
significance of interconnects, current trends in HPC interconnects do not
appear to fulfill the requirements for next generation multi-petaflop and exaflop systems.
Application requirements drive networks with high bandwidth, low latency, and
high message rate, while practical constraints, such as signaling, packaging,
and cost, limit improvements in hardware bandwidth and latencies. To address these challenges, Sandia and Oak Ridge National Laboratories have
established the Institute for Advanced Architectures and Algorithms (IAA). In
this talk, I will present some of the challenges and potential solutions for exa-scale interconnection networks, which are being
considered by IAA. |
|
Deployment Experiences,
Performance Observations, and Early Science Results on Ranger John (Jay) R. Boisseau, Ph.D. Director, Texas Advanced Computing
Center The University of Texas at The Texas
Advanced Computing Center (TACC) at The University
of |
|
Preparing for the Sustained Petascale System Robert Pennington, Urbana, IL, U.S.A. The NCSA
Blue Waters system will be installed at the |
|
The Impact of Petacomputing
on Models and Theories Thomas Lippert John von Neumann-Institute for Computing (NIC) FZ In 2008,
supercomputers have reached the Petaflop/s
performance level. Machines likes the IBM Blue Gene/P, the Los Alamos
Roadrunner or the IBM Ranger at TACC achieve their unprecedented power using O(100.000) cores. In my talk I will, on the one hand,
discuss the question if we have arrived at the limits of scalability – I will
present first scalability results from the Jülich
Blue Gene/P system with 64k cores –, and, on the other hand, argue how Petacomputers with hundreds of thousands of processors
might transform science itself. |
|
Scalable Middleware for Large Scale Systems Barton P. Miller Computer Sciences Department I will discuss the problem of developing tools for large scale parallel
environments. We are especially interested in systems, both leadership class
parallel computers and clusters that have 10,000's or even millions of
processors. The infrastructure that we have developed to address this problem
is called MRNet, the Multicast/Reduction Network. MRNet's approach to scale is to structure control and
data flow in a tree-based overlay network (TBON) that allows for efficient
request distribution and flexible data reductions. The second part of this talk will present an overview of the MRNet design, architecture, and computational model and
then discuss several of the applications of MRNet. The applications include scalable automated
performance analysis in Paradyn, a vision
clustering application and, most recently, an effort to develop our first petascale tool, STAT, a scalable stack trace analyzer
running currently on 100,000's of processors. I will conclude with a brief description of a new fault tolerance
design that leverages natural redundancies in the tree structure to provide
recovery without checkpoints or message logging. |
|
Old problems never die – managing
the multi-programming mix Miron Livny Computer Sciences Department University of Wisconsin – Old
problems never die; they just fade away as technologies and tradeoffs
change. As the state of the art in
hardware and applications evolves further, they resurface. When virtual memory was introduced almost
50 years ago, computer systems had to find a way to prevent thrashing by
controlling the number and properties of the applications allowed to share
their physical memory. The recent proliferation of multi-core processors,
usage of virtual machines and deployment of complex I/O sub-systems require
the development of similar capabilities to control and manage at several
scales the mix of applications that share the compute and storage resources
of today’s systems. |
|
Active Data: Blurring the
distinction between data and computation Tim Ho and David Abramson Clayton, The amount
of data being captured, generated, replicated and archived |
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Using
Peer-to-Peer Dynamic Querying in Grid Information Services Domenico Talia and Paolo Trunfio DEIS, Dynamic
querying (DQ) is a technique adopted in unstructured Peer-to-Peer (P2P)
networks to minimize the number of nodes that is necessary to visit to obtain
the desired number of results. In this talk we describe the use of the DQ
technique over a distributed hash table (DHT) to implement a scalable Grid
information service. The DQ-DHT (dynamic querying over a distributed hash
table) algorithm has been designed to perform DQ-like searches over DHT-based
networks. The aim of DQ-DHT is two-fold: allowing arbitrary queries to be
performed in structured P2P networks, and providing dynamic adaptation of
search according to the popularity of resources to be located. Through
the use of the DQ-DHT technique it is possible to implement a scalable Grid
information service supporting both structured search and execution of
arbitraries queries for searching Grid resources on the basis of complex
criteria or semantic features. |
|
Algorithms and scheduling techniques for
clusters and grids Yves
Robert Ecole
Normale Supérieure de Lyon, France In this talk we provide several
examples to |
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Feedback
control for efficient autonomic solutions on the Grid Rizos Sakellariou This talk
will consider different approaches for |
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Accidentally Using Grid Services Charlie
Catlett Maths
and Computer Science Division Argonne
National Laboratory Argonne,
IL and Though
the term "grid" has fallen from the front page headlines, there is an
extremely active market of "grid services" - based on web services
and other standards - emerging. The web originally empowered Internet
users to create services and products with very little infrastructure, and
signs of success a decade ago included server meltdown from high demand.
Today one need not own any infrastructure at all to launch a new
service or product, and the combination of virtual and web services offers
not only near unlimited scaling but also reliability. This talk will focus
on a number of examples of new services, illustrating that at least one
measure of success is not only "ease of use" but "accidental
use" of transparent, but foundational, services. |
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From Grid Computing to Cloud Computing The evolution of the Grid Marketplace Avner Algom The
Israeli Association of Grid Technologies Over the last few years we have seen grid
computing evolve from a niche technology associated with scientific and technical
computing, into a business-innovating technology that is driving increased
commercial adoption. Grid deployments accelerate application performance,
improve productivity and collaboration, and optimize the resiliency of the IT
infrastructure. Today, the maturity of the Virtualization
technologies, both at the VM and at the IT infrastructure levels, and the
convergence of the Grid, Virtualization and SOA concepts, enables the
business implementation of the Cloud Computing for utility and SaaS services. At last, the Grid Computing vision becomes a
reality: people that get electricity from their electrical outlet, on-demand,
can get applications, computing and storage services from the network,
on-demand. We can dynamically scale our computation and storage power, at no
time, and we pay only for what we use. This is going to change the marketplace as we know
it. |
|
Cloud
Computing for on-Demand Resource Provisioning Distributed Systems
Architecture Group Universidad
Complutense de Madrid Madrid, Spain The aim of the presentation is to show the
benefits of the separation of resource provisioning from job execution
management in different deployment scenarios. Within an organization, the
incorporation of a new virtualization layer under existing Cluster and HPC
middleware stacks decouples the execution of the computing services from the
physical infrastructure. The dynamic execution of working nodes, on virtual
resources supported by virtual machine managers such as the OpenNEbula Virtual Infrastructure Engine, provides
multiple benefits, such as cluster consolidation, cluster partitioning and
heterogeneous workload execution. When the computing platform is part of a Grid
Infrastructure, this approach additionally provides generic execution
support, allowing Grid sites to dynamically adapt to changing VO demands, so
overcoming many of the obstacles for Grid adoption. The previous scenario can
be modified so the computing services are executed on a remote virtual
infrastructure. This is the resource provision paradigm implemented by some
commercial and scientific infrastructure Cloud Computing solutions, such as Globus VWS or Amazon EC2, which provide remote interfaces
for control and monitoring of virtual resources. In this way a computing
platform could scale out using resources provided on-demand by a provider, so
supplementing local physical computing services to satisfy peak or unusual
demands. Cloud interfaces can also provide support for the federation of
virtualization infrastructures, so allowing virtual machine managers to
access resources from remote resources providers or Cloud systems in order to
meet fluctuating demands. The OpenNEbula Virtual
Infrastructure Engine is being enhanced to access on-demand resources from
EC2 and Globus-based clouds. This scenario is being
studied in the context of the RESERVOIR– Resources and Services
Virtualization without Barriers — EU-funded initiative. |
|
Network resource reservation and
virtualization for grid applications Marcelo Pasin INRIA, École Normale
Supérieure de Lyon Laboratoire de
l’informatique du parallélisme Lyon, France The
coordination of grid resource allocation often needs a service to
|
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Parallel Data Mining from Multicore to Cloudy Grids Geoffrey Fox We
describe a suite of data mining tools that cover clustering, Gaussian modeling and dimensional reduction and embedding. These
are applied to three class of applications; Geographical information systems,
cheminformatics and bioinformatics. The data vary in dimension from low (2),
high (thousands) to undefined (sequences with dissimilarities but not vectors
defined). We use deterministic annealing to provide more robust algorithms
that are relatively insensitive to local minima. We use embedding algorithms
both to associate vectors with sequences and to map high dimensional data to
low dimensions for visualization. We discuss the algorithm structure and
their mapping to parallel architectures of different types and look at the
performance of the algorithms on three classes of system; multicore,
cluster and Grid using a MapReduce style algorithm.
Each approach is suitable in different application scenarios. |
|
Summary-based Distributed Semantic Database for Resource and Service
Discovery André Höing Electrical Engineering and
Computing Science Technical University of Today's
RDF triple stores that are based on distributed hash tables (DHTs) distribute the knowledge of all participating peers
in the P2P network. They use hash values of the subject, predicate, and
object of each triple in order to identify three nodes in the network that
shall store a copy of the triple. Query processors collect relevant triples
by identifying responsible nodes using the hash values of literals and
constants occurring in the query. |
|
UNICORE 6 – A European Grid Technology Achim Streit Jülich Supercomputing
Centre (JSC) at Forschungszentrum The
development of UNICORE started back in 1997 with two projects funded by the
German ministry of education and research (BMBF). UNICORE is a vertically
integrated Grid middleware, which provides a seamless, secure, and intuitive
access to distributed resources and data and provides components on all
levels of a Grid architecture from an easy-to-use graphical client down to
the interfaces to the Grid resources. Furthermore, UNICORE has a strong
support for workflows while security is established through X.509
certificates. Since 2002 UNICORE is continuously improved to mature production
ready quality and enhanced with more functionalities in several European
projects. Today UNICORE is used in several national and international Grid
infrastructures like D-Grid and DEISA and is also providing access to the
national Supercomputer of the NIC in Germany. The talk
will give details about the new version of UNICORE 6, which is web-services
enabled, OGSA-based and standards-compliant. To begin with the underlying
design principles and concepts of UNICORE are presented. A detailed
architecture diagram shows the different components of UNICORE 6 and its
interdependencies with a special focus on workflows. This is followed by a
view on the adoption of common open standards in UNICORE 6, which allows
interoperability with other Grid technologies and a realisation of an open
and extensible architecture. The talk closes with some interesting use case
examples, where the UNICORE Grid technology is used. The
European UNICORE Grid Middleware is available as Open Source from http://www.unicore.eu. |
|
e-Science Applications on Grids -
The DEISA Success Story Wolfgang Gentzsch DEISA Distributed European
Infrastructure for Supercomputing Applications and We will
present selected compute and data intensive applications which Bio: Wolfgang Gentzsch DEISA, Duke University Wolfgang Gentzsch is
Dissemination Advisor for the DEISA Distributed European Initiative for
Supercomputing Applications. He is adjunct professor of computer science at
Duke University in Durham, and visiting scientist at RENCI Renaissance
Computing Institute at UNC Chapel Hill, both in North Carolina. From 2005 to
2007, he was the Chairman of the German D-Grid Initiative. Recently, he was
Vice Chair of the e-Infrastructure Reflection Group e-IRG; Area Director of
Major Grid Projects of the OGF Open Grid Forum Steering Group; and he is a
member of the US President's Council of Advisors for Science and Technology
(PCAST-NIT). Before, he was Managing Director of MCNC Grid and Data Center
Services in North Carolina; Sun's Senior Director of Grid Computing in Menlo
Park, CA; President, CEO, and CTO of start-up companies Genias
and Gridware, and professor of mathematics and
computer science at the University of Applied Sciences in Regensburg,
Germany. Wolfgang Gentzsch studied mathematics and
physics at the Technical Universities in Aachen and
Darmstadt, Germany. |
|
Superlink-online - delivering the power of GPUs,
clusters and opportunistic grids to geneticists M. Silberstein Technion-Israel Institute of Technology Haifa, Israel Genetic linkage analysis is a statistical tool used
by geneticists for mapping disease-susceptibility genes in the study of genetic
diseases. The analysis is based on the exact inference in very large
probabilistic (Bayesian) networks, which is often computationally hard
(ranging from seconds to years on a single CPU). We
present a distributed system for faster analysis of genetic data, called Superlink-online. The system achieves high performance
through parallel execution of linkage analysis tasks over thousands of
computational resources residing in multiple opportunistic computing
environments, aka Grids. It utilizes the resources
in many available grids, unifying thousands CPUs over campus grids in the Technion and the University of Wisconsin in Madison,
EGEE, Open Science Grid and Community Computing Grid Superlink@Technion.
Notably,
the system is available online, which allows geneticists to perform
computationally intensive analyses with no need for either While the grids potentially provide enormous amount
of computing power, we also explore an alternative approach of using Graphics
Processing Units (GPUs) to accelerate the genetic
linkage computations. We achieve up to two orders of magnitude speedups on
average, and up to three order of magnitude speedups on some particularly
complex problem instances versus the optimized application performance on a
single CPU. The use of GPUs is particularly
appealing in the context of Community Grids, considering the number of
high performance GPUs available worldwide. |
|
Building Collaborative Applications for System-Level Science Marian Bubak
Institute of Computer Science AGH,
al. Mickiewicza 30, 30-059 ACC CYFRONET AGH, Krakow, ul. Nawojki 11, 30-950 A novel
approach to scientific investigations, besides analysis of individual phenomena,
integrates different, interdisciplinary sources of knowledge about a complex
system to obtain an understanding of the system as a whole. This innovative
way of research has recently been called system-level science [1]. Problem-solving
environments and virtual laboratories have been the subject of research and
development for many years [2]. Most of them are built on top of workflow
systems [3]. Their main drawbacks include limited expressiveness of the
programming model and lack of mechanisms for integration of computing
resources from grids, clusters and dedicated computers. The ViroLab project [4] is developing a virtual laboratory
[5] for research of infectious diseases to facilitate medical knowledge
discovery and provide decision support for HIV drug resistance [6], and this
virtual laboratory may be useful in other
areas of system-level science. To
overcome the limitations of the programming methods, we have defined an
experiment plan notation based on a high-level scripting language - Ruby. For easy interfacing of
different technologies, we have introduced a grid object abstraction level
hierarchy [7]. Each grid object class is an abstract entity which defines the
operations that can be invoked from the script, each class may have multiple
implementations, representing the same functionality; and an implementation
may have multiple instances,running on different
resources [8]. The
Experiment Planning Environment is an Eclipse-based tool supporting rapid
experiment plan development while Experiment Management Interface enables
loading and execution of experiments. The Experiment Repository stores
experiment plans prepared by developers and published for future usage, and
the laboratory database holds the obtained results.To
enable high-level programming, the virtual laboratory engine, called the GridSpace, includes the Grid Operation Invoker which
instantiates grid object representatives and handles remote operation
invocations. The GridSpace Application Optimizer is
responsible for optimal load balancing
on computational servers.The Data Access Service
acquires data from remote databases located in research institutions and
hospitals. To meet the specific requirements for exchanging biomedical
information within such a virtual environment, the solution introduced in DAS
bases on existing Grid technologies: Globus
Toolkit, OGSA-DAI, and Shibboleth. The provenance approach [9] in the ViroLab virtual laboratory brings together ontology-based
semantic modeling, monitoring of applications and
the runtime infrastructure, and database technologies, in order to collect
rich information concerning the execution of experiments, represent it in a
meaningful way, and store it in a scalable repository [10]. The
virtual laboratory has already been used to plan and execute a few virological experiments, with various types of analysis
of HIV virus genotypes such as calculation of drug resistance based on virus
genotype, querying historical and provenance information about experiments, a
drug resistance system based on the Retrogram set
of rules, data mining and classification with Weka
[5], and the molecular
dynamics NAMD application which has been installed on the CYFRONET EGEE site.
The
virtual laboratory provides an environment to collaboratively plan, develop
and use collaborative applications; it is dedicated for multi-expertise
task-oriented groups running complex computer simulations; its basic features
are: mechanisms for user-friendly experiment creation and execution,
possibility of reusing existing libraries,
tools etc., gathering and exposing provenance information, integration of
geographically-distributed data resources, access to WS, WSRF, MOCCA
components and jobs, secure access to data and applications. Acknowledgments
The
Virtual Laboratory is being developed at the Institute of Computer Science
and CYFRONET AGH, Gridwise Technologies, Universiteit van Amsterdam, and HLRS
Stuttgart in the framework of the EU IST ViroLab
and CoreGRID projects as
well as the related Polish SPUB-M and Foundation for Polish Science
grants. References [1] I. Foster and C. Kesselman:
Scaling System-Level Science: Scientific Exploration and IT Implications, IEEE Computer, vol. 39, no 11, 31-39,
2006 [2] K. Rycerz, M. Bubak, P.M.A. Sloot, V. Getov: Problem Solving Environment for Distributed
Interactive Simulations in: Sergiei Gorlatch,
Marian Bubak, and Thierry Priol
(Eds). Achievements in European Reseach on
Grid Systems. CoreGRID Integration Workshop 2006
(Selected Papers) ISBN-13: 978-0-387-72811-7; pp
55 - 66, Springer, 2008 [3] Y. Gil, E. Deelman, M. Ellisman, T. Fahringer, G. Fox, D. Gannon, C. Goble, M. Livny,
L. Moreau, and J. Myers. Examining the Challenges of Scientific Workflows. IEEE Computer vol 40, no 12 pp 24-32, 2007 [4] ViroLab - EU IST STREP Project 027446; www.virolab.org [5] ViroLab Virtual Laboratory, http://virolab.cyfronet.pl [6] P. M.A. Sloot, I. Altintas, M. Bubak, Ch.A. Boucher: From Molecule to Man: Decision
Support in Individualized E-Health, IEEE Computer vol. 39, no 11, 40-46,
2006 [7] T. Gubala, M. Bubak: GridSpace - Semantic
Programming Environment for the Grid, PPAM'2005, LNCS 3911,
172-179, 2006 [8] M. Malawski,
M. Bubak, M. Placek, D. Kurzyniec, V. Sunderam: Experiments with Distributed Component
Computing Across Grid Boundaries, Proc. HPC-GECO/CompFrame
Workshop - HPDC'2006, Paris, 2006 [9] D. de Roure,
N.R. Jennings, N. Shadbolt, The semantic grid: a future e-science infrastructure, Grid
Computing - Making the Global Infrastructure a
Reality, Wiley, 2003, pp. 437-470 [10] B. Balis, M. Bubak, and J. Wach: User-Oriented Querying over Repositories of Data and
Provenance, In
G. Fox, K. Chiu, and R. Buyya, editors, Third IEEE
International Conference on e-Science and Grid
Computing, e-Science 2007, Bangalore, India, 10-13 December 2007,
pages 77-84. IEEE Computer Society, 2007 |
|
A Performance
Based Distribution Algorithm for Grid Computing Heterogeneous Tasks Abderezak Touzene, Hussein AlMaqbali,
Ahmed AlKindi, Khaled Day Department
of Recently in
[1] we proposed a performance based load-balancing algorithm for independent
tasks, which require similar computing need in the sense that the tasks are
almost identical. This paper extends the work and proposes a load
distribution algorithm for independent tasks with different computing
requirements including short and long tasks. We assume a preprocessing
phase of prediction of the number of instruction (TNI) needed for each task
in the grid. Our load distribution algorithm takes into account both the CPU
speed of the computing units and the TNI of different tasks. We design a
simulation model using steady-state, based on NS2 to study the performance of
our load distribution algorithm. Keywords: grid
computing, load-balancing, steady-state, resource management, performance
evaluation, simulation models. |
|
DMOVER: Scheduled Data
Transfer for HPC Grid Workflows Derek Simmel TeraGrid
users have expressed a need for better tools to schedule and |
|
Grid Computing or the Internet of services? Opportunities and perspectives from research to business Antonio Congiusta NICE-ITALY, Cortanze, Asti, Italy Experience has shown that solutions to
better enable organizations to take advantage of the benefits of Grid
computing, are based on clear identification of the requirements and the
application of the best available standardized and reliable technologies. Relevant examples of such principle with
related best practices can be extracted from some of the success stories that
recently have involved EnginFrame in the Oil &
Gas industry, the Energy and Automotive sectors, HPC support from
collaboration facilities to infrastructure provision and management, and also
some fruitful cooperations with strategical
partners. In particular, beyond to well
established HPC activities within a primary European consortium for providing
a production quality infrastructure, a new trend has been undertaken towards
the integration of collaboration facilities to HPC environments. Quite
interesting are also the activities devoted to enable for workflow management
and distributed visualization, some of which are part of European-wide
research projects. From all such experiences we can
envision as future of the Grid an always strong evolution towards
interoperable key services, within a scenario in which comprehensive and
all-inclusive software is ever less important. In such a scenario, a key role
is played by integration technologies capable of homogenizing and enforcing
service interactions and access. |
|
e-Research
& Grid computing in David Abramson Clayton, Over the
past few years the Australian government has performed a major review of
its research infrastructure needs, from hard technological areas to the
social sciences. Along with this review, they have investigated the
electronic platforms required to support these
various disciplines. What has evolved is an grid
computing strategy called "Platforms for Collaboration" that
addresses computation, networking and data management. In addition to
this, various computer science groups are developing grid technologies
that underpin this platform. In this talk I will give an over of
the Australian e-Research agenda and highlight a few major research activities in grid computing. |
|
Kihyeon Cho
e-Science Division Korea Institute of Science and Technology Information Daejeon, 305-806, For Grid and e-Science in Korea we had been focused on the research of
Grid technology and Grid infrastructure till 2006. Since 2007, we have been
in the stage of enabling science on the cyber infrastructure for four major
sciences such as e-Life Science, e-Physics, e-Engineering, and e-Geo Science. In order to support these application areas we also
work on scientific workflow technology and WSRF (Web Service Resource
Framework) based Grid service technology. We also provide researcher
collaborations with both infrastructure and technology. In this talk, we will present the current status of
Grid and e-Science projects and activities in Korea. |
|
Atul Gurtu Tata Institute of Fundamental Research Grid technology has changed the way advanced
research is being conducted today. In India too, the main driver for
introduction of GRID activity has been participation in High Energy Physics (HEP)
experiments at the LHC. To extract meaningful physics results within a
reasonable span of time from peta-bytes of data of
unprecedented complexity and to include effective participation of
collaborating institutions spread out world wide, the only way was to develop
GRID based distributed computing. Integration of various distributed
environments in different administrative domains having varied security
policies poses new challenges in data security & data sharing. The
required computational performance is supported through world wide LHC
Computing Grid (WLCG). The progress and status of setting up WLCG networking
and computing in India will be described with main emphasis on the High
Energy Physics related activity. Status of Tier-2 centers for participation
in the ALICE and CMS experiments at the LHC at CERN will be presented. The
role of the EU-India GRID project in developing Grid based solutions in other
areas of science such as earth sciences, bio, condensed matter physics etc.
will be mentioned as also some domestic GRID initiatives within the country. |
|
National Grid Initiative of Anatoly Sachenko American-Ukrainian School of
Computer Science Department of Information
Computing Systems and Control Ternopil State Economic University Uniting of
the existing Grid segments and supercomputer centers in scientific and
educational areas into joint
Ukrainian National Grid Initiative(UNGI) and the issues of UNGI integration
into the European Grid infrastructure are considered in this paper. The
peculiarities of Grid segment at
National Academy of Science as well as the UGrid
Project of Ministry of Education and Science are described too. It’s stressed
on the joint project UNGI for EGI and other integration possibilities within
INTAS, NATO and Frame 7 programs. Finally an advanced approach for security
strengthening in Grid-systems is
proposed. |
|
Per Öster CSC – Finnish IT Center for Science The European Grid Initiative (EGI) has as goal to
ensure a long-term sustainability of grid infrastructures in Europe. This is to be done through establishment of
a new federated model bringing together National Grid Infrastructures to
build the EGI Organisation. For this purpose, the
European Commission has funded a specific project, the EGI Design Study
(EGI_DS), to in 27 months make the conceptual setup and operation of a new organisational model of a sustainable pan-European grid
infrastructure. The goal of the EGI Design Study (EGI_DS) is to evaluate use
cases for the applicability of a coordinated effort, to identify processes
and mechanisms for establishing EGI, to define the structure of a
corresponding body, and ultimately to initiate the construction of the EGI Organisation. The project started in September 2007 and a
very important milestone is the Blueprint of the EGI Organisation.
In this talk the EGI Blueprint and feedback from the NGIs
will be presented together with a discussion of the role of EGI in the
European “HPC Ecosystem”. |
|
Building Shared High Performance Computing
Infrastructure for the Biomedical Sciences Marcos Athanasoulis,
Dr.PH, MPH In recent years high
performance computing has moved from the sidelines to the mainstream of
biomedical research. Increasingly researchers are employing
computational methods to facilitate their wet lab research. Some
emerging laboratories and approaches are based on a 100% computational ramework. While there are many lessons to be
learned from the computational infrastructure put into place for the physical
and mechanical sciences, the character, nature and demands of biomedical
computing differ from the needs of the other sciences. Biomedical
computational problems, for example, tend to be less computationally
intensive but more “bursty” in their needs.
This creates both an opportunity (it is easier to meet capacity needs) and a
challenge (job scheduling rules are more complicated to accommodate the
bursts). Harvard Medical School provides one of the most advanced shared high
performance research computing centers at an academic medical center. In
2007, Harvard convened the first Biomedical High Performance Computing
Leadership Summit to explore the issues in creating shared computing
infrastructure for the biomedical sciences. We brought together over
100 leaders in the field to exchange ideas and approaches. Through
special sessions and direct participant surveys a number of themes emerged
around best practices in deploying shared computational infrastructure for
the biomedical sciences. Based on prior experience and the summit
findings, we summarizes obstacle and opportunities facing those who wish to
provide biomedical oriented high performance computing infrastructure.
We will provide quantitative results about HPC Biomedical Implementations. We
will also provide some examples of current problems in biomedical
computations including whole genome processing, massively parallel
correlation analysis and natural language processing of clinical notes. |
|
ViroLab: Distributed
Decision Support in a virtual laboratory for infectious diseases P. Sloot In future
years, genetic information is expected to become increasingly significant in many
areas of medicine. This expectation comes from the recent and anticipated
achievements in genomics, which provide an unparalleled opportunity to
advance the understanding of the role of genetic factors in human health and
disease, to allow more precise definition of the non-genetic factors
involved, and to apply this |
|
Processing of Large-Scale Biomedical Images on a Cluster of Multi-Core
CPUs and GPUs Umit Catalyurek Department of Biomedical
Informatics The As
microprocessor manufacturers strain to continue to increase performance,
multi-core chips are quickly becoming the norm. The demand in computer gaming
industry also brought us GPUs as an alternative
fast, general purpose, streaming co-processors. Commodity
GPUs and multi-core CPUs bring together an
unprecedented combination
of high performance at low cost, and provide an ideal environment for biomedical
image analysis applications. In this
talk we will present our ongoing efforts on developing optimized biomedical
image analysis kernels for heterogeneous multi-core CPUs and GPUs. We will also present how a cooperative cluster of
multi-Core CPUs and GPUs can be efficiently used
for large scale biomedical image analysis. |
|
Grid Computing for Financial Applications M. Al-Baali§, P. Beraldi*,
L. Grandinetti*,
G. Aloisio^ I. Epicoco^,
A. Violi**, C. Figŕ Talamancaç § Dept. of Mathematics and
Statistics, Sultan Qaboos University, * Department of Electronics,
Informatics and Systems, * * CESIC - ^ ç Innova
spa In recent
years financial operators have shown an increasing interest in quantitative
tools able to efficiently measure, control and manage risk. Such an interest
is motivated by the necessity to operate in a very competitive and volatile
environment with a high level of
complexity increased by the
globalization of the economic activities and the continuous
introduction of innovative financial products. The complexity of the problems
to deal with and the necessity to operate in real time has highlighted the
serious computational constraints imposed by conventional numerical
platforms, prompting the need to take advantage of high performance computing
systems. In this
talk we present a prototypal system designed to support financial operators
in investment decisions concerning the strategic asset allocation
problem. The system has been designed
and tested within the European Project BEINGRID. At the
core of the system is the formulation of sophisticated optimization models
able to capture with an increasing level of realism with respect to
traditional approaches, the specific features of the applicative problem.
Moreover, the system is based on the integration of advanced scenario
generation procedures and efficient methods to solve the resulting huge sized
problems. The
system has been deployed on the SPACI grid infrastructure. In particular, an
user – friendly web grid environment
has been realized by using the GRB technology for the resource management and
the GRelC services for distributed data. |
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Data Issues in a challenging HPC
application to Climate Change Giovanni Aloisio University of Salento Lecce, Italy Earth
Science is strongly becoming a data intensive and oriented activity. Petabytes of data, big collections, huge datasets are
continuously produced, managed and stored as well as accessed, transferred
and analyzed by several scientists and researchers at multiple sites. From
the data grid perspective, a key element to search, discover, manage and
access huge amount of data stored within distributed storages is the related
data and metadata framework. A new supercomputing centre, the
Euro-Mediterranean Centre for Climate Change (CMCC), was recently created by
the Italian Government to support research on Climate Change. The SPACI
Consortium, one of the main CMCC Associate Centres, provides know-how and
expertise on High Performance and Grid Computing. The GRelC
Middleware (provided by SPACI Consortium) has been recently adopted as part
of the CMCC Data Grid framework in order to provide a secure, transparent and
scalable grid enabled metadata management solution. We
present the CMCC initiative, the supercomputing facility as well as data grid
architectural and infrastructural issues concerning the adopted grid
data/metadata handling systems. |
|
Tim David Centre for Bioengineering
University of Canterbury “A
Heterogeneous Computing Model for a Grand Challenge Problem” |
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The e-Science for High Energy Physics Kihyeon Cho,
Ph.D. KISTI (Korea Institute of Science
and Technology Information) The
e-Science for High Energy Physics is to study High Energy Physics (HEP) any
time and anywhere even if we are not on-site of accelerator laboratories. The
components are 1) data production, 2) data processing and 3) data analysis
any time and anywhere. The data production is to do remote control and take
shifts remotely. The data processing is to run jobs anytime, anywhere using
Grid farms. The data analysis is to work together to publish papers using
collaborative environment. We apply this concept to LHC experiment at CERN
and Tevatron experiment at Fermilab.
It this talk we will present the current status and embodiment of the idea. |
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A HPC infrastructure at the service of Scientific Research in Italy Giovanni
Erbacci CINECA -
System and Technology Deparment CINECA Inter-University Consortium,
Casalecchio di Reno, Italy State of the art HPC infrastructures are fundamental
to support scientific research and to advance science at European level.
Since many years, at Italian level, CINECA has been able to assure to the
scientific community a competitive
advantage by putting into timely production advanced HPC systems that have proven very wide applicability
and success. The CINECA HPC infrastructure de facto represents
the national facility for supercomputing and the CINECA HPC systems are part of the Italian research
Infrastructures system, integrated by means of the Italian academic and
research network facility (GARR). In this work we present the CINECA HPC
infrastructure, its evolution, and the
service model. Moreover, we outline
the CINECA role in the context of the
main HPC Infrastructure projects, operating
at the European level: DEISA, PRACE and HPC-Europa. DEISA is a consortium between the most advanced HPC centres in Europe, whose
aim is to deploy and operate a persistent, production quality, distributed
supercomputing environment with continental scope. This infrastructure is mainly intended to support
challenge scientific applications by integrating and making easily accessible
supercomputers in different centres. PRACE is a feasibility project intended to build
the next generation of challenge HPC infrastructure and services at European level. The infrastructure will
consist of a limited number (3 to 5) of
PetaFlop/s class HPC systems integrated in a
network of HPC systems on a pyramidal model basis, with three different
layers (European, National and
Regional) in the European HPC
eco-system. HPC-Europa supports the
human network of knowledge, experiences and expertise exchange, in the
context of the scientific research communities using advanced HPC systems. HPC-Europa factively promotes such mission supporting the mobility
of the European researchers among the main research institutions, and providing the access to the computational
resources offered by the main European HPC infrastructures. |
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