How local, regional, and national cyberinfrastructure can be coordinated and linked to advance science and engineering, based on experiences and lessons from the Center for Computation & Technology at LSU (ideas, funding, implementation), plus some thoughts on what might be done differently if we were starting today. Presented at First Workshop - Center for Computational Engineering & Sciences, Unicamp, Campinas, Brazil 10 APR 2014
Rethinking how we provide science IT in an era of massive data but modest bud...Ian Foster
A talk given in January 2012 at a wonderful conference organized in Zakopane, Poland, by colleagues from the erstwhile GridLab project. I talked about how increasing data volumes demand radically new approaches to delivering research computing. Lively discussion ensued.
Mapping e-science, e-social science, and e-research landscape using Webometrics
박한우
영남대학교 언론정보학과 교수
미국 뉴욕주립대 박사
WCU 웹보메트릭스 연구단 사업단장
hanpark@ynu.ac.kr
http://www.hanpark.net
http://english-webometrics.yu.ac.kr
Web Observatories, e-Research and the Importance of Collaboration. WST 2014 Webinar series, 20th March 2014
See Web Science Trust http://webscience.org/
Keynote talk for NCRM Stream Analytics workshop, 19 January 2017, Manchester.
My talk is called "New and Emerging Forms of Data: Past, Present, and Future” and I will be giving a perspective from my role as one of the ESRC Strategic Advisers for Data Resources, in which I was responsible for new and emerging forms of data and realtime analytics. The talk also includes some of the current work in the Oxford e-Research Centre on Social Machines (the SOCIAM project) and an introduction to the PETRAS Internet of Things project.
The talk raises a number of important issues looking ahead, including massive scale of data that is already being supplied by Internet of Things, the implications of automation in our research, reproducibility and confidence in research results. I will also ask, how can the new forms of data and new research methods enable social scientists to work in new ways, and can we move on from the dependence on the traditional investment in longitudinal studies?
Rethinking how we provide science IT in an era of massive data but modest bud...Ian Foster
A talk given in January 2012 at a wonderful conference organized in Zakopane, Poland, by colleagues from the erstwhile GridLab project. I talked about how increasing data volumes demand radically new approaches to delivering research computing. Lively discussion ensued.
Mapping e-science, e-social science, and e-research landscape using Webometrics
박한우
영남대학교 언론정보학과 교수
미국 뉴욕주립대 박사
WCU 웹보메트릭스 연구단 사업단장
hanpark@ynu.ac.kr
http://www.hanpark.net
http://english-webometrics.yu.ac.kr
Web Observatories, e-Research and the Importance of Collaboration. WST 2014 Webinar series, 20th March 2014
See Web Science Trust http://webscience.org/
Keynote talk for NCRM Stream Analytics workshop, 19 January 2017, Manchester.
My talk is called "New and Emerging Forms of Data: Past, Present, and Future” and I will be giving a perspective from my role as one of the ESRC Strategic Advisers for Data Resources, in which I was responsible for new and emerging forms of data and realtime analytics. The talk also includes some of the current work in the Oxford e-Research Centre on Social Machines (the SOCIAM project) and an introduction to the PETRAS Internet of Things project.
The talk raises a number of important issues looking ahead, including massive scale of data that is already being supplied by Internet of Things, the implications of automation in our research, reproducibility and confidence in research results. I will also ask, how can the new forms of data and new research methods enable social scientists to work in new ways, and can we move on from the dependence on the traditional investment in longitudinal studies?
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
"'Tis true. There's magic in the Web: The Short and the Long of Co-Creation, Web Science, and Data Driven Innovation". Keynote for the DATA-DRIVEN INNOVATION WORKSHOP 2016 collocated with ACM Web Science 2016, Hannover, Germany, Sunday 22 May 2016
Developments in Education for Information: Will ‘Data’ Trigger the Next Wave ...Yasar Tonta
?” Paper presented at the International Conference on Information Management and Libraries (ICIML), November 10-13, University of the Punjab, Lahore, Pakistan.
US University Research Funding, Peer Reviews, and MetricsDaniel S. Katz
My part of the "Digital Science Webinar: Articulating Research Impact – Strategies from Around the Globe" (http://www.digital-science.com/events/digital-science-webinar-articulating-research-impact-strategies-from-around-the-globe/)
Daniel S. Katz will discuss how reviewers at the National Science Foundation (USA) consider the “intellectual merit” and “broader impacts” criteria for funding and in particular how metrics might help applicants understand their impacts in these areas.Dan will also talk about how reviewers might use qualitative and quantitative altmetrics data to inform their peer reviews for grant applications. He will address many of the salient questions around this use of metrics, for example, do reviewers take metrics seriously and what types of metrics are of most value to them?
Discussing Software Citation and related topics at Workshop on Data and Software Citation (June 6-7 at Harvard Medical School, http://www.software4data.com/#!nsf-workshop/jghgb)
Keynote talk at the 3rd International Conference on Supercomputing in Mexico: www.isum.mx. A great group of people!
A substantially revised version of a talk with the same title given on previous occasions.
A description of software as infrastructure at NSF, and how Apache projects may be similar. What lessons can be shared from one organization to the other? How does science software compare with more general software?
Cyberinfrastructure in Louisiana: From Black Holes to Hurricanes. Presentation at Cyberinfrastructure Days, Notre Dame, April 29-30, 2010. http://ci.nd.edu/
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
"'Tis true. There's magic in the Web: The Short and the Long of Co-Creation, Web Science, and Data Driven Innovation". Keynote for the DATA-DRIVEN INNOVATION WORKSHOP 2016 collocated with ACM Web Science 2016, Hannover, Germany, Sunday 22 May 2016
Developments in Education for Information: Will ‘Data’ Trigger the Next Wave ...Yasar Tonta
?” Paper presented at the International Conference on Information Management and Libraries (ICIML), November 10-13, University of the Punjab, Lahore, Pakistan.
US University Research Funding, Peer Reviews, and MetricsDaniel S. Katz
My part of the "Digital Science Webinar: Articulating Research Impact – Strategies from Around the Globe" (http://www.digital-science.com/events/digital-science-webinar-articulating-research-impact-strategies-from-around-the-globe/)
Daniel S. Katz will discuss how reviewers at the National Science Foundation (USA) consider the “intellectual merit” and “broader impacts” criteria for funding and in particular how metrics might help applicants understand their impacts in these areas.Dan will also talk about how reviewers might use qualitative and quantitative altmetrics data to inform their peer reviews for grant applications. He will address many of the salient questions around this use of metrics, for example, do reviewers take metrics seriously and what types of metrics are of most value to them?
Discussing Software Citation and related topics at Workshop on Data and Software Citation (June 6-7 at Harvard Medical School, http://www.software4data.com/#!nsf-workshop/jghgb)
Keynote talk at the 3rd International Conference on Supercomputing in Mexico: www.isum.mx. A great group of people!
A substantially revised version of a talk with the same title given on previous occasions.
A description of software as infrastructure at NSF, and how Apache projects may be similar. What lessons can be shared from one organization to the other? How does science software compare with more general software?
Cyberinfrastructure in Louisiana: From Black Holes to Hurricanes. Presentation at Cyberinfrastructure Days, Notre Dame, April 29-30, 2010. http://ci.nd.edu/
Understanding the Big Picture of e-ScienceAndrew Sallans
A. Sallans. "Understanding the Big Picture of e-Science." Presented at the 2011 eScience Bootcamp at the University of Virginia's Claude Moore Health Sciences Library. 4 March 2011
A National Big Data Cyberinfrastructure Supporting Computational Biomedical R...Larry Smarr
Invited Presentation
Symposium on Computational Biology and Bioinformatics:
Remembering John Wooley
National Institutes of Health
Bethesda, MD
July 29, 2016
Coupling Australia’s Researchers to the Global Innovation EconomyLarry Smarr
08.10.10
Fifth Lecture in the
Australian American Leadership Dialogue Scholar Tour
University of Queensland
Title: Coupling Australia’s Researchers to the Global Innovation Economy
Brisbane, Australia
High Performance Cyberinfrastructure to Support Data-Intensive Biomedical Res...Larry Smarr
08.06.16
Invited Talk
Association of University Research Parks BioParks 2008
"From Discovery to Innovation"
Salk Institute
Title: High Performance Cyberinfrastructure to Support Data-Intensive Biomedical Research Instruments
La Jolla, CA
GENI Engineering Conference -- Ian FosterIan Foster
I was invited to talk at the 18th GENI Engineering Conference (http://groups.geni.net/geni/wiki/GEC18Agenda) on experiences in the Grid community with creating and operating large shared infrastructures. I chose to focus on our experiences using Software as a Service (SaaS: aka Cloud) to reduce barriers to the use of the capabilities required to create and operate virtual organizations.
Calit2: An Experiment in Social NetworksLarry Smarr
06.08.16
Invited Talk
Conversation on Social Networks, Social Movements
Third Annual Seminar in Experimental Critical Theory
University of California Humanities Research Institute, UCI
Title: Calit2: An Experiment in Social Networks
Irvine, CA
About the Webinar
Big data is being collected at a rate that is surpassing traditional analytical methods due to the constantly expanding ways in which data can be created and mined. Faculty in all disciplines are increasingly creating and/or incorporating big data into their research and institutions are creating repositories and other tools to manage it all. There are many challenge to effectively manage and curate this data—challenges that are both similar and different to managing document archives. Libraries can and are assuming a key role in making this information more useful, visible, and accessible, such as creating taxonomies, designing metadata schemes, and systematizing retrieval methods.
Our panelists will talk about their experience with big data curation, best practices for research data management, and the tools used by libraries as they take on this evolving role.
Scott Edmunds slides for class 8 from the HKU Data Curation (module MLIM7350 from the Faculty of Education) course covering science data, medical data and ethics, and the FAIR data principles.
Similar to Advancing Science through Coordinated Cyberinfrastructure (20)
A talk presented to the US Networking and Information Technology Research and Development (NITRD) Program's High End Computing Interagency Working Group, 16 January 2020
(a slightly updated version of this talk is at https://doi.org/10.6084/m9.figshare.10301741.v1)
A talk on the role of software in research and how NCSA is responding in terms of people and roles - given at the 2019 Data Science Leadership Summit (https://sites.google.com/msdse.org/datascienceleadership2019/).
This is partially based on a previous paper: Daniel S. Katz, Kenton McHenry, Caleb Reinking, Robert Haines, "Research Software Development & Management in Universities: Case Studies from Manchester's RSDS Group, Illinois' NCSA, and Notre Dame's CRC", 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science)
doi: https://doi.org/10.1109/SE4Science.2019.00009
preprint: https://arxiv.org/abs/1903.00732
Parsl: Pervasive Parallel Programming in PythonDaniel S. Katz
a seminar presented at the School of Computer Science at the University of St Andrews 18 October 2019 (see https://blogs.cs.st-andrews.ac.uk/csblog/2019/09/25/daniel-katz-parsl/)
Requiring Publicly-Funded Software, Algorithms, and Workflows to be Made Publ...Daniel S. Katz
A presentation made to OECD's Committee for Scientific and Technological Policy (CSTP) at the Workshop on the Revision of the Recommendation of the Council concerning Access to Research Data from Public Funding, 15 October 2019
How different groups think about software sustainability, what "equations" we might use to measure it, and how it really can't be measured looking forward but only predicted.
Slides for:
"Software Citation in Theory and Practice," by Daniel S. Katz and Neil P. Chue Hong (published paper - https://doi.org/10.1007/978-3-319-96418-8_34; preprint - https://arxiv.org/abs/1807.08149), presented at International Congress on Mathematical Software (ICMS 2018)
Abstract. In most fields, computational models and data analysis have become a significant part of how research is performed, in addition to the more traditional theory and experiment. Mathematics is no exception to this trend. While the system of publication and credit for theory and experiment (journals and books, often monographs) has developed and has become an expected part of the culture, how research is shared and how candidates for hiring, promotion are evaluated, software (and data) do not have the same history. A group working as part of the FORCE11 community developed a set of principles for software citation that fit software into the journal citation system, allow software to be published and then cited, and there are now over 50,000 DOIs that have been issued for software. However, some challenges remain, including: promoting the idea of software citation to developers and users; collaborating with publishers to ensure that systems collect and retain required metadata; ensuring that the rest of the scholarly infrastructure, particu- larly indexing sites, include software; working with communities so that software efforts count; and understanding how best to cite software that has not been published.
A talk about "Conceptualizing a US Research Software Sustainability Institute (URSSI)" presented at the Toward a New Computational Fluid Dynamics Software Infrastructure (CFDSI, https://www.colorado.edu/events/cfdsi/) workshop in Boulder, CO, 16 May 2018.
A brief status of software citation work presented at AAS splinter meeting on implementing the FORCE11 Software Citation Principles in Astronomy (2018-01-11)
A talk about citation and reproducibility in software, presented at the HSF (High Energy Physics Software Foundation) meeting at SDSC, San Diego, CA, USA, 23 January 2017
Based on citation work done by the FORCE11 Software Citation Working Group as well as recent reproducibility discussions, blogs, and papers
Software Citation: Principles, Implementation, and ImpactDaniel S. Katz
A talk about Software Citation Principles for the 3:am conference (Bucharest, Romania, 28 September 2016), as developed by Arfon M. Smith, Daniel S. Katz, Kyle E. Niemeyer, and the FORCE11 Software Citation Working Group
A talk about the "Working towards Sustainable Software for Science: Practice and Experience (WSSSPE)" community/theme/set of workshop, focused on WSSSPE3, the working groups that were formed there, how they have developed from activities in previous WSSSPE3 meetings, and their current status.
This talk was given as a Dagstuhl meeting on Engineering Academic Software (http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=16252) 20 June 2016.
Working towards Sustainable Software for Science: Practice and Experience (WS...Daniel S. Katz
This was a short talk about the WSSSPE events, given at the Dagstuhl workshop on Engineering Academic Software, 20 June 2016. It mostly discusses the working groups that have formed gradually over the WSSSPE meetings, and specifically those that worked through WSSSPE3, and what that have done since then.
Scientific Software Challenges and Community ResponsesDaniel S. Katz
a talk given at RTI International on 7 December 2015, discussing 12 scientific software challenges and how the scientific software community is responding to them
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...
Advancing Science through Coordinated Cyberinfrastructure
1.
www.ci.anl.gov
www.ci.uchicago.edu
Advancing
Science
through
Coordinated
Cyberinfrastructure
Daniel
S.
Katz
d.katz@ieee.org
Senior
Fellow,
ComputaBon
InsBtute,
University
of
Chicago
&
Argonne
NaBonal
Laboratory
Affiliate
Faculty,
Center
for
ComputaBon
&
Technology,
Louisiana
State
University
Adjunct
Associate
Professor,
Electrical
and
Computer
Engineering,
LSU
2. www.ci.anl.gov
www.ci.uchicago.edu
2
Advancing
Science
through
CI
–
d.katz@ieee.org
Topics
• What
we
did
in
Louisiana
from
2006-‐2010
• What
I
would
do
differently
now
• A
short
video
to
highlight
some
addiBonal
issues
that
I
hope
the
Center
for
ComputaBonal
Engineering
&
Sciences
will
keep
in
mind
3. www.ci.anl.gov
www.ci.uchicago.edu
3
Advancing
Science
through
CI
–
d.katz@ieee.org
Louisiana
• Area: 134 382 km2 (33/51)
• Population: 4 533 000 (2010, 25/51)
• GDP: $208 billion (2009, 24/51)
• GDP/person: $45 700 (2009, 21/51)
• In Poverty: 17% (2009, 44/51)
• High School Degree: 82% (2009, 46/51)
• BS Degree: 21% (2009, 47/51)
• Advanced Degree: 7% (2009, 48/51)
State
Goals:
talented
workforce,
great
compeBBveness,
strong
educaBonal
system,
increased
economic
development
4. www.ci.anl.gov
www.ci.uchicago.edu
4
Advancing
Science
through
CI
–
d.katz@ieee.org
PITAC
Report
Summary:
• “ComputaBonal
science
-‐-‐
the
use
of
advanced
compuBng
capabiliBes
to
understand
and
solve
complex
problems
-‐-‐
is
criBcal
to
scienBfic
leadership,
economic
compeBBveness,
and
naBonal
security.
It
is
one
of
the
most
important
technical
fields
of
the
21st
century
because
it
is
essenBal
to
advances
throughout
society.”
• “UniversiBes
must
significantly
change
organizaBonal
structures:
mulBdisciplinary
&
collaboraBve
research
are
needed
[for
US]
to
remain
compeBBve
in
global
science”
Complex
problems:
Innova1ons
will
occur
at
boundaries
5. www.ci.anl.gov
www.ci.uchicago.edu
5
Advancing
Science
through
CI
–
d.katz@ieee.org
Big
Science
and
Infrastructure
• Higgs*
boson
discovery
announced
at
CERN
July
4,
2012
• Instrument:
Large
Hadron
Collider
(LHC)
• Infrastructure
– CompuBng
Hardware:
Worldwide
LHC
CompuBng
Grid
(WLCG):
235,000
cores
across
36
countries,
including
OpenScience
Grid
(OSG,
US),
European
Grid
Infrastructure
(EGI,
Europe),
...
– Data:
~20
PB
of
data
created
in
2011-‐2012
– Soiware:
grid
middleware,
physics
analysis
applicaBons,
...
– Networks
– EducaBon
&
Training
• Data
generated
centrally,
moved
(~3
PB/week)
across
mulB-‐Bered
infrastructure
to
be
compuBng
upon
6. www.ci.anl.gov
www.ci.uchicago.edu
6
Advancing
Science
through
CI
–
d.katz@ieee.org
Big
Science
and
Infrastructure
• Hurricanes
affect
humans
• MulB-‐physics:
atmosphere,
ocean,
coast,
vegetaBon,
soil
– Sensors
and
data
as
inputs
• Humans:
what
have
they
built,
where
are
they,
what
will
they
do
– Data
and
models
as
inputs
• Infrastructure:
– Urgent/scheduled
processing,
workflow
systems
– Soiware
applicaBons,
workflows
– Networks
– Decision-‐support
systems,
visualizaBon
– Data
storage,
interoperability
7. www.ci.anl.gov
www.ci.uchicago.edu
7
Advancing
Science
through
CI
–
d.katz@ieee.org
Long-‐tail
Science
and
Infrastructure
• Exploding
data
volumes
&
powerful
simulaBon
methods
mean
that
more
researchers
need
advanced
infrastructure
• Such
“long-‐tail”
researchers
cannot
afford
expensive
experBse
and
unique
infrastructure
• Challenge:
Outsource
and/or
automate
Bme-‐consuming
common
processes
– Tools,
e.g.,
Globus
Online
and
data
management
o Note:
much
LHC
data
is
moved
by
Globus
GridFTP,
e.g.,
May/June
2012,
>20
PB,
>20M
files
– Gateways,
e.g.,
nanoHUB,
CIPRES,
access
to
scienBfic
simulaBon
soiware
NSF
grant
size,
2007.
(“Dark
data
in
the
long
tail
of
science”,
B.
Heidorn)
8. www.ci.anl.gov
www.ci.uchicago.edu
8
Advancing
Science
through
CI
–
d.katz@ieee.org
Long-‐tail
Science
and
Infrastructure
• CIPRES
Science
Gateway
for
PhylogeneBcs
– Study
of
diversificaBon
of
life
and
relaBonships
among
living
things
through
Bme
• Highly
used,
as
of
mid
2013:
– Cited
in
at
least
400
publicaBons,
e.g.,
Nature,
PNAS,
Cell
– More
than
5000
unique
users
in
3
years
– Used
rouBnely
in
at
least
68
undergraduate
classes
– 45%
US
(including
most
states),
55%
70
other
countries
• Infrastructure
– Flexible
web
applicaBon
o A
science
gateway,
uses
soiware
and
lessons
from
XSEDE
gateways
team,
e.g.,
idenBfy
management,
HPC
job
control
– Science
soiware:
tree
inference
and
sequence
alignment
o Parallel
versions
of
MrBayes,
RAxML,
GARLI,
BEAST,
MAFFT
o PAUP*,
Poy,
ClustalW,
Contralign,
FSA,
MUSCLE,
...
– Data
o Personal
user
space
for
storing
results
o Tools
to
transfer
and
view
data
Credit:
Mark
Miller,
SDSC
9. www.ci.anl.gov
www.ci.uchicago.edu
9
Advancing
Science
through
CI
–
d.katz@ieee.org
Infrastructure
Challenges
• Science
– Larger
teams,
more
disciplines,
more
countries
• Data
– Size,
complexity,
rates
all
increasing
rapidly
– Need
for
interoperability
(systems
and
policies)
• Systems
– More
cores,
more
architectures
(GPUs),
more
memory
hierarchy
– Changing
balances
(latency
vs
bandwidth)
– Changing
limits
(power,
funds)
– System
architecture
and
business
models
changing
(clouds)
– Network
capacity
growing;
increase
networks
-‐>
increased
security
• Soiware
– MulBphysics
algorithms,
frameworks
– Programing
models
and
abstracBons
for
science,
data,
and
hardware
– V&V,
reproducibility,
fault
tolerance
• People
– EducaBon
and
training
– Career
paths
– Credit
and
avribuBon
10. www.ci.anl.gov
www.ci.uchicago.edu
10
Advancing
Science
through
CI
–
d.katz@ieee.org
Cyberinfrastructure
“Cyberinfrastructure
consists
of
compu1ng
systems,
data
storage
systems,
advanced
instruments
and
data
repositories,
visualiza1on
environments,
and
people,
all
linked
together
by
so@ware
and
high
performance
networks
to
improve
research
produc1vity
and
enable
breakthroughs
not
otherwise
possible.”
-‐-‐
Craig
Stewart
11. www.ci.anl.gov
www.ci.uchicago.edu
11
Advancing
Science
through
CI
–
d.katz@ieee.org
ComputaBonal
&
Data-‐enabled
Science
&
Engineering
(CDS&E)
• LIGO:
Laser
Interferometric
GravitaBonal
Wave
Observatory
• Ties
together
theory,
computaBon,
and
experiment
– Each
drives
the
other
two!
12. www.ci.anl.gov
www.ci.uchicago.edu
12
Advancing
Science
through
CI
–
d.katz@ieee.org
How
We
Started
• State
commitment:
$25M/year
for
Vision
20/20
– $9M:
LSU
-‐>
CCT
(similarly,
ULL
-‐>
LITE)
• University
commitment
to
build
new
programs
for
21st
century
• State
and
University
willingness
to
make
extraordinary
investments
• Opportunity
to
build
new
world
class
program
in
interdisciplinary
research
and
educaBon,
involving
all
of
LSU
• Ed
Seidel-‐led
vision
to
insBgate
state-‐wide
collaboraBon
13. www.ci.anl.gov
www.ci.uchicago.edu
13
Advancing
Science
through
CI
–
d.katz@ieee.org
Advancing
Research
• PotenBally
requires
advances
in
three
areas,
depending
on
exisBng
strengths
14. www.ci.anl.gov
www.ci.uchicago.edu
14
Advancing
Science
through
CI
–
d.katz@ieee.org
CCT
Director Office
Edward Seidel
HPC Partnership
McMahon
Cyberinfrastructure
Development
Katz
Focus Areas
Allen
LONI
Systems and
Software
Coast to Cosmos
LSU HPC
Performance Team
Core Comp. Sci.
Corporate Relations
Blue Waters, etc.
Material World
Labs: ACAL, DSL,
Viz, LCAT, …
NSF TeraGrid
Cultural Computing
Visualization
14
CCT
OrganizaBon
15. www.ci.anl.gov
www.ci.uchicago.edu
15
Advancing
Science
through
CI
–
d.katz@ieee.org
Cyberinfrastructure
Development
• Vision:
combine
research
and
infrastructure
– Research
o Computer
science
o ApplicaBons
o Tools
• Both
together
have
squared
growth
of
either
alone
• CyD
staff
–
PhDs
in
CS
and
apps
who
understand
the
whole
picture
and
want
to
grow
the
ecosystem
15
– Infrastructure
o Hardware
o OperaBons
o Policies
16. www.ci.anl.gov
www.ci.uchicago.edu
16
Advancing
Science
through
CI
–
d.katz@ieee.org
NaBonal
Lambda
Rail
UNO
Tulane
UL-‐L
SUBR
LSU
LA
Tech
LONI:
40
Gbps
network
LONI:
~100TF
IBM,
Dell
Supercomputers
Cybertools:
Tools
and
Services
CompuBng
in
Louisiana
LONI
InsBtute:
People
and
CollaboraBons
TeraGrid,
OSG
17. www.ci.anl.gov
www.ci.uchicago.edu
17
Advancing
Science
through
CI
–
d.katz@ieee.org
LONI
-‐
Networking
CompuBng
LSU
La TechLSU HSC
ULL
Tulane
SU
UNOLSU HSC
LONI node
Multiple 10GE
~500 core Dell cluster
112 proc. IBM P5 cluster
~4500 core Dell Cluster
ULM
McNeese
NSU
SLU
Alex
Network:
partners
and
customers
18. www.ci.anl.gov
www.ci.uchicago.edu
18
Advancing
Science
through
CI
–
d.katz@ieee.org
LONI
CompuBng
Resources
(2010)
• One
central
Dell
cluster
(Queen
Bee)
– 5500
IB-‐connected
cores
at
ISB
in
Baton
Rouge
– Archival
storage
contracted
through
NCSA
– 50%
of
allocaBons
dedicated
to
TeraGrid
from
2008
• Six
distributed
512-‐core
Dell
clusters
• Five
distributed
14-‐node
(112
procs)
IBM
P5-‐575
clusters
• Distributed
PetaShare
storage
– 32
TB
disk
@
each
small
Dell
cluster
– 8
TB
disk
on
LSU
LaTech
small
Dell
clusters
–
for
LBRN
– 8
TB
at
SC-‐S
HSC-‐NO
–
for
LBRN
– 250
TB
tape
• All
run
by
HPC@LSU,
including
user
support/training
20. www.ci.anl.gov
www.ci.uchicago.edu
20
Advancing
Science
through
CI
–
d.katz@ieee.org
Cactus
• Component-‐based
HPC
framework
– Freely-‐available
environment
for
collaboraBve
applicaBon
development
• Cuzng
edge
CS
– Grid
compuBng,
petascale,
accelerators,
steering,
remote
viz
• AcBve
user
developer
communiBes
– 10
year
pedigree,
$10M
support
– Numerical
RelaBvity,
CFD,
Coastal,
Reservoir
Engineering,
…
• Domain-‐specific
toolkits,
e.g.
CFD
toolkit
– FD/FV/FE
numerical
methods
– Structured,
mulB-‐block,
unstructured
– Uses
PETSc,
Trilinos,
MUMPS,
HYPRE
– Used
to
build
Black
Oil
Toolkit
21. www.ci.anl.gov
www.ci.uchicago.edu
21
Advancing
Science
through
CI
–
d.katz@ieee.org
PetaShare
• Main
concept:
data
is
managed
(migrated,
moved,
replicated,
cached,
etc.)
automaBcally
• Data-‐aware
storage
systems,
data-‐aware
schedulers,
cross-‐domain
metadata
scheme
• Provides:
250
TB
disk,
400
TB
tape
storage
(and
access
to
naBonal
storage
faciliBes)
• ApplicaBons:
coastal
environmental
modeling,
geospaBal
analysis,
bioinformaBcs,
medical
imaging,
fluid
dynamics,
petroleum
engineering,
numerical
relaBvity,
high
energy
physics.
Credit:
Tevfik
Kosar
22. www.ci.anl.gov
www.ci.uchicago.edu
22
Advancing
Science
through
CI
–
d.katz@ieee.org
LONI
InsBtute
“CCT
for
the
Louisiana”
• $15M
5-‐year
project
– $7M
BoR,
$8M
from
LaTech,
LSU,
SUBR,
Tulane,
UNO,
ULL
• Catalyzes
new
inter-‐insBtuBonal
collaboraBons,
ambiBous
projects
and
top
level
hires:
– LONI
network
and
compuBng
– NSF
projects:
PetaShare,
VizTangibles,
TeraGrid,
Blue
Waters
– EPSCoR:
NSF
CyberTools,
DOE
UCoMS,
DoD
– NIH:
$17M
LBRN
– Promote
collaboraBve
research
at
interfaces
for
innovaBon
23. www.ci.anl.gov
www.ci.uchicago.edu
23
Advancing
Science
through
CI
–
d.katz@ieee.org
LONI
InsBtute
Vision
• LONI
investments
create
world
leading
infrastructure
• Create
bold
new
inter-‐university
superstructure
– New
faculty,
staff,
students;
train
others.
Focus
on
CS,
Bio,
Materials,
but
all
disciplines
impacted
– Promote
research
at
interfaces
for
innovaBon
• Draw
on,
enhance
strengths
of
all
universiBes
– Strong
groups
recently
created;
collecBvely
world-‐class
– Solve
complex
problems
through
collaboraBon
computaBon
– Much
stronger
recruiBng
opportuniBes
for
all
insBtuBons
– Statewide
interdisciplinary
educaBon
research
program
• Create
University-‐Industry
Research
Centers
(UIRCs)
– Research
Triangle,
NCSA/UIUC,
Bay
Area,
others
• Transform
Louisiana
– Such
commived
cooperaBon
between
sites
extraordinary
24. www.ci.anl.gov
www.ci.uchicago.edu
24
Advancing
Science
through
CI
–
d.katz@ieee.org
LONI
InsBtute
Hiring
and
Projects
• Two
new
faculty
at
each
insBtuBon
(12
total)
– Six
in
CS,
six
in
Comp.
Bio/Materials
• Six
ComputaBonal
ScienBsts
– Following
Bavarian
KONWIHR
project
– Support
70-‐90
projects
over
five
years;
lead
to
external
funding
• Graduate
students
– 36
new
students
funded,
trained;
two
years
each
• One
Coordinator/economic
development
• All
hiring
coordinated
across
state
• Leading
faculty
across
state
create
mulB-‐insBtuBonal
seed
projects
• Building
on
seeds,
dozens
of
new
projects
selected,
started
• Exploit
common
themes,
compuBng
environments,
tools
found
in
all
areas
25. www.ci.anl.gov
www.ci.uchicago.edu
25
Advancing
Science
through
CI
–
d.katz@ieee.org
TeraGrid
(XSEDE)
• TeraGrid:
world’s
largest
open
scienBfic
discovery
infrastructure
• Leadership
class
resources
at
eleven
partner
sites
combined
to
create
an
integrated,
persistent
computaBonal
resource
– High-‐performance
networks
– High-‐performance
computers
(1
Pflops
(~100,000
cores)
-‐
1.75
Pflops)
o And
a
Condor
pool
(w/
~13,000
CPUs)
– VisualizaBon
systems
– Data
CollecBons
(30
PB,
100
discipline-‐specific
databases)
– Science
Gateways
– User
portal
– User
services
-‐
Help
desk,
training,
advanced
app
support
• Allocated
to
US
researchers
and
their
collaborators
through
naBonal
peer-‐review
process
– Generally,
review
of
compuBng,
not
science
• Mid
2011:
TeraGrid
-‐-‐
XSEDE
26. www.ci.anl.gov
www.ci.uchicago.edu
26
Advancing
Science
through
CI
–
d.katz@ieee.org
Campus
Champions
• “Champion”
is
a
staff
or
faculty
member
on
a
campus
that
provides
informaBon
on
XSEDE
to
his/her
colleagues
• Currently
~160
insBtuBons
represented
by
champions
• Champions
get:
– Monthly
training
and
updates
– Start-‐up
accounts
– Forum
for
sharing
and
interacBons
– Access
to
informaBon
on
usage
by
local
users
– RegistraBons
for
annual
XSEDE
Conference
waived
• Champions
do:
– Raise
awareness
locally
– Provide
training
– Get
users
started
with
access
quickly
– Represent
needs
of
local
community
– Provide
feedback
to
improve
services
– Avend
annual
XSEDE
conference
– Share
their
training
and
educaBon
materials
– Build
community
across
campus,
and
among
all
Champions
March 26, 2014
Revised March 22, 2014
Campus Champion Institutions
Standard – 87
EPSCoR States – 51
Minority Serving Institutions – 12
EPSCoR States and Minority Serving Institutions – 8
Total Campus Champion Institutions – 158
Credit:
Kay
Hunt
27. www.ci.anl.gov
www.ci.uchicago.edu
27
Advancing
Science
through
CI
–
d.katz@ieee.org
LONI
and
NaBonal
Cyberinfrastructure
• TeraGrid
– One
of
the
11
TeraGrid
Resource
Providers
– Playing
a
role
in
TG-‐wide
governance
(TeraGrid
Forum,
ExecuBve
Steering
Commivee,
various
working
groups,
GIG
Director
of
Science)
– Contributed
administraBve
soiware
AmieGold
(glue
between
TG
account
info
and
local
info)
and
CS
soiware
(HARC,
PetaShare,
SAGA)
• OSG
– Currently
providing
resources
• XSEDE
– LONI
not
a
partner
in
XSEDE,
but
a
service
provider
• NaBonally
– Bringing
in
new
users
from
the
southeast
US
– LONI
InsBtute
ComputaBonal
ScienBsts
-‐
Campus
Champions
28. www.ci.anl.gov
www.ci.uchicago.edu
28
Advancing
Science
through
CI
–
d.katz@ieee.org
Create
and
maintain
a
CI
ecosystem
providing
new
capabili'es
that
advance
and
accelerate
scienBfic
inquiry
at
unprecedented
complexity
and
scale
Support
the
foundaBonal
research
needed
to
conBnue
to
efficiently
advance
CI
Enable
transformaBve,
interdisciplinary,
collaboraBve,
science
and
engineering
research
and
educaBon
through
the
use
of
advanced
CI
Transform
pracBce
through
new
policies
for
CI
addressing
challenges
of
academic
culture,
open
disseminaBon
and
use,
reproducibility
and
trust,
curaBon,
sustainability,
governance,
citaBon,
stewardship,
and
avribuBon
of
authorship
Develop
a
next
generaBon
diverse
workforce
of
scienBsts
and
engineers
equipped
with
essenBal
skills
to
use
and
develop
CI,
with
CI
used
in
both
the
research
and
educa'on
process
NSF
Vision:
Infrastructure
Role
Lifecycle
29. www.ci.anl.gov
www.ci.uchicago.edu
29
Advancing
Science
through
CI
–
d.katz@ieee.org
Relevant
NSF
Programs
• EPSCoR
–
targeted
support
for
states
that
are
less
successful
in
NSF
funding
• MRI
–
Major
Research
InstrumentaBon
• CIF21
(NSF’s
CI
umbrella)
– eXtreme
Digital
(XD)
– Track
1
(Blue
Waters)
– Soiware
Infrastructure
for
Sustained
InnovaBon
(SI2)
– Campus
Cyberinfrastructure
-‐
Network
Infrastructure
and
Engineering
(CC-‐NIE)
• IntegraBve
Graduate
EducaBon
and
Research
Traineeship
Program
(IGERT)
• General
research
programs
30. www.ci.anl.gov
www.ci.uchicago.edu
30
Advancing
Science
through
CI
–
d.katz@ieee.org
Recap
(to
2010)
• Louisiana
decides
that
science
and
technology
can
lead
to
a
bever
future
• Builds
a
regional
cyberinfrastructure
(network,
compuBng,
soiware,
~data,
people)
that
connects
to
naBonal-‐scale
infrastructure
– Using
a
mix
of
naBonal,
state,
and
local
funding
• Starts
to
change
culture
–
infuse
computaBon
in
academic
departments,
interdisciplinary
hiring,
large
collaboraBve
projects
• But...
• Didn’t
really
think
about
data
as
much
as
we
would
have
were
we
starBng
again
today
31. www.ci.anl.gov
www.ci.uchicago.edu
31
Advancing
Science
through
CI
–
d.katz@ieee.org
• Swii
is
designed
to
compose
large
parallel
workflows,
from
serial
or
parallel
applicaBon
programs,
to
run
fast
and
efficiently
on
a
variety
of
pla~orms
– A
parallel
scripBng
system
for
Grids
and
clusters
for
loosely-‐coupled
applicaBons
-‐
programs
(executable,
shell,
python,
R,
Octave,
Matlab,
etc.)
linked
by
exchanging
files
– Easy
to
write:
simple
high-‐level
C-‐like
funcBonal
language,
allows
small
Swii
scripts
to
do
large-‐scale
work
– Easy
to
run:
contains
all
services
for
running,
in
one
Java
applicaBon
o Works
on
mulBcore
workstaBons,
HPC,
Grids
(interfaces
to
schedulers,
Globus,
ssh)
– A
powerful,
efficient,
scalable
and
flexible
execuBon
engine.
o Scaling
O(10M)
tasks
–
.5M
in
live
science
work,
and
growing
o CollecBve
data
management
being
developed
to
opBmize
I/O
• Used
in
earth
science,
neuroscience,
proteomics,
molecular
dynamics,
biochemistry,
economics,
staBsBcs,
knowledge
modeling,
and
more
• hvp://www.ci.uchicago.edu/swii
M.
Wilde,
N.
Hategan,
J.
M.
Wozniak,
B.
Clifford,
D.
S.
Katz,
I.
Foster,
Swii:
A
language
for
distributed
parallel
scripBng,
Parallel
CompuBng,
v.
37(9),
pp.
633-‐652,
2011.
32. www.ci.anl.gov
www.ci.uchicago.edu
32
Advancing
Science
through
CI
–
d.katz@ieee.org
Swii
Programming
model:
all
execuBon
driven
by
parallel
data
flow
• analyze1()
and
analyze2()
are
computed
in
parallel
• analyze()
returns
r
when
they
are
done
• This
parallelism
is
automa1c
• Works
recursively
throughout
the
program’s
call
graph
– E.g.,
can
embed
within
foreach
loop,
itself
done
in
parallel
– Foreach
loops
can
be
nested
(int r) analyze(int i)!
{!
j = analyze1(i); !
k = analyze2(i);!
r = 0.5*(j + k);!
}!
!
33. www.ci.anl.gov
www.ci.uchicago.edu
33
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Science
through
CI
–
d.katz@ieee.org
Submit host (login node, laptop, Linux server)
Data server
Swift
script
Swii
runBme
system
has
drivers
and
algorithms
to
efficiently
support
and
aggregate
vastly
diverse
runBme
environments
Swii
Environment
Clouds:
Amazon
EC2,
XSEDE
Wispy,
Future
Grid
…
Application
Programs
34. www.ci.anl.gov
www.ci.uchicago.edu
34
Advancing
Science
through
CI
–
d.katz@ieee.org
Globus
Big data transfer
and sharing…
…with Dropbox-like simplicity…
…directly from your own storage systems
Run as a non-profit service
to the non-profit research community
35. www.ci.anl.gov
www.ci.uchicago.edu
35
Advancing
Science
through
CI
–
d.katz@ieee.org
Globus
Users
• “I
need
a
good
place
to
store
or
backup
my
(big)
research
data,
at
a
reasonable
price.”
• “I
need
to
easily,
quickly,
and
reliably
move
or
mirror
porBons
of
my
data
to
other
places,
including
my
campus
HPC
system,
lab
server,
desktop,
laptop,
XSEDE,
cloud,
etc.”
• “I
need
a
way
to
easily
and
securely
share
my
data
with
my
colleagues
at
other
insBtuBons.”
• “I
want
to
publish
my
data
so
that
it’s
available
and
discoverable
long-‐term.”
• “I
want
to
archive
my
data
in
case
it’s
needed
someBme
in
the
future.”
36. www.ci.anl.gov
www.ci.uchicago.edu
36
Advancing
Science
through
CI
–
d.katz@ieee.org
Globus
is
SaaS
• Web,
command
line,
and
REST
interfaces
• Reduced
IT
operaBonal
costs
• New
features
automaBcally
available
• Consolidated
support
troubleshooBng
• Easy
to
add
your
laptop,
server,
cluster,
supercomputer,
etc.
with
Globus
Connect
37. www.ci.anl.gov
www.ci.uchicago.edu
37
Advancing
Science
through
CI
–
d.katz@ieee.org
Globus
Connected
Resources
on
Campus
• Research
compuBng
center
• Department
/
lab
storage
• Campus-‐wide
home/project
file
system
• Mass
Storage
Systems
• Science
instruments
• Desktops
and
laptops
• Custom
web
applicaBons
• Amazon
Web
Services
S3
38. www.ci.anl.gov
www.ci.uchicago.edu
38
Advancing
Science
through
CI
–
d.katz@ieee.org
Lessons
• Three
triangle
facets
(infrastructure,
computaBonal,
interdisciplinary)
have
be
taken
seriously
at
highest
levels,
seen
as
important
component
of
academic
research
• Infrastructure
need
to
be
integrated
at
all
levels
(laboratory,
campus,
regional,
naBonal,
internaBonal)
–
users
need
to
be
able
to
easily
move
work
and
data
to
appropriate
systems,
and
collaborate
across
locaBons
• EducaBon
and
training
of
students
and
faculty
is
crucial
–
vast
improvements
are
needed
over
the
small
numbers
currently
reached
through
HPC
center
tutorials;
computaBon
and
computaBonal
thinking
need
to
be
part
of
new
curricula
across
all
disciplines
• Emphasis
should
be
made
on
broadening
parBcipaBon
in
computaBon,
not
just
focusing
on
high
end
systems
where
decreasing
numbers
of
researchers
can
join
in,
but
making
tools
much
more
easily
usable
and
intuiBve
and
freeing
all
researchers
from
the
limitaBons
of
their
personal
workstaBons,
and
providing
access
to
simple
tools
for
large
scale
parameter
studies,
data
archiving,
visualizaBon
and
collaboraBon
• Vision
needs
to
be
consistent
–
cannot
be
just
one
person
• Funding
needs
to
be
stable
(acBviBes
need
to
be
sustainable)
39. www.ci.anl.gov
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39
Advancing
Science
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CI
–
d.katz@ieee.org
Video
• Data
Sharing
-‐
hvps://www.youtube.com/
watch?v=N2zK3sAtr-‐4
40. www.ci.anl.gov
www.ci.uchicago.edu
40
Advancing
Science
through
CI
–
d.katz@ieee.org
Sources
• D.
S.
Katz
et
al.,
“Louisiana:
A
Model
for
Advancing
Regional
e-‐Science
through
Cyberinfrastructure,”
Philosophical
TransacBons
of
the
Royal
Society
A,
367(1897),
2009.
– authors
from
Louisiana
State
University,
Tulane
University,
University
of
Louisiana
at
Lafayeve,
Louisiana
Tech
University,
Louisiana
Community
and
Technical
College
System,
Southern
University,
University
of
New
Orleans
• G.
Allen
and
D.
S.
Katz,
“ComputaBonal
science,
infrastructure
and
interdisciplinary
research
on
university
campuses:
experiences
and
lessons
from
the
Center
for
ComputaBon
and
Technology,”
NSF
Workshop
on
Sustainable
Funding
and
Business
Models
for
Academic
Cyberinfrastructure
FaciliBes,
Cornell
University,
2010
• Daniel
S.
Katz,
David
Proctor,
“A
Framework
for
Discussing
e-‐Research
Infrastructure
Sustainability,”
hvp://dx.doi.org/10.6084/m9.figshare.790767,
submived
to
Workshop
on
Sustainable
Soiware
for
Science:
PracBce
and
Experiences
(hvp://wssspe.researchcompuBng.org.uk)
at
SC13
• Swii:
Swii
Team,
led
by
Mike
Wilde,
hvp://www.ci.uchicago.edu/swii
• Globus:
Globus
Team,
led
by
Ian
Foster
and
Steve
Tuecke,
hvp://
www.globus.org