The Future of Scientific
Computing at Harvard
Alyssa A. Goodman
Professor of Astronomy
Director, Initiative in Innovative Computing
“The Heavy Red Bag”
How can computers advance (my) science?
A new collaborative scientific initiative at Harvard.
Computational challenges are
common across scientific disciplines
How to:
Acquire, transmit, organize, and query new kinds of data?
Apply distributed computing resources to solve complex problems?
Derive meaningful insight from large datasets?
Share, integrate and analyze knowledge across geographically dispersed
researchers?
Visually represent scientific results so as to maximize understanding?
Opportunity to collaborate and apply insights from one field to another
Filling the “Gap”
between Science and Computer Science
Scientific
disciplines
Computer Science
departments
Increasingly, core problems in
science require computational
solution
Focused on finding elegant
solutions to basic computer
science challenges
Typically hire/“home grow”
computationalists, but often lack
the expertise or funding to go
beyond the immediate pressing
need
Often see specific, “applied”
problems as outside their
interests
“Workflow” & “Continuum”
Workflow
Examples
Astronomy
Public Health
“Collect”
Telescope
Microscope,
Stethoscope, Survey
COLLECT
“National Virtual
Observatory”/
COMPLETE
CDC Wonder
“Analyze”
Study the density
structure of a starforming glob of gas
Find a link between
one factory’s chlorine
runoff & disease
ANALYZE
Study the density
structure of all starforming gas in…
Study the toxic effects
of chlorine runoff in the
U.S.
“Collaborate”
Work with your student
COLLABORATE
Work with 20 people in 5 countries, in real-time
“Respond”
Write a paper for a Journal.
RESPOND
Write a paper, the quantitative results of which
are shared globally, digitally.
Workflow
IIC contact: AG, FAS
Workflow
a.k.a. The Scientific Method (in the Age of the Age of High-Speed Networks,
Fast Processors, Mass Storage, and Miniature Devices)
IIC contact: Matt Welsh, FAS
Workflow: The Harvard Virtual Brain
Establishing a Harvard-wide Neuroscience Infrastructure
Data Acquisition
 MRI
 PET
 Microscopy
etc.
Faculty of Arts and Sciences
 Harvard College
 Division of Engineering
Distributed Data Storage
Harvard School
of Public Health
Data Processing
 Analysis
 Visualization
 Integration
etc.
BWH/MGH and UCSD Data
Left Hippocampal Volume
6000
Faculty of Medicine
 Harvard Medical School
 Affiliated Teaching Hospitals
5000
4000
3000
2000
Information Access
 Query
 Statistical Analysis
 Knowledge Management
etc.
1000
0.3
IIC contact: David Kennedy, HMS/MGH
0.4 0.5 0.6 0.7 0.8 0.9
CVLT Discriminability Score
1.0
Harvard IIC
New technologies for measurement and simulation are
transforming the “workflow.”
Biomedicine: pre-genomics
Biomedicine: genomics era
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•
•
•
•
Manual/low throughput
Solitary
Limited by two hands
Analog
•
•
•
•
High throughput
Automated/networked
Highly scalable
Digital
Continuum
“Computational Science”
Missing at Most Universities
“Pure” Discipline
Science
“Pure” Computer
Science
(e.g. Galileo)
(e.g. Turing)
Workflow & Continuum
For any particular scientific
investigation:
Where does, and could,
“computational science” make
improvements in this cycle?
Harvard Public Health “NOW” (Oct. 2004)
"In the past, experiments did not involve such large data sets," observed
Dyann Wirth, professor of infectious diseases in the Department of
Immunology and Infectious Diseases and member of the advisory group
for the core. "There has been a dramatic change in the past five to 10
years in the amount and availability of genomic data [or the DNA
sequences themselves] and functional genomic data, [or the sequences’
purpose]." In the past five years alone, the genomes of humans, rats,
and the malaria parasite Plasmodium Falciparum have been published,
for example.
"One of the purposes of bioinformatics is to reduce the number of
experiments that need to be done to achieve reliable information," said
L.J. Wei, professor of biostatistics in the Department of Biostatistics and
member of the advisory group for the core. "However, an issue right now
is that there are huge data sets that can be run through different kinds of
software programs, ending up with many data points. Unless we
understand and use bioinformatics well, we may not even know which of
those data points are important."
Filling the “computational science” gap: IIC
Problem-driven approach
…focusing effort on solving problems that will have greatest impact & educational
value
Collaborative projects
…combining disciplinary knowledge with computer science expertise
Interdisciplinary effort
…to ensure that best practices are shared across fields and that new tools and
methodologies will be broadly applicable
Links with industry
…to draw on and learn from experience in applied computation
Institutional funding
…to ensure effort is directed towards key needs and not driven solely by narrow
priorities of funding agencies
IIC at Harvard
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Numerical
Simulation of
Star Formation
•MHD turbulence gives “t=0”
conditions; Jeans mass=1
Msun
•50 Msun, 0.38 pc, navg=3 x
105 ptcls/cc
•forms ~50 objects
•T=10 K
•SPH, no B or L, G
•movie=1.4 free-fall times
Bate, Bonnell & Bromm 2002
(UKAFF)
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Simulations
&
Public Health
Goal:
Statistical
Comparison of
“Real” and
“Synthesized”
Star Formation
Figure based on work of Padoan, Nordlund, Juvela, et al.
Excerpt from realization used in Padoan & Goodman 2002.
Measuring Motions: Molecular Line Maps
Spectral Line Observations
Radio Spectral-line Observations of Interstellar Clouds
Radio Spectral-Line Survey
Alves, Lada & Lada 1999
Velocity from
Spectroscopy
Observed Spectrum
Telescope 
Spectrometer
1.5
Intensity
1.0
0.5
0.0
All thanks to Doppler
-0.5
100
150
200
250
"Velocity"
300
350
400
Velocity from
Spectroscopy
Observed Spectrum
Telescope 
Spectrometer
1.5
Intensity
1.0
0.5
0.0
All thanks to Doppler
-0.5
100
150
200
250
"Velocity"
300
350
400
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codec
decompressor
this picture.
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COMPLETE/FCRAO W(13CO)
Barnard’s Perseus
“Astronomical Medicine”
Excerpts from Junior Thesis of Michelle Borkin (Harvard College); IIC Contacts: AG (FAS) & Michael Halle (HMS/BWH/SPL)
IC 348
IC 348
“Astronomical Medicine”
“Astronomical Medicine”
“Astronomical Medicine”
Before “Medical Treatment”
After “Medical Treatment”
3D Slicer Demo (available after talk)
IIC contacts: Michael Halle & Ron Kikinis
IIC: Five Research Branches
Visualization
Distributed
Computing
Databases/
Provenance
Physically
meaningful
combination of
diverse data types.
e-Science aspects
of large
collaborations.
Management, and
rapid retrieval, of
data.
Sharing of data
and computational
resources and
tools in real-time.
“Research
reproducibility”
…where did the
data come from?
How?
Analysis &
Simulations
Development of
efficient
algorithms.
Cross-disciplinary
comparative tools
(e.g. statistical).
Instrumentation
Improved data
acquisition.
Novel hardware
approaches (e.g.
GPUs, sensors).
IIC: Innovative Organizational Model
Staffing
Highly accomplished academics and senior
experts whose careers have been primarily
in industry, working together
Promotion/
career path
Criteria for promotion will give equal weight
to scholarly activities, and to technological
invention
Culture
No “class” distinctions made between teaching and nonteaching faculty, scientists and engineers, artists and
designers working in the visualization program
How IIC will Function: Overview
IIC Objectives
Project selection
Identify and fund projects that are likeliest to have the
greatest and broadest impact
Project execution
Pursue projects in way that will yield best outcome, enable
shared learning, etc.
Dissemination
of knowledge
Enable new research for specific scientific discipline
Generate new computational tools for broader application
Project Selection
Project proposals
Program Advisory
Committee
IIC Management Team
Who participates
Any Harvard researcher (e.g.,
in genomics, fluid dynamics,
epidemiology,neuroscience,
nanoscience, comp bio,
chemical biology, optics,
geology, astronomy, quantum
mechanics, et al.)
Role
Submit proposal in
response to call for ideas
Harvard researchers
representing broad interests of
IIC stakeholders plus IIC
Director & Dir. of Research
Evaluate/rank proposals for
scientific merit: should this
be a priority for IIC?
Consists of
• IIC Director
• Dirs. of Res. & Adm/Ops
• Heads of IIC branches
Evaluate/prioritize proposals
according to technical
feasibility, assess resource
needs
Project Execution
IIC Project Team C, etc.
IIC Project
TeamManager
B
Project
IIC Project
TeamManager
A
Project
Project Manager
Discipline scientists
Discipline scientists
Discipline scientists
Scientists who “own”
the problem and are
committed to working
with IIC staff to tackle it
Responsible for
project execution and
metrics for tracking
progress/performance;
interfaces with IIC
branch heads
IIC staff
IIC staff
IIC staff
IIC staff scientists
assigned to work on
project by relevant IIC
branch heads. The
same IIC staff member
may serve on multiple
IIC project teams
Dissemination of Knowledge
Communities of
practice
• Internal...
• External…
Seminars/colloquia
Publications
Knowledge
management
system
• New tools
• IIC process
• Scientific journals
• IIC white papers
Education is central to IIC’s mission
At Harvard:
Undergraduate & graduate courses focused on “data-intensive
science”
New graduate certificate program, within existing Ph.D. programs
Research opportunities at undergraduate, graduate, and postdoctoral
levels
Beyond Harvard:
New museum, highlighting the kind of science done at the IIC
IIC organization: research and education
Provost
Dean,
Physical Sciences
Assoc Provost
IIC Director
Dir of Admin &
Operations
Dir of Research
Assoc Dir,
Instrumentation
Project 1
(Proj Mgr 1)

Project 2
(Proj Mgr 2)
Project 3
(Proj Mgr 3)
Etc.
CIO
(systems)
Knowledge
mgmt

Assoc Dir,
Visualization
Assoc Dir,
Databases/Data
Provenance

Assoc Dir,
Analysis &
Simulation
Assoc Dir,
Distributed
Computing
Education &
Outreach staff







Dir of Education &
Outreach
IIC: Examples
Visualization
Distributed
Computing
Databases/
Provenance
Physically
meaningful
combination of
diverse data types.
e-Science aspects
of large
collaborations.
Management, and
rapid retrieval, of
data.
Sharing of data
and computational
resources and
tools in real-time.
“Research
reproducibility”
…where did the
data come from?
How?
Analysis &
Simulations
Development of
efficient
algorithms.
Cross-disciplinary
comparative tools
(e.g. statistical).
Instrumentation
Improved data
acquisition.
Novel hardware
approaches (e.g.
GPUs, sensors).
Visualization: 3D Slicer (BWH Surgical Planning Lab)
IIC contacts: Michael Halle & Ron Kikinis
“Image and Meaning” (Visualization)
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Cinepak decompressor
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QuickTime™ and a
Cinepak decompressor
are needed to see this picture.
IIC contact: Felice Frankel (MIT)
Work: Garstecki/Whitesides (FAS)
Distributed Computing: Semantics, Ontologies
IIC Contact: Tim Clark (HMS/MGH)
IIC Contact: Tim Clark (HMS/MGH)
Distributed Computing & Large Databases:
Large Synoptic Survey Telescope
Optimized for time domain
scan mode
deep mode
7 square degree field
6.5m effective aperture
24th mag in 20 sec
> 5 Tbyte/night
Real-time analysis
Simultaneous multiple science goals
IIC contact: Christopher Stubbs (FAS)
Astronomy
LSST
SDSS
2MASS
2011
1998
2001
5000
Peak 500
Avg
8.3
Daily average data
rate (TB/day)
20
Annual data store
(TB)
High Energy Physics
DLS
BaBar
Atlas
RHIC
1992
1999
1998
2007
1999
1
1
2.7
60 (zerosuppressd)
6*
540*
120* (’03)
250* (’04)
0.02
0.016
0.008
0.012
0.6
60.0
3 (’03)
10 (’04)
2000
3.6
6
1
0.25
300
7000
200 (’03)
500 (’04)
Total data store
capacity (TB)
20,000
(10 yrs)
200
24.5
8
2
10,000
100,000
(10 yrs)
10,000
(10 yrs)
Peak computational load
(GFLOPS)
140,000
100
11
1.00
0.600
2,000
100,000
3,000
Average computational
load (GFLOPS)
140,000
10
2
0.700
0.030
2,000
100,000
3,000
Data release delay
acceptable
1 day
moving 3
months
static
2
months
6 months
1 year
6 hrs
(trans)
1 yr
(static)
1 day (max)
<1 hr (typ)
Few days
100 days
30 sec
none
none
<1 hour
1 hr
none
none
none
TBD
1GHz
Xeon
18
450MHz
Sparc
28
60-70MHz
Sparc
10
500MH
z
Mixed/
20GHz/
Pentium/
Pentium
5000
10,000
2500
First year of
operation
Run-time data rate to
storage (MB/sec)
Real-time alert of event
Type/number of
processors
MACHO
5
Analysis &
Simulations
Figure based on work of Padoan, Nordlund, Juvela, et al.
Excerpt from realization used in Padoan & Goodman 2002.
Analysis & Simulations: Neural Net Models of Intelligence
Does Speed of Convergence in Neural Nets Predict
Scores on Measures of “General Intelligence”?
Network Architecture
¥ (Asymmetric) Fully Connected Networks
Π Every node is connected to every other node
Π Connection may be excitatory (positive), inhibitory (negative), or
irrelevant (  0).
Π Most general
Π Symmetric fully connected nets: weights are symmetric ( wij = wji)
Input nodes : receive
input from the
environment
Output nodes : send
signals to the
environment
Hidden nodes : no
direct interaction to
the environment
Select from the lower 8
the one that completes the
pattern in the top 9
IIC contact: Stephen Kosslyn (Psychology)
(Easier) Analysis of Large Data Sets:
Mendelian Disease Genes
world
world
world
world
189
189
189
189
Hello
Hello
world 189
world 189
Hello
Hello
world 189
world 189
reformat,
merge,
and filter
Large data files
Can a biologist get
from here to there?
Without programming?
OMIM on the genome
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
Chromosome
Hello
Hello
Hello
Hello
0
50
100
150
Position (MB)
200
Location of every known disease
gene on the human genome
IIC contact: Eitan Rubin (FAS/CGR)
250
Instrumentation
IIC contact: Matt Welsh, FAS
IIC: Mission
The Institute for Innovative Computing (IIC) will make Harvard a world leader in the
innovative and creative use of computational resources to address forefront
scientific problems.
We will focus on developing capabilities that are applicable to multiple disciplines, by
undertaking specific, well-defined projects, thereby developing tools and
approaches that can be generalized and shared.
We will foster the flow of ideas and inventions along the continuum from basic
science to scientific computation to computational science to computer science.
We will train a next generation of creative and computationally capable scientists,
build linkages to industry, and communicate with the public at large.
Why Here?
Diverse group of senior faculty and accomplished scientists…
…spanning a wide range of relevant disciplines, e.g.,
Computer science
Physics, Chemistry, Astronomy, Statistics, Biology, Medicine, etc.
Psychology, Graphic Design
…with backgrounds in both academia and industry…
…deeply committed to the vision of a collaborative approach to
solving the most compelling computing challenges facing scientists
today