The Future of Scientific Computing at HarvardThe Future of Scientific Computing at HarvardAlyssa A. GoodmanProfessor of AstronomyDirector, Initiative in Innovative Computing

Alyssa A. GoodmanProfessor of AstronomyDirector, Initiative in Innovative Computing

“The Heavy Red Bag”“The Heavy Red Bag”

How can computers advance (my) science?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

Increasingly, core problems in science require computational solution

Typically hire/“home grow” computationalists, but often lack the expertise or funding to go beyond the immediate pressing need

Focused on finding elegant solutions to basic computer

science challenges

Often see specific, “applied” problems as outside their

interests

Scientific disciplines

Computer Science departments

“Workflow” & “Continuum”

Workflow Examples Astronomy Public Health

““Collect”Collect” TelescopeTelescope Microscope, Microscope,

Stethoscope, SurveyStethoscope, Survey

COLLECTCOLLECT ““National Virtual National Virtual Observatory”/Observatory”/COMPLETECOMPLETE

CDC WonderCDC Wonder

““Analyze”Analyze” Study the density Study the density structure of a star-structure of a star-forming glob of gasforming glob of gas

Find a link between one Find a link between one factory’s chlorine runoff factory’s chlorine runoff

& disease& disease

ANALYZEANALYZE Study the density Study the density structure of structure of allall star- star-

forming gas in…forming gas in…

Study the toxic effects Study the toxic effects of chlorine runoff of chlorine runoff in the in the

U.SU.S..

““Collaborate”Collaborate” Work with your student Work with your student

COLLABORATECOLLABORATE Work with 20 people in 5 countries, in real-timeWork with 20 people in 5 countries, in real-time

““Respond”Respond” Write a paper for a Journal.Write a paper for a Journal.

RESPONDRESPOND Write a paper, the quantitative results of which Write a paper, the quantitative results of which are shared globally, digitally.are shared globally, digitally.

IIC contact: AG, FAS

WorkflowWorkflow

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

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CVLT Discriminability Score

1000

2000

3000

4000

5000

6000

Left Hippocampal Volume

BWH/MGH and UCSD Data

Faculty of Arts and Sciences Harvard College Division of Engineering

Harvard School of Public Health

Faculty of Medicine Harvard Medical School Affiliated Teaching Hospitals

Data Acquisition MRI PET Microscopy etc.

Distributed Data Storage

Data Processing Analysis Visualization Integration etc.

Information Access Query Statistical Analysis Knowledge Management etc.

Establishing a Harvard-wide Neuroscience Infrastructure

Harvard IICIIC contact: David Kennedy, HMS/MGH

New technologies for measurement and simulation are transforming the “workflow.”

• Manual/low throughput• Solitary• Limited by two hands• Analog

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• High throughput • Automated/networked• Highly scalable• Digital

Biomedicine: pre-genomics Biomedicine: genomics era

Continuum

“Pure” Discipline Science

(e.g. Galileo)

“Pure” Computer Science

(e.g. Turing)

“Computational Science”Missing at Most Universities

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

valueCollaborative 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 HarvardIIC at Harvard

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Numerical Simulation of

Star Formation

Bate, Bonnell & Bromm 2002 (UKAFF)

•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 •movie=1.4 free-fall times

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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.

Spectral Line Observations

Measuring Motions: Molecular Line Maps

Alves, Lada & Lada 1999

Radio Spectral-Line Survey

Radio Spectral-line Observations of Interstellar Clouds

Velocity from Spectroscopy

1.5

1.0

0.5

0.0

-0.5

Inte

nsit

y

400350300250200150100

"Velocity"

Observed Spectrum

All thanks to Doppler

Telescope Spectrometer

1.5

1.0

0.5

0.0

-0.5

Inte

nsit

y

400350300250200150100

"Velocity"

Observed Spectrum

Telescope Spectrometer

All thanks to Doppler

Velocity from Spectroscopy

Barnard’s Perseus

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COMPLETE/FCRAO W(13CO)

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“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”

After “Medical Treatment”After “Medical Treatment”Before “Medical Treatment”Before “Medical Treatment”

3D Slicer Demo (available after talk)

IIC contacts: Michael Halle & Ron Kikinis

Visualization Distributed Computing

Databases/ Provenance

Analysis & Simulations

Instrumentation

Physically meaningful combination of diverse data types.

e-Science aspects of large collaborations.

Sharing of data and computational resources and tools in real-time.

Management, and rapid retrieval, of data.

“Research reproducibility” …where did the data come from? How?

Development of efficient algorithms.

Cross-disciplinary comparative tools (e.g. statistical).

Improved data acquisition.

Novel hardware approaches (e.g. GPUs, sensors).

IIC: Five Research Branches

IIC: Innovative Organizational Model

Culture

Staffing

Promotion/career path

Criteria for promotion will give equal weight to scholarly activities, and to technological invention

No “class” distinctions made between teaching and non-teaching faculty, scientists and engineers, artists and designers working in the visualization program

Highly accomplished academics and senior experts whose careers have been primarily in industry, working together

How IIC will Function: Overview

IIC Objectives

Identify and fund projects that are likeliest to have the greatest and broadest impact

Pursue projects in way that will yield best outcome, enable shared learning, etc.

Enable new research for specific scientific disciplineGenerate new computational tools for broader application

Project execution

Dissemination of knowledge

Project selection

Role

Submit proposal in response to call for ideas

Evaluate/rank proposals for scientific merit: should this be a priority for IIC?

Evaluate/prioritize proposals according to technical feasibility, assess resource needs

Who participatesAny Harvard researcher (e.g., in genomics, fluid dynamics, epidemiology,neuroscience, nanoscience, comp bio, chemical biology, optics, geology, astronomy, quantum mechanics, et al.)

Harvard researchers representing broad interests of IIC stakeholders plus IIC Director & Dir. of Research

Consists of• IIC Director• Dirs. of Res. & Adm/Ops• Heads of IIC branches

Project Selection

Program Advisory Committee

Project proposals

IIC Management Team

Project Execution

Responsible for project execution and metrics for tracking progress/performance; interfaces with IIC branch heads

Scientists who “own” the problem and are committed to working with IIC staff to tackle it

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

Discipline scientists IIC staff

Project Manager

IIC Project Team C, etc.

Discipline scientists IIC staff

Project Manager

IIC Project Team B

Discipline scientists IIC staff

Project Manager

IIC Project Team A

Dissemination of Knowledge

Seminars/colloquia Publications

Knowledge management

system

Communities of practice

• Scientific journals• IIC white papers

• Internal...• External…

• New tools• IIC process

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

Assoc Dir, Instrumentation

Assoc Dir, Visualization

Assoc Dir, Analysis & Simulation

Provost

IIC DirectorAssoc Provost

Dir of Admin & Operations

Project 1(Proj Mgr 1)

Project 2(Proj Mgr 2)

Project 3(Proj Mgr 3)

Dir of Education &Outreach

Etc.

CIO (systems)

Knowledgemgmt

Education &Outreach staff

Dean, Physical Sciences

Dir of Research

Assoc Dir, Databases/Data

Provenance

Assoc Dir, Distributed Computing

Visualization Distributed Computing

Databases/ Provenance

Analysis & Simulations

Instrumentation

Physically meaningful combination of diverse data types.

e-Science aspects of large collaborations.

Sharing of data and computational resources and tools in real-time.

Management, and rapid retrieval, of data.

“Research reproducibility” …where did the data come from? How?

Development of efficient algorithms.

Cross-disciplinary comparative tools (e.g. statistical).

Improved data acquisition.

Novel hardware approaches (e.g. GPUs, sensors).

IIC: Examples

Visualization: 3D Slicer (BWH Surgical Planning Lab)

IIC contacts: Michael Halle & Ron Kikinis

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IIC contact: Felice Frankel (MIT)Work: Garstecki/Whitesides (FAS)

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“Image and Meaning” (Visualization)

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 domainOptimized for time domain

scan modescan mode

deep modedeep mode

7 square degree field7 square degree field

6.5m effective aperture6.5m effective aperture

24th mag in 20 sec24th mag in 20 sec

> 5 Tbyte/night> 5 Tbyte/night

Real-time analysisReal-time analysis

Simultaneous multiple science goalsSimultaneous multiple science goals

IIC contact: Christopher Stubbs (FAS)

  Astronomy High Energy Physics

  LSST SDSS 2MASS MACHO DLS BaBar Atlas RHIC

First year of operation

2011 1998 2001 1992 1999 1998 2007 1999

Run-time data rate to storage (MB/sec)

 5000 Peak

500 Avg

 8.3

 1

 1

 2.7

60 (zero-suppressd)

6*

 540*

120* (’03)250* (’04)

Daily average datarate (TB/day)

20 0.02 0.016 0.008 0.012 0.6 60.0 3 (’03)10 (’04)

Annual data store(TB)

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 computationalload (GFLOPS)

140,000 10 2 0.700 0.030 2,000 100,000 3,000

Data release delayacceptable

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

Real-time alert of event

30 sec none none <1 hour 1 hr none none none

Type/number of processors

TBD 1GHzXeon

18

450MHz Sparc

28

60-70MHz Sparc

10

500MHz

Pentium5 

Mixed/

5000

20GHz/

10,000

Pentium/

2500

Analysis & Simulations

Figure based on work of Padoan, Nordlund, Juvela, et al.Excerpt from realization used in Padoan & Goodman 2002.

Network Architecture• (Asymmetric) Fully Connected Networks

– Every node is connected to every other node– Connection may be excitatory (positive), inhibitory (negative), or

irrelevant (≈0).– Most general– : (Symmetric fully connected nets weights are symmetricwij=wji)

Input nodes:receive input from the

environmentOutput nodes:send

signals to theenvironment

Hidden nodes:no direct interaction to

the environment

Analysis & Simulations: Neural Net Models of Intelligence

Does Speed of Convergence in Neural Nets Predict Scores on Measures of “General Intelligence”?

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

OMIM on the genome

0123456789

101112131415161718192021222324

0 50 100 150 200 250Position (MB)

Ch

rom

oso

me

12

Hello world 189Hello world 189Hello world 189Hello world 189

Hello world 189Hello world 189

Hello world 189Hello world 189

Large data files

reformat,merge,and filter

Can a biologist get from here to there?

Location of every known disease gene on the human genome

Without programming?

IIC contact: Eitan Rubin (FAS/CGR)

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