The document discusses the acceleration of scientific discovery through the use of automated data management services, outsourcing, and cloud-based platforms like Globus. It highlights the importance of eliminating data friction and creating discovery engines to enhance collaboration and efficiency in scientific research. Key themes include the automation of data publication, sharing, and analysis, as well as the need for services that address the entire research lifecycle.

1. Ian Foster
Accelerating discovery
via science services

2. Life Sciences
and Biology
Advanced
MaterialsCondensed
Matter Physics
Chemistry and
Catalysis
Soft Materials
Environmental
and Geo
Sciences
Can we determine
pathways that lead
to novel states and
nonequilibrium
assemblies?
Can we observe –
and control –
nanoscale chemical
transformations in
macroscopic
systems?
Can we create new materials with
extraordinary properties – by engineering
defects at the atomic scale?
Can we map – and
ultimately harness –
dynamic heterogeneity
in complex correlated
systems?
Can we unravel the
secrets of biological
function – across
length scales?
Can we understand
physical and chemical
processes in the most
extreme environments?
2
We want to accelerate progress
on the most pressing questions

3. Publish
results
Collect
data
Design
experiment
Test
hypothesis
Hypothesize
explanation
Identify
patterns
Analyze
data
The discovery process is
iterative and time-consuming
Pose
question

4. J.C.R Licklider, 1960:
About 85% of my
“thinking” time
was spent getting into
a position to think,
to make a decision,
to learn something I
needed to know

5. Outsourcing
for economies of
scale in the use of
automated methods
Automation
to apply more
sophisticated
methods at larger
scales

6. Higgs discovery “only possible because
of the extraordinary achievements of …
grid computing”—Rolf Heuer, CERN DG
10s of PB, 100s of institutions,
1000s of scientists,
100Ks of CPUs,
Bs of tasks
Outsourcing and automation:
(1) The Grid

7. Outsourcing and automation:
(2) The Cloud

8. The Software as a Service
(SaaS) revolution
Customer relationship
management (CRM):
A knowledge-intensive process
Historically, handled manually
or via expensive, inflexible on-
premise software
SaaS has revolutionized
how CRM is consumed
 Outsource to provider who
runs software on cloud
 Access via simple interfaces
Ease of use Cost
Flexibility
SaaS
On-premise

9. Where can we automate and
outsource in science broadly?
Run experiment
Collect data
Move data
Check data
Annotate data
Share data
Find similar data
Link to literature
Analyze data
Publish data
Automate
and
outsource
Science
services

10. Many services are used by
science, but have limitations

11. Science services exist, but do
not address whole life cycle

12. Accelerating
discovery
via science services
(1) Eliminate data friction

13. The elimination of data friction
is a key to faster discovery
Civilization advances
by extending the number
of important operations
which we can perform
without thinking about
them (Whitehead, 1912)
Obstacles to data access,
movement, discovery,
sharing, and analysis slow
research, distort research
directions, and waste time
(DOE reports, 2005-2015)

14. We have the highways but not
the delivery service
Our highways
encompass the Internet,
ultra-high-speed networks,
science DMZs, data
transfer nodes, high-speed
transport protocols
A good delivery service
automates, schedules,
accelerates, adapts.
It provides APIs for
experts and casual users.
Cuts costs and saves

15. Globus: Research data
management as a service
Essential research data
management services
 File transfer
 Data sharing
 Data publication
 Identity and groups
Builds on 15 years of DOE
research
Outsourced and automated
 High availability, reliability,
performance, scalability
 Convenient for
 Casual users: Web interfaces
 Power users: APIs
 Administrators: Install, manage
globus.org

16. “I need to easily, quickly, & reliably
move data to other locations.”
Research Computing HPC Cluster
Lab Server
Campus Home Filesystem
Desktop Workstation
Personal Laptop
DOE supercomputer
Public Cloud
16

17. 17
One APS node
connects to
125 locations

18. “I need to get data from a scientific
instrument to my analysis system.”
Next Gen
Sequencer
Light Sheet Microscope
MRI Advanced
Light Source
18

19. “I need to easily and securely
share my data with my colleagues.”
19

20. Globus and the research data lifecycle
Researcher initiates
transfer request; or
requested automatically
by script, science
gateway
1
Instrument
Compute Facility
Globus transfers files
reliably, securely
2
Globus controls
access to shared
files on existing
storage; no need
to move files to
cloud storage!
4
Curator reviews and
approves; data set
published on campus
or other system
7
Researcher
selects files to
share, selects
user or group,
and sets access
permissions
3
Collaborator logs in to
Globus and accesses
shared files; no local
account required;
download via Globus
5
Researcher
assembles data set;
describes it using
metadata (Dublin
core and domain-
specific)
6
6
Peers, collaborators
search and discover
datasets; transfer and
share using Globus
8
Publication
Repository
Personal Computer
Transfer
Share
Publish
Discover
• SaaS  Only a web
browser required
• Use storage system
of your choice
• Access using your
campus credentials
20

21. Globus and DOE:
Terabytes per month

23. 5
major
services
130
federated
campus IdPs
115
petabytes
transferred
8,000
managed
storage systems
20 billion
files
processed
99.95%
uptime over
past 2 years
25,000
registered
users
>30
institutional
subscribers
3 months
longest
transfer
1 petabyte
biggest
transfer
50M
most files in
one transfer
13
national labs
use services
Globus by the numbers

24. Accelerating
discovery
via science services
(2) Create platform services

25. Globus service APIs provide
elements of a science platform
Identity, Group, and
Profile Management
…
Globus Toolkit
GlobusAPIs
GlobusConnect
Data Publication & Discovery
File Sharing
File Transfer & Replication
25

27. Publication as service for ACME
climate modeling consortium

28. kbase.us

29. Accelerating
discovery
via science services
(3) Liberate scientific data

30. Q: What is the biggest obstacle
to data sharing in science?
A: The vast majority of data
that is lost, or not online;
if online, not described;
if described, not indexed
Not accessible
Not discoverable
Not used
Contrast with common practice
for consumer photos (iPhoto)
 Automated capture
 Publish then curate
 Processing to add value
 Outsourced storage

31. We must automate the capture,
linking, and indexing of all data
Globus publication service
encodes and automates data
publication pipelines
Example application: Materials
Data Facility for materials
simulation and experiment data
Proposed distributed virtual
collections index, organize,
tag, & manage distributed data
Think iPhoto on steroids –
backed by domain knowledge
and supercomputing power

32. We must automate the capture,
linking, and indexing of all data
chiDB: Human-computer
collaboration to extract Flory-
Huggins (𝞆) parameters from
polymers literature
R. Tchoua et al.
Plenario: Spatially and
temporally integrated, linked,
and searchable database of
urban data
C. Catlett, B. Goldstein, T. Malik et al.

33. Flory-Huggins parameters liberated!
R. Tchoua, J. De Pablo

34. “I need to publish my data so that
others can find it and use it.”
Scholarly
Publication
Reference
Dataset
Research
Community
Collaboration

35. Publish dashboard
35

36. Configuring a publication
pipeline: Publication “facets”
URL Handle DOI
identifier
none standard custom
description
domain-specific
none acceptance machine-validated
curation
human-validated
anonymous Public collaborators
access
embargoed
transient project lifetime “forever”
preservation
archive
36

37. Accelerating
discovery
via science services
(4) Create discovery engines

38. Data-driven science requires
collaborative discovery engines
informatics
analysis
high-throughput
experiments
problem
specification
modeling and
simulation
analysis &
visualization
experimental
design
analysis &
visualization
Integrated
databases
Rick Stevens

39. Example: A discovery engine for
disordered structures
Diffuse scattering images from Ray Osborn et al., Argonne
SampleExperimental
scattering
Material
composition
Simulated
structure
Simulated
scattering
La
60%
Sr
40%
Detect errors
(secs—mins)
Knowledge base
Past experiments;
simulations; literature;
expert knowledge
Select experiments
(mins—hours)
Contribute to knowledge base
Simulations driven by
experiments (mins—days)
Knowledge-driven
decision making
Evolutionary optimization

40. Integrate data movement, management, workflow,
and computation to accelerate data-driven
applications, organize data for efficient use
New architectures and methods create
opportunities and challenges
Integrate statistics/machine learning to assess
many models and calibrate them against `all'
relevant data
New computer facilities enable on-demand
computing and high-speed analysis of large
quantities of data

41. Simulation
Characterize,
Predict
Assimilate
Steer data
acquisition
Data analysis
Reconstruct,
detect features,
auto-correlate,
particle
distributions, …
Science automation services
Scripting, security, storage, cataloging, transfer
~0.001-0.5 GB/s/flow
~2 GB/s total burst
~200 TB/month
~10 concurrent flows
(Today: x10 in 5 yrs)
Integration
Optimize, fit, …
Configure
Check
Guide
Batch
Immediate
0.001 1 100+
PFlops
Precompute
material
database
Reconstruct
image
Auto-
correlation
Feature
detection
Scientific opportunities
 Probe material structure and
function at unprecedented scales
Technical challenges
 Many experimental modalities
 Data rates and computation
needs vary widely; increasing
 Knowledge management,
integration, synthesis
Towards discovery engines for
energy science (Argonne LDRD)

42. Linking experiment and
computation
Single-crystal diffuse scattering
Defect structure in disordered materials.
(Osborn, Wilde, Wozniak, et al.)
Estimate structure via inverse modeling:
many-simulation evolutionary optimization on
100K+ BG/Q cores (Swift+OpenMP).
Near-field high-energy X-ray diffraction microscopy
Microstructure in bulk materials (Almer, Sharma, et al.)
Reconstruction on 10K+ BG/Q cores (Swift) takes ~10 minutes,
vs. >5 hours on APS cluster or months if data taken home. Used to
detect errors in one run that would have resulted in total waste of beamtime.
X-ray nano/microtomography
Bio, geo, and material science imaging.
(Bicer, Gursoy, Kettimuthu, De Carlo, et al.).
Innovative in-slice parallelization method gives
reconstruction of 360x2048x1024 dataset in ~1
minute, using 32K BG/Q cores, vs. many days
on cluster: enables quasi-instant response
2-BM
1-ID
6-ID
Populate
Sim Sim
Select
Sim
Microstructure of a copper
wire, 0.2mm diameter
Advanced
Photon Source
Experimental and simulated
scattering from manganite

43. Rapid assessment of alignment quality
in high-energy diffraction microscopy
Blue Gene/Q
Orthros
(All data in NFS)
3: Generate
Parameters
FOP.c
50 tasks
25s/task
¼ CPU hours
Uses Swift/K
Dataset
360 files
4 GB total
1: Median calc
75s (90% I/O)
MedianImage.c
Uses Swift/K
2: Peak Search
15s per file
ImageProcessing.c
Uses Swift/K
Reduced
Dataset
360 files
5 MB total
feedback to experiment
Detector
4: Analysis Pass
FitOrientation.c
60s/task (PC)
1667 CPU hours
60s/task (BG/Q)
1667 CPU hours
Uses Swift/T
GO Transfer
Up to
2.2 M CPU hours
per week!
ssh
Globus Catalog
Scientific Metadata
Workflow ProgressWorkflow
Control
Script
Bash
Manual
This is a
single
workflow
3: Convert bin L
to N
2 min for all files,
convert files to
Network Endian
format
Before
After
Hemant Sharma
Justin Wozniak
Mike Wilde
Jon Almer

44. Science services raise research
and policy questions
 What else can we automate and outsource?
 How do we choose opportunities?
 How do we measure success?
 How must our computer systems evolve?
 High-capacity discovery engines: where, how?
 What will science become in a services era?
 Will it be more democratic? Collaborative?
Entrepreneurial? More or less creative?
 What are implications for trust and reproducibility?

45. What would Beer say?
The question which asks how to
use the computer in the
enterprise, is, in short, the wrong
question. A better formulation is
to ask how the enterprise should
be run given that computers exist.
The best version of all is the
question asking what, given
computers, the enterprise now
is. – Stafford Beer, “Brain of the
Firm”, 1972

46. informatics
analysis
high-throughput
experiments
problem
specification
modeling and
simulation
analysis &
visualization
experimental
design
analysis &
visualization
Integrated
databases
Opportunities and challenges
for discovery acceleration
Immediate opportunities
 Reduce data friction and
accelerate discovery by
applying Globus services
across DOE facilities
 Develop new services to
capture, link science data
Important research agenda
 Discovery engines to answer
major scientific questions
 New research modalities
linking computation and data
 Organization and analysis of
massive science data

47. Thank you to our sponsors!
U.S. DEPARTMENT OF
ENERGY
47

48. For more information: foster@anl.gov
Thanks to co-authors and Globus team
Globus services (globus.org)
 Foster, I. Globus Online: Accelerating and democratizing science through
cloud-based services. IEEE Internet Computing(May/June):70-73, 2011.
 Chard, K., Tuecke, S. and Foster, I. Efficient and Secure Transfer,
Synchronization, and Sharing of Big Data. Cloud Computing, IEEE, 1(3):46-55,
2014.
 Chard, K., Foster, I. and Tuecke, S. Globus Platform-as-a-Service for
Collaborative Science Applications. Concurrency - Practice and Experience,
27(2):290-305, 2014.
Publication (globus.org/data-publication)
 Chard, K., Pruyne, J., Blaiszik, B., Ananthakrishnan, R., Tuecke, S. and Foster, I.,
Globus Data Publication as a Service: Lowering Barriers to Reproducible
Science. 11th IEEE International Conference on eScience Munich, Germany, 2015
Discovery engines
 Foster, I., Ananthakrishnan, R., Blaiszik, B., Chard, K., Osborn, R., Tuecke, S., Wilde,
M. and Wozniak, J. Networking materials data: Accelerating discovery at an
experimental facility. Big Data and High Performance Computing, 2015.

49. Questions?
foster@anl.gov