This presentation was given at the 2019 GlobusWorld Conference in Chicago, IL by Kyle Chard from the University of Chicago.

1. Globus Labs: Forging the
Next Frontier
Kyle Chard
chard@uchicago.edu

2. Globus Labs
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3. Research data management and analysis challenges
• Data acquired at various
locations/times
• Analyses executed on
distributed resources with
different capabilities
– Processing time decreases
with distance
• Dynamic collaborations
around data and analysis
Raw
data
Catalog
DOE LabCampus
Community Archive
FPGACloud

4. Exacerbated by large-scale science
• Best practices overlooked, useful
data forgotten, errors propagate
• Researchers allocated short periods
of instrument and compute time
• Inefficiencies  less science
• Errors  long delays, missed
opportunity …forever!

5. Making research data reliably, rapidly, and securely
accessible discoverable, and usable
• Automation: encode research pipelines comprised of triggers and actions
• funcX: scalable function as a service for science
• Parsl: intuitive parallel programming in Python
• PolyNER: extracting scientific facts from published literature
• DLHub: model publication and inference
• MDF: publication and scarping of materials datasets
• XtractHub: deriving metadata from scientific files
• Cost-aware computing: application profiling, resource prediction, automated
provisioning
• Cloud classification: identifying different types of (real) clouds in climate data
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6. Ripple: A Trigger-Action platform for data
• Monitors events on various
file system types
• Includes a set of triggers
and actions to create rules
• Ripple processes data
triggers and reliably
executes actions
• Usable by non-experts

7. Automating the research lifecycle
• Simple state machine model
– JSON-based language
– Conditions, loops, fault tolerance, etc.
– Propagates state through the flow
• Standardized API for integrating
custom event and action services
– Actions: synchronous or asynchronous
– Custom Web forms prompt for user input
• Actions secured with Globus Auth
Auth
Search
Manage
Execute

8. Remote execution of scientific workloads
• Compute wherever it makes the most sense:
– Hardware or software availability, data location,
analysis time, wait time, etc.
• Remote computing has always been
complex and expensive
– Now we have high speed networks, universal
trust fabrics (Globus Auth), and containers
• Many scientific workloads are comprised
of a collection of short duration functions
– E.g., machine learning inference, real-time
analyses, metadata extraction, image
reconstruction, sensor stream analysis
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9. funcX: High Performance Function as a Service for
Science
• Endpoints deployed at resource
– Manage provisioning and scheduling of
resources and data
– Scale-out based on resource needs
• Cloud service routes requests to
endpoints
• Singularity containers run functions
securely
• Globus Auth secures communication
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10. Composition and parallelism in Python
• Software is increasingly assembled rather than written
– High-level language (e.g., Python) to integrate and wrap components
from many sources
• Parallel and distributed computing is pervasive
– Increasing data sizes combined with plateauing sequential processing
power
– Parallel hardware (e.g., accelerators) and distributed computing systems
10parsl-project.org

11. Parsl: Pervasive Parallel Programming in Python
Apps define opportunities for parallelism
• Python apps call Python functions
• Bash apps call external applications
Apps return “futures”: a proxy for a result
that might not yet be available
Apps run concurrently respecting data
dependencies. Natural parallel programming!
Parsl scripts are independent of where they
run. Write once run anywhere!
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pip install parsl

12. Parsl executors scale to 2M tasks/256K workers
(weak scaling)
Weak scaling: 10 tasks (0-1s) per worker
HTEX and EXEX outperform other Python-
based approaches and scale to millions of tasks
HTEX and EXEX scale to 2K* and 8K* nodes,
respectively, with >1K tasks/s

13. Scientific literature is inaccessible to most machines
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Materials Informatics

14. PolyNER: Generalizable Scientific Named Entity
Recognition
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Word
embedding
Labelling
Trained classifier
Active
learning
Active Learning
• Scientific NER challenges:
– NLP approaches are not yet suitable for application to scientific
information extraction
– There is a lack of training data for applying ML
• PolyNER automates the creation of training data using
minimal human guidance
– Word embedding models to generate entity-rich corpora
– Context- and content-based classifiers
– Active learning to prioritize expert effort
• Better performance than leading chemical entity
extractors at a fraction of the cost
– 1000 labels, 5 hours of expert time
• Training data for lexicon-infused Bi-LSTM

15. Questions?
labs.globus.org
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