Foundations for the Future of Science
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Foundations for the future of science discusses using artificial intelligence and machine learning to advance scientific research. Key points discussed include using AI to analyze large datasets, develop scientific models, and automate experimental workflows. The document also outlines several examples of how the Globus data platform is currently enabling AI-powered scientific applications across multiple domains. Overall, the document advocates that embracing "AI for science" has the potential to accelerate scientific discovery by overcoming limitations in human analysis capabilities and computational resources.
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Foundations for the Future of Science
- 1. Foundations for the future of science Ian Foster, Rachana Ananthakrishnan, Kyle Chard, Vas Vasiliadis GlobusWorld - May 12, 2021
- 4. Serial Synchrotron Crystallography of SARS-CoV-2 proteins
- 5. The COVID’19 data pipeline: HPC, ML, people developing machine readable datasets for small molecule libraries CHEMICAL LIBRARY DATABASE AND MORE known molecules 4B COMPUTING RESOURCES CANONICALIZATION COMPUTE FEATURES DEEP LEARNING FILTERING FINGERPRINTING SIMILARITY SEARCH GENERATE IMAGES CNN FILTERING Yadu Babuji, Ben Blaiszik, Kyle Chard, Ryan Chard, Ian Foster, Logan Ward, Tom Brettin et al
- 8. Ingest Annotate,Assemble, Align,Interpolate, Normalize Introspect Correct,Calibrate Characterize,Detect Anomalies FAIR Data Commons Intelligent Edge AdaptiveSampling &EdgeComputing Data Sources Data Product Create,Publish Catalog,Version, ShareDOI Experiment Engine Active Data Path Continuous Reanalysis Active Learning Simulation Scientists, Public, DecisionMakers 8
- 9. The next frontier? “AI for science” “Most of the modeling and prediction necessary to produce the next generation of breakthroughs in science, energy, medicine, and national security will come not from applying traditional theory, but from employing data-driven methods at extreme scale tightly coupled to experiments and scientific user facilities.” — US Department of Energy FY 2021 Congressional Budget Justification
- 10. Why am I excited about “AI for science”? Push • Step changes in AI/ML methods, notably deep neural networks • Major advances in areas like machine translation, speech recognition, image processing • New hardware specialized for deep neural networks Pull • Exploding volumes of data due to new sensors and instrumentation exceed human capabilities • End of Moore’s Law puts hard problems out of reach • Growing complexity of science and engineering problems slowing rate of discovery Why are we excited about “AI for science”? Push • Step changes in AI/ML methods, notably deep neural networks • Major advances in areas like machine translation, speech recognition, image processing • New hardware specialized for deep neural networks Pull • Exploding volumes of data due to new sensors and instrumentation exceed human capabilities • End of Moore’s Law puts hard problems out of reach • Growing complexity of science and engineering problems slowing rate of discovery
- 11. AI Enabled Experimental Workflows (how to make it) …materials, polymers, organisms… …self-driving labs, synthesis search… • data Sets • literature • science “news” • strategy Cleaned Updated Annotated Aggregated Interpreted AI Enabled Scientific Comprehension (what it means) AI-Enabled Design Workflows (what to make) Insight? AI Science Applications: One per Planet
- 12. Augmented Simulations Design Control Science and Math Comprehension Generative Models Inverse Problems Multimodal Learning Decision- Making Materials Biology Chemistry Devices Batteries Drugs Waveforms Text Images Structured Graphs Time- series Image2Phase Spectra 2 Structures Waveform 2 Source Detector Simulations Cosmology Biodesign Experiments Accelerators Reactors Mobility Simulation Energy Landscape Search Surrogates Optimize Mathematics Physics Biochemistry Risk Assessment Research Priorities The Next Problem AI for Science: AI Building Blocks (examples)
- 14. Protein engineering Liquid-handling robot SAXS, SA- XPCS: 8-ID-I Beamline Digital twin + AI components Robotic pendant drop Screen ~108 conditions for LLPS Screen ~104 combos for LLPS (turbidity, confocal microscope imaging) Screen ~102 combos at various temperatures Selected matrixes (e.g., salt, pH, PEG) Stock proteins (different periods, repeats) X-ray Info transfer and control, demonstrated Information transfer and control, not yet demonstrated Material transfer, not yet demonstrated Change sample Measure sample HPC simulation Compute ~105 properties ALCF APCF APS Arvind Ramanathan et al. Example: Rational design of intrinsically disordered polypeptides
- 15. AI for science means rethinking infrastructure 15 Infrastructure for AI-enabled Science Scientific instruments Major user facilities Laboratories Automated labs … Sensors Environmental Laboratories Mobile … Simulation codes Computational results Function memoization … Databases Reference data Experimental data Computed properties Scientific literature … Scientists, engineers Expert input Goal setting … Industry, academia New methods Open source codes AI accelerators … Data ingest Inference HPO Data enhancement Data QA/QC Feature selection Model training UQ Model reduction Active/ reinforcement learning Artificial Intelligence Methods Data Models Accelerators Compute Agile Infrastructure Surrogates System Software Data mgmt Operating system Portability Compilers Runtime system Workflow Automation Prog. envs. Languages Model creation Libraries Resource mgmt Authen/Access
- 17. Understanding SARS-CoV-2 Protein Structure 17 “These data services have taken the time to solve a structure from weeks to days and now to hours” Darren Sherrell, SBC beamline scientist APS Sector 19
- 18. Data Management at Cyro-EM Facilities 18 Case Western Reserve – Cryo-EM Core Credit: https://case.edu/medicine/research/som-core- facilities/cryo-electron-microscopy-core Credit: https://pncc.labworks.org/about-us Pacific Northwest Cryo-EM Processing Center (PNNL and Oregon Health Sciences University) Globus for – automated data sync as new data is collected – provisioning of data access for researchers – reliable, secure data access for users – Monitoring and management via console
- 19. The Bioinformatics Core of the Lineberger Comprehensive Cancer Center at the University of North Carolina Global data distribution at bioinformatics core – Multiple research projects use Globus for data sharing with external collaborators – Support wide variety of projects: different locations, sources, sizes, cancer types, institution type, storage systems, and identities
- 20. Digital agriculture – University of Winnipeg • Increasing crop yields using machine learning models • Building training data sets – 40K images per day, tagged with metadata – Move data from diverse sources to campus storage, then onto Compute Canada HPC to run models • Orchestrate data transfer using Globus CLI 20 Credit: Dilbarjot and Michael Beck, Physics and Applied Computer Science , University of Winnipeg
- 21. Dark Energy Science Collaboration • Preparation for the arrival of the Rubin Observatory • Data Challenge 2: extreme-scale simulation of 300 sq degree patch of the sky over five years – 5 TB of data – ~90M core house at ALCF and NERSC • Data Portal based on Globus makes data accessible to collaborators 21
- 22. Federated Research Data Repository • National Research Data Management platform, where data can be – Ingested, curated, and preserved – Discovered, cited, and shared • Globus Services – Authentication – Transfer to repository service – Search for metadata catalog for data discovery (includes metadata from 70 other repositories) 22
- 23. Rebuilding A Kidney GPCR GUDMAP Synapse FaceBase ● DERIVA is an asset management platform for science used in various biomedical data repositories ● Globus Auth for authentication with external identities ● Globus groups for roles (e.g., curator, viewer, administrator) ● Globus Auth for desktop GUI and CLI DERIVA
- 24. 24 Data Provider Models / Functions API layer API layer Data Publishers Model Publishers Consumers Science! Increasing Data Interoperability & Reusability From foundry import Foundry f = Foundry() X,y = f.load(“dataset1”, v=“1.0”) y_pred = f.run(“model1”, v=“1.0”, X) f.data.publish(“./” “dataset1”, v=“1.1”) f.model.publish(“./” “model1”, v=“1.1”) • Models run locally or on distributed endpoints • Capabilities to pull datasets to desired location or move compute to desired location Dataset Function CH MaD • Radically reduce the energy barrier to access curated ML datasets and ML models • Facilitate reuse, meta-studies, benchmarking, and more • Long term implications for education NSF CSSI Started Oct. 2019 (Dane Morgan, Paul Voyles, Michael Ferris, Marcus Schwarting, Ben Blaiszik)
- 26. Enabled by the Globus data platform 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 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 Automating research workflows and ensuring those that need access to the data have it. 8 Personal Computer Transfer Share • Use a Web browser or platform services • Access any storage • Use an existing identity Build The Globus Command Line Interface, API sets, and Python SDK provide a platform… 6 … for building science gateways, portals and publication services. 7
- 28. Globus Search Globus Search • Scalable, secure search for research data • Features: – Metadata store with fine- grained visibility controls – Schema agnostic – Free text and faceted search – Integrated with Globus research platform (Auth, Groups) 28 Input form Extract Metadata Ingest metadata, set visibility polices Discovery POST /index/123 { "filters": [ { "field_name": "record_year", "values": ["2020"],"type": "match_all” }, { "field_name": "temp_farenheit", "values": [{"from": 90, "to": "*"}]"type": "range" ] } Query Bulk ingest
- 30. Globus Search • Documentation: docs.globus.org/api/search • SDK: globus-sdk-python.readthedocs.io • CLI: pypi.org/project/globus-search-cli • Sample code and walkthrough: docs.globus.org/api/search/guides/searchable_files 30
- 31. 31 Globus automation services Managed, secure and reliable task orchestration across heterogenous resources, with declarative language for composition, and extensible to plugin custom actions, supporting an event driven execution model, for automation at scale
- 32. Create and deploy flows 32 • Define the flow and deploy to Flows service • Uses declarative language (JSON or YAML) • Set policy: visibility, runnable by Action 1 Action 2 Action 3 Action 4 Action 1 Action 2 Choice Action 4 Action 5 Action 3
- 33. Start and manage runs 33 • An instance of Flow execution – Provide input parameter – Check status – Cancel • Set policy: monitor, manager • Triggers to start flows
- 34. Build action providers 34 • Action Provider is a service endpoint – Run – Status – Cancel – Release – Resume • Action Provider Toolkit action-provider- tools.readthedocs.io/en/latest Search Transfer Notification ACLs Identifier Delete Ingest User Form Describe Xtract funcX Web Form Custom built Globus Provided
- 35. Automation services ecosystem GET /provider_url/ POST /provider_url/run GET /provider_url/action_id/status GET /provider_url/action_id/cancel GET /provider_url/action_id/status Create Action Providers Define and deploy flows { “StartAt”: ”ToProject”, ”States” : { ”ToProject” : { … }, ”SetPermission” : { …}, “ProcessData” : { … } … }} Run flows
- 36. Example: CFDE 36 Data Coordinating Centers User Data Portal Deposit metadata Index for discovery Powered by Globus Auth, Groups & Flows Common Fund Data Ecosystem
- 37. Example: High-Performance Ptychography Workflows Funding Sources: ASCR, BES
- 38. Automation services • Documentation: docs.globus.org/globus-automation-services • CLI: globus-automate-client.readthedocs.io • Python SDK: globus-automate-client.readthedocs.io • Sample flows visible to all users 38
- 39. (Re)laying the foundation: GCSv5 39
- 40. Globus Connect Server v5 • Feature parity with v4 • Custom DNS names (e.g. data.university.edu) • Multi-factor authentication policy • Enhanced sharing policy • Containerized deployment 40
- 41. Fire-and forget transfers Data sharing with collaborators
- 42. Partnership with the community to develop new connectors Community Connector Program
- 43. Easy egress and ingress of data Data sharing with collaborators Publish data
- 44. POSIX Staging Connector • For POSIX file system that cache from tertiary storage • Custom plug-in for staging files • Example: – IBM Spectrum Scale plugin, Brock Palen at University of Michigan - github.com/brockpalen/ltfsee-globus 44
- 46. Globus Groups • Groups platform in production • Administrators can add users, in addition to invite • Membership policies simplified groups.api.globus.org/redoc 46
- 47. Transfer and Sharing • Skip files with not found errors – List of skipped files once task is completed • Fail tasks with quota errors • Scheduled and replicated transfers – Manage scheduled/repeated transfer and sync tasks – pypi.org/project/globus-timer-cli 47
- 48. Leveraging the Globus data platform… 48
- 49. APS XPCS: secure data discovery 49 Globus Auth Globus Groups Globus Search
- 50. APS XPCS: data access & preview 50 Globus Transfer HTTPS access
- 51. APS XPCS: automated processing & indexing 51 Globus Flows: Transfer, analysis, and ingest to search index
- 53. Globus Connect • Tools to migrate from v4 to v5 – Migration in phases (Q2 – Q3) – Goal: not require end user intervention • IPv6 support • Connectors – Azure Blob – Intel DAOS 53
- 54. IAM and Data platform • Support use cases that need higher task throughput • Enhancements to data permissions management • Improvements to consent management • Integration with NIH Researcher Auth Service • Search service for high assurance tier • Leverage Search for Globus resources 54
- 55. Automation platform • Lower the barrier for adoption – Web interfaces – Supporting tools/libraries – Action Providers for all Globus functionality • Exemplar flows for common use cases – Instrument data management – Data publication • Supported in high assurance tier 55
- 56. Clients • Streamline SDK/CLI to across services • Web App – Updated management console – Accessibility standards • Enhancements to sample portal – Open source, for customization and deployment – Flask, Django 56
- 58. Building the compute foundation for Globus 58
- 59. Requirements for reliable, scalable, remote computing Researcher needs to run a computation on a remote PC, cloud, supercomputer 1. Compute Compute Facility Collaborator wants to run their colleague’s computation on another system closer to their data 3. Share Instrument 5. Build Gateway and application developers want to add remote computation to their code 2. Specialize Researcher needs to move it to a new system or architecture to improve performance 4. Community Access Collaborators want to share access to a single allocation to run compute tasks
- 60. Function as a Service (FaaS) Developers work in terms of programming functions 1. Pick a runtime (e.g., Python) 2. Register function code 3. Run (and scale) Low latency, on-demand, elastic scaling, easy to deploy and update 60 def compute(input_args): # do something return results
- 61. funcX: managed and federated FaaS • Cloud-hosted service for managing compute • Register and share compute endpoints • Register and share Python functions • Reliably, scalable, securely execute functions on remote endpoints • Integrated with Globus Auth and data ecosystem 61 Try funcx on Binder https://funcx.org
- 62. Transform laptops, clusters, clouds into function serving endpoints • Python-based agent and pip installable locally or in Conda • Elastically provisions resources from local, cluster, or cloud system • Manages concurrent execution on provisioned resources • Optionally manages execution in Docker, Singularity, Shifter containers • Share endpoints with collaborators 62 $ pip install funcx-endpoint $ funcx-endpoint configure myep $ funcx-endpoint start myep
- 63. Register and share functions Create funcX client (and authn) 63 def compute(input_args): # do something return results def compute(input_args): # do something return results def compute(input_args): # do something return results Define and register Python function
- 64. Execute tasks on any accessible endpoint Select: function ID, endpoint ID, and input arguments Retrieve results asynchronously (funcX stores results in the cloud) 64 F(ep1,1) F(ep1, 2) F(ep1, 3) F(ep1, 4) F(ep1, 5) F(ep1, 6) F(ep2, 7)
- 66. Canonical research automation flow for instruments 69 Data Capture Data Analysis / Model in the Loop Publication Data Staging Metadata Extraction And Data Cataloging Data Staging Catalog Feedback Data Generation Examples • Serial X-Ray Crystallography • X-Ray Photon Correlated Spectroscopy • High energy diffraction microscopy • High throughput ptychography • High energy x-ray diffractions
- 67. Applying the Globus platform to science at the APS 70 Advanced Photon Source Key: funcX agent Globus Connect Theta Bebop Cluster Argonne Leadership Computing Facility Laboratory Computing Research Center Petrel store APS Computing Orthros Cluster APS DM system Portal server Portal server Cooley Action 1 Action 2 Action 3 Action 4
- 68. Example: Rapid Training of Deep Neural Networks using Remote Resources • DNN at the edge for fast processing, filtering, QC • Requires tight coupling with simulation and training with real-time data • Globus Flow: 71 Data Source HPC/DCAI Edge(Host) Globus, Automate Commands Status Data Model User Request Status Commands Status C/S Zhengchun Liu, Jana Thayar, et al. – Globus to rapidly move data for training – funcX for simulation and model training – Globus to move models to the edge – (Future) funcX for inference at the edge
- 70. Our Mission Increase the efficiency and effectiveness of researchers engaged in data-driven science and scholarship through sustainable software
- 71. 74 Active endpoints in over 70 countries
- 72. Adoption among R1 Institutions: 126 of 130 use Globus So, how are we doing? 75
- 73. Adoption among U.S. national laboratories So, how are we doing? 76
- 74. Notables… 77 BIG Movers 66.3 PB 2 Share 1,593 💛 Frequent Movers 887,000
- 75. Thank you, funders... U . S . D E P A R T M E N T O F ENERGY
- 77. Thank you to our Platinum sponsor!
- 78. Thank you Gold sponsors!
- 79. Thank you Gold sponsors!
- 80. Thank you Gold sponsors!
- 81. Thank you Gold sponsors!
- 82. Thank you Gold sponsors!
- 83. Thank you Gold sponsors!
- 84. Thank you Patron sponsors!
- 85. A word from our Platinum Sponsor Jordan Winkelman, Field Solutions CTO 89
- 86. Join us in Gather.Town • Get answers at the Globus Genius Bar • Visit the Sponsor Showcase • Joint the scavenger hunt in The Garden • Play a game bit.ly/globustown (passcode: globus) 90