Unleash your inner (data) scientist :The ability and audacity to scale your science with

extensible cyberinfrastructure

Nirav MerchantThe University of Arizona &iPlant Collaborativenirav@email.arizona.edu

Topic Coverage

• The “Big Data” and “Data Scientist” wave• What is cyberinfrastructure (CI)• Delivering pragmatic CI ecosystem• What has the community built with our CI• Lifecycle of research and innovation • Continuing education and learning with CI• Future thoughts and challenges

Science Paradigms1. Thousand years ago: science was empirical

describing natural phenomena, observations2. Last few hundred years: theoretical branch

using models, generalizations3. Last few decades: a computational branch

simulating complex phenomena4. Today: data exploration (eScience)

unify theory, experiment, and simulation

Based on the transcript of a talk given by the late Jim Grayto the National Research Council – Computer Science and Telecommunication Board in Mountain View, CA, on January 11, 20073

The Fourth Paradigm: Data-Intensive Scientific Discovery

• Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets.

• The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud computing technologies.

http://research.microsoft.com/en-us/collaboration/fourthparadigm/4

The Discovery Lifecycle

The Fourth Paradigm: Data-Intensive Scientific Discovery5

Evolution of X-Info• The evolution of X-Info and Comp-X for each discipline X e.g.

(Bio-Informatics , Computational-Biology)• How to codify and represent our knowledge• The Generic Problems:

• How to share it with others• Query and Vis tools• Building and executing models• Integrating data and literature• Documenting experiments• Curation and long-term preservation

• Data ingest• Managing a petabyte• Common schema• How to organize it• How to reorganize it

The Fourth Paradigm: Data-Intensive Scientific Discovery6

•Classic paradigm: You produce data, analyze, interpret (end to end)

•Conventional paradigm: Consortium/centers produce data and you consume it

•New Paradigm: Consortium/centers have produced data and creating “cyber infrastructure” to tackle the “grand challenge”

Paradigm Shift

7

∧

8

Big Data

• Extracting meaningful results from vast amount of data (linked data)• Big data “information assets” demand cost-effective, innovative

forms of information processing for enhanced insight and decision making.

• “Big Data” Is only the Beginning of Extreme Information Management

• Big Data Technology, all Is Not New

Attributed to Gartner Consulting 9

A few word about “Big Data” and “Data Science”The 2014 Gartner Technology Hype-Cyclehttp://www.gartner.com/newsroom/id/2819918

+ =

Simple Formula for Success

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The Reality

+ +

• Excel, R • PERL • Python • ARCGIS• Java Ruby• Fortran C C#

C++ Matlab• etc.

• Amazon• Azure• Rackspace• Campus HPC• XSEDE• Etc.

and lots of glue…..12

+ =

Simple Formula

http://cloudtweaks.com/2011/05/the-lighter-side-of-the-cloud-data-transfer/

Rise of the “data janitors”

15

The relevance• Bioinformatics has become too central to biology to

be left to specialist bioinformaticians.• Biologists are all bioinformaticians now

- Lincoln Stein Dec. 2008

http://genomebiology.com/2008/9/12/114

iPlant Collaborative: Vision

www.iPlantCollaborative.org

Enable life science researchers and educators touse and extend cyberinfrastructure

The iPlant CollaborativeWe are a Cyberinfrastructure

Platforms, tools, datasets Storage and compute Training and support

From data to discovery

The iPlant CollaborativeAnd a virtual organization

• Developer Expertise• Computational Capacity• Science Domain Expertise • Training• Administrative and Organization

• Facilitating the 4A’s of “Computational Thinking” approaches for Life Sciences: Abstraction, Automation, Ability and Audacity

• Allowing researchers and educators to establish and manage data driven collaborations: Supporting distributed teams and virtual organizations (VO) at global scale

• Making efficient and coordinated use of CI resources from national, regional, institutional and commercial providers: NSF XSEDE, iPlant, campus HPC and high bandwidth connections to commercial cloud providers

• Adopting best practices from science domains where key CI challenges have been solved: Astronomy, Particle Physics etc.

• Community driven, self-provisioning, extensible and open source: Development and prioritization driven through community engagement, active engagement with CISE communities

iPlant Collaborative: CI for Scalable Science

iPlant Collaborative: Platform Philosophy• Strive to provide the CI Lego blocks• Danish 'leg godt' - 'play well’• Also translates as 'I put together' in Latin• If desired functionality is not available, the

community can craft their own by using andextending iPlant CI components (like lego blocks)

• Through these extensible and customizedplatforms create a ecosystem of interoperabletools that benefit the broad community (and notfew lab groups)

• Provide the tools to allow community to managetheir digital assets (cloud, HPC etc.)

• Improve Computational Productivity

Who did we build it for ?

iPlant: Platform for Big Data Collaborations

Ready to usePlatforms

FoundationalCapabilities

Established CI Components

Extensible Services

Eas

e of

use

iPlant Collaborative: Products

iPlant: Cohesive Platform for Big Data lifecycle

Researchers like to share !• User Statistics

• ~27000 user accounts• 4900 users with data• 2600 users (53% of users with data) made at least 1 share • 2100 shares per user• 42 million files (58% shared)• 59 million (1.1 million/month) shares

• Community Data Statistics• 5 million files• 55 million (1.0 million/month) shares

• ~1.1PB of User Managed data• Our users consume 5M+ SU annually and more

(we graduate them to compete for their own allocations from XSEDE)

How is it being used ?

• User build their own systems (powered by iPlant components) but managed by them

• Consume specific components (a la carte, data store, Atmosphere)• Directly use applications (DE)• Custom design appliances (Atmosphere)• Publish their findings (PNAS, Nature)• Advocate use• Create learning material and courses

• Many 1000’s omes projectmanage their data & analysis

• Execute large scale workflows(25-50TB data , Million+ CPUhours)

• Data infrastructure tocoordinate digitization effortsfor multiple sites

• Sharing, Visualizing (3D) &Analyzing high resolutionmicroscopy images (40K x40K) via web browser

• Learning material, new coursework, custom applications

iPlant CI: What is the community building ?

And it goes way beyond plants and life science

• Partnership with SoftwareCarpentry and Data Carpentry toprovide best practices necessaryto make efficient use of CI

• Allowing individual researchersand educators to utilize data andcomputational infrastructure atscale (and encounter realchallenges)

• Community contributed material(built on iPlant CI)

iPlant Collaborative: Training data scientists

Applied Cyberinfrastructure Concepts (ACIC) • Semester long project based learning course: introduces fundamental

concepts, tools and resources for effectively managing common tasks associated with analyzing large datasets.

• Graduate + Undergraduate course working on a REAL research workflows where scalability is a bottleneck

• Provide familiarity with cyberinfrastrucutre (CI) resources available at the University of Arizona campus, iPlant Collaborative, NSF XSEDE centers, Cloud (Future Grid and commercial providers such as Amazon).

• Learning to apply relevant CI skills (for final project) and developing wiki based documentation of these best practices.

• Learning how to effectively collaborate in interdisciplinary team settings.• Deliver a functional solution to the stakeholder

From research question to reality

Why is it valuable ?

• Users are able to over come data and computational bottle necks• Share data of ANY size with ANYONE• Connect data and compute on single platform • Manage their data and computations regardless of scale • Build their own apps and solutions (create their own community

iAnimal, iVirome)• Create custom appliances

iPlant: What worked• All major CI components have seen steady adoption (few

exception)• “Think tank to do tank” transition was rapid• Evolved to a technology proving ground• Take research products (NSF funded) to production use for our

community• Running infrastructure is not fun, building is. Allowing people to

focus on science (while stream line CI)

iPlant: What worked• Evolution of training (software carpentry)• Sharing/collaboration• Give people exit strategy (options) and they are happy adopt

solution• Provide feedback to CI component creators to improve (usability)• Expectation management: Do not expect the same experience

(cable cord cutting v/s netflix/hulu)

What did not work• Managing distributed teams is harder in VO (load balancing,

enthusiasm etc)• Technology lifecycle is not synchronized across all products• Relying on multiple providers for solution is challenging

(downtimes) • Changing/Evolving needs of community are hard to predict • Growth of users out paces our cloud capabilities (see tweets)

Even the tech geeks notice

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Luck favors the braveAnalysis favors the organized