MACCCR 5th Fuels Research ReviewSeptember 17, 2012

Michael Frenklach

Supported by AFOSR

PrIMe Next Frontier: Large, Multi-dimensional Data Sets

OUTLINE• PrIMe Cloud Infrastructure:

‒Data Flow Network‒Remote Server: PrIMe-RMG‒Interfaces‒Big Data

• Other new developments:‒Species identification app‒UQ: Statistical sampling of the feasible set

. . .

• PrIMe with Humanities

PrIMeProcess Informatics Model

INFRASTRUCTURE FOR UQ-PREDICTIVE MODELING

http://primekinetics.org

Data sharing

App sharing

Automation

PrIMe

Portal

Assess to distributed resourcesUser authorizationSocial networking

User forumsData evaluation panels

Help, tutorials, examples

Customized Drupal (PHP)

platform independent

Workflow

“Browser-based” softwareUser building projects

Data/app linkingBinary XML interfaces

Remote-server supportProject sharing

C#, Windows, IEapps: C#, Matlab

Warehouse

Data collectionsModels and ExperimentsControlled by schemas

Submission formsMultiple-mode access

WebDAVXML

domain 2 web page

Present-day Science Sharing:via web-page access

database

database

apps

apps

Internet

domain 1 web page

science domain 2

database

database

apps

apps

Internet

science domain 1

PrIMe Science Sharing:via web-service data/app

access

science domain 2

database

database

apps

apps

Internet

science domain 1

PrIMe Science Sharing:via web-service data/app

access

clientweb service data

flownetwork

clientworkflowapp

Initial Model:“Upload your data to PrIMe Warehouse” (“give me your data”)

New, Distributed Model:“You may, if choose, connect your data to the communal system”

• with a switch in the OFF position: “you can use the communal data and tools but your own data is private to you only”

• “but please flip the switch to the ON position when you are ready to share your own data”

PRIME DATA MODEL

“Connect your code to the communal system”- you control your own code:

• release version• user access, licenses• collect fees, if desired

SAME FOR APPS

TECHNOLOGY: HOW

Remote server app—PrIMe Web Services (PWS)• no restrictions on platform• no restrictions on data formats• no restrictions on local programming language(s)

PrIMe Workflow Interface (PWI) is the only “standard”• developed, maintained, and controlled by the community

client machine

client data

PrIMe web services

PrIMe Data Flow Network

PrIMe Dispatcher

BIG DATA

excessively large data sets• do not move the data

• but use “smart agents” (eg, HTML5 walkers)

web services with user-reloaded tasks:fetch data features for user-requested analysis

PrIMe remote-server webservices

PRIME REMOTE-SERVER WEBSERVICES

•Created ~2 years ago‒ installed by professional programmers‒ implemented on Reaction Design site

•Modified June 2012‒ can be installed by users‒ implemented with RMG at MIT site‒ installed by first-year grad students!

installation manual

PRIME – RMG• User creates a PrIMe Workflow

(PWA) project

• User submits a request: “create a reaction model for …”

• The request activates RMG code at MIT server

• User receives email when the model is generated

• User retrieves the model or it “moves” along the PWA project to the next component

PRIME INTERFACES

client machine

client data

PrIMe web services

binary XML – HDF5e.g., reaction model: GRI-Mech 3.0

• input data for UQ bypassing Warehouse

• species identification via crowd-sourcing

• UQ: sampling within the feasible region

• comparison between interval-to-interval UQ and rigorous Bayesian

• parallelization of Chemkin II

NEW DEVELOPMENTS

UPLOAD YOUR OWN DATASET TO RUN UQ

SPECIES IDENTIFICATION BY CROWD-SOURCING

SPECIES IDENTIFICATION BY CROWD-SOURCING

DATACOLLABORATION: BOUNDS-TO-BOUNDS PREDICTIONSCONSTRAINED TO THE FEASIBLE SET

expe

rim

enta

l un

cert

aint

y

M(x1,x2)

Ffeasible set

experiment/theory constrain feasible set

prior knowledge

FEASIBLE SET SAMPLING

PREDICTION ON THE FEASIBLE SET

COMPARISON BETWEEN BOUNDS-TO-BOUNDS UQ (DATACOLLABORATION)

ANDRIGOROUS BAYESIAN

An ongoing collaborative study withJerome Sacks, National Institute of Statistical SciencesRui Paulo, ISEG Technical University of LisbonGonzalo Garcia-Donato, Universidad de Castilla-La Mancha

Bayesian simulations:• no simplifying assumptions,• but utilize the Solution Mapping strategy for numerical efficiency

PARALLELIZATION: CHEMKIN IIExecution time of flame simulations with a large acetylene model

0 2 4 6 8 10 1250

100

150

200

250

300

350

Number of Threads

Tim

e (s

)

Execution time of Parallel PREMIX for Large Acetylene Model

PARALLELIZATION: CHEMKIN IIExecution time of flame simulations with a hydrogen model

0 2 4 6 8 10 1230

40

50

60

70

80

90

100

Number of Threads

Tim

e (s

)Execution time of Parallel PREMIX for Small Hydrgen Model

KNOWLEDGE UNIX A collaborative project of PrIMe with Humanities:

• Berkeley Electronic Cultural Atlas Initiative

“Study of Buddhist Texts”PrIMe is used to predict the past

The abstracted dots represent 166000 “panes”

KNOWLEDGE UNIX A collaborative project of PrIMe with Humanities:

• Berkeley Electronic Cultural Atlas Initiative• Berkeley Institute of Information: “Editors Notes”

Current and Next• Remote-server app and new apps

−RMG: interface (with MIT, Bill Green)

−Communal/User tools: Cantera (with NCSU, Phil Westmoreland)

−Big Data: feature collection for UQ (with Utah, Phil Smith)

• Enabling new science infrastucture−ALS-data analysis (with NCSU; Phil

Westmoreland)

−Species IDs (with Kaust; Mani Sarathy)

−H2-O2: automation/addition of flame targets (with Tsinghua, Xiaoqing You)

−Submission of Chemkin mechanisms (with Kaust and Tsinghua)