workflow-driven science using kepler

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Workflow-Driven Science using Kepler. Ilkay Altintas, PhD San Diego Supercomputer Center, UCSD altintas@sdsc.edu. words.sdsc.edu. Scientific Workflow-Driven Science. Accelerate Workflow Design and Reuse via a Drag-and-Drop Visual Interface. Analyze Results. Facilitate Sharing. - PowerPoint PPT Presentation

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Workflow-Driven Science using Kepler

Ilkay Altintas, PhDSan Diego Supercomputer Center, UCSD

altintas@sdsc.edu

words.sdsc.edu

Scientific Workflow-Driven Science

• Experiment-oriented workflow notebook

• Moving large-scale data efficiently

• Building multi-scale workflows that enable large scale model assembly

• Tracking provenance for reproducibility

Workflow Design

Reporting

Workflow Monitoring

Workflow Execution

Workflow Scheduling

and Execution Planning

Run Review

Provenance Analysis

Deploy and

Publish

Accelerate Workflow Design and Reuse via a Drag-and-Drop Visual Interface

Facilitate Sharing

Schedule, Run and Monitor Workflow Execution

Analyze Results

Support for end-to-end computational scientific process

BUILD SHARE RUN LEARN

Kepler is a Scientific Workflow System

Ptolemy II: A laboratory for investigating design

KEPLER: A problem-solving environment for Scientific Workflow

KEPLER = “Ptolemy II + X” for Scientific Workflows

• A cross-project collaboration

… initiated August 2003… 2.4 released 04/2013• Builds upon the open-source Ptolemy II framework

www.kepler-project.org

A Typical Kepler Workflow

A green box is called an ‘actor’ , which performs a task.

This special actor represents an annotation component, such as BLAST search.

Workflow parameters, which can be specified by users in the portal, are passed to workflow components.

Data flow is divided.

Kepler is a Team Effort

Ptolemy II

NIMROD/K

Full list of contributors, projects, individuals and funding info are at the Kepler website!!

Cross-project collaboration

Initiated August 2003

Kepler 2.4 release: April, 2013

Data Science Workflows in Kepler- Programmable Scalability -

• Access and query data• Scale computational analysis• Increase reuse • Save time, energy and money• Formalize and standardize

Real-Time Hazards Managementwifire.ucsd.edu

Data-Parallel BioinformaticsbioKepler.org

Scalable Automated Molecular Dynamics and Drug Discoverynbcr.ucsd.edu

kepler-project.org words.sdsc.edu

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