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Large-Scale Science Through Workflow
Management
Ewa Deelman
Center for Grid Technologies
USC Information Sciences Institute
Acknowledgements
Ewa Deelman, Carl Kesselman, Gaurang Mehta, Gurmeet Singh, Mei-Hui Su, Karan Vahi (Center for Grid Technologies, ISI)
James Blythe, Yolanda Gil (Intelligent Systems Division, ISI)
http://pegasus.isi.edu Research funded as part of the NSF
GriPhyN, NVO and SCEC projects and EU-funded GridLab
Today’s Scientific Applications
Increasing in the level of complexity Use of individual application components Reuse of individual intermediate data products (files) Description of Data Products using Metadata Attributes
Execution environment is complex and very dynamic Resources come and go Data is replicated Components can be found at various locations or staged in on demand
Separation between the application description the actual execution description
Workflow Definitions
Workflow template: shows the main steps in the scientific analysis and their dependencies without specifying particular data products
Abstract workflow: depicts the scientific analysis including the data used and generated, but does not include information about the resources needed for execution
Concrete workflow: an executable workflow that includes details of the execution environment
Scientific AnalysisW
orkf
low
Evo
lutio
n Select the Input Data
Map the Workflow onto Available Resources
Execute the Workflow
Construct the Analysis
Workflow Template
Abstract Worfklow
Concrete Workflow
Tasks to be executed
Grid Resources
Execution EnvironmentScientific AnalysisW
orkf
low
Evo
lutio
n
Grid Resources
Select the Input Data
Map the Workflow onto Available Resources
Execute the Workflow
Information Services
Library of Application
Components
Data Catalogs
Construct the Analysis
Resource availability and characteristics
Tasks to be executed
Data properties
Component characteristics
Workflow Template
Abstract Worfklow
Concrete Workflow
Aut
omat
edU
ser
guid
ed
Concrete Workflow Generation and Mapping
Input Data Selector
Compositional Analysis Tool
(CAT)
PegasusCondor
DAGManConcrete Workflow
Results
Workflow Template
Chimera
MontageAbstract Workflow Service
Abstract Workflow
Grid Resourcesjobs
Application-dependent
Application independent
Pegasus:Planning for Execution in Grids
Maps from abstract to concrete workflow Algorithmic and AI-based techniques
Automatically locates physical locations for both workflow components and data
Finds appropriate resources to execute Reuses existing data products where applicable Publishes newly derived data products
Provides provenance information
Generating a Concrete Workflow
Information location of files and component
Instances State of the Grid resources
Select specific Resources Files Add jobs required to form a concrete
workflow that can be executed in the Grid environment
Data movement Data registration Each component in the abstract
workflow is turned into an executable job
FFT filea
/usr/local/bin/fft /home/file1
Move filea from host1://home/filea
to host2://home/file1
Abstract Workflow
Concrete Workflow
DataTransfer
Data Registration
Information Components used by Pegasus
Globus Monitoring and Discovery Service (MDS) Locates available resources Finds resource properties
Dynamic: load, queue length Static: location of GridFTP server, RLS, etc
Globus Replica Location Service Locates data that may be replicated Registers new data products
Transformation Catalog Locates installed executables
Example Workflow Reduction
Original abstract workflow
If “b” already exists (as determined by query to the RLS), the workflow can be reduced
d1 d2ba c
d2
b c
Mapping from abstract to concrete
Query RLS, MDS, and TC, schedule computation and data movement
Execute d2 at B
Move b from A
to B
Move c from B
to U
Register c in the
RLS
d2
b c
Pegasus Research
resource discovery and assessment resource selection resource provisioning workflow restructuring
task merged together or reordered to improve overall performance
adaptive computing Workflow refinement adapts to changing
execution environment
Benefits of the workflow & Pegasus approach
The workflow exposes the structure of the application maximum parallelism of the application
Pegasus can take advantage of the structure to Set a planning horizon (how far into the workflow to plan) Cluster a set of workflow nodes to be executed as one (for
performance)
Pegasus shields from the Grid details
Benefits of the workflow & Pegasus approach
Pegasus can run the workflow on a variety of resources Pegasus can run a single workflow across multiple
resources Pegasus can opportunistically take advantage of
available resources (through dynamic workflow mapping) Pegasus can take advantage of pre-existing intermediate
data products Pegasus can improve the performance of the
application.
Mosaic of M42 created on the Teragrid resources using Pegasus
Pegasus improved the runtime of this application by 90% over the baseline case
Bruce Berriman, John Good (Caltech)
Joe Jacob, Dan Katz(JPL)
Future Directions
Support for workflows with real-time feedback to scientists. Providing intermediate analysis results so that the experimental setup can be adjusted while the short-lived samples or human subjects are available.
time
Levels ofabstraction
Application-level
knowledge
Logicaltasks
Tasksbound toresources
and sent forexecution
User’sRequest
Relevantcomponents
Fullabstractworkflow
Partialexecution
Not yetexecuted
executed
Workflow refinement
Onto-basedMatchmaker
Workflow repair
Policyreasoner
Cognitive Grids: Distributed Intelligent Reasoners that Incrementally Generate the Workflow
BLAST: set of sequence comparison algorithms that are used
to search sequence databases for optimal local alignments to a query
Lead by Veronika Nefedova (ANL) as part of the Paci Data Quest Expedition program
2 major runs were performed using Chimera and Pegasus:
1) 60 genomes (4,000 sequences each), In 24 hours processed Genomes selected
from DOE-sponsored sequencing projects67 CPU-days of processing time
delivered~ 10,000 Grid jobs>200,000 BLAST executions50 GB of data generated
2) 450 genomes processed
Speedup of 5-20 times were achieved because the compute nodes we used efficiently by keeping the submission of the jobs to the compute cluster constant.
Tomography (NIH-funded project) Derivation of 3D structure from a
series of 2D electron microscopic projection images,
Reconstruction and detailed structural analysis complex structures like synapses large structures like dendritic
spines. Acquisition and generation of huge
amounts of data Large amount of state-of-the-art
image processing required to segment structures from extraneous background.
Dendrite structure to be rendered byTomography
Work performed with Mark Ellisman, Steve Peltier, Abel Lin, Thomas Molina (SDSC)
LIGO’s pulsar search at SC 2002
The pulsar search conducted at SC 2002 Used LIGO’s data collected
during the first scientific run of the instrument
Targeted a set of 1000 locations of known pulsar as well as random locations in the sky
Results of the analysis were be published via LDAS (LIGO Data Analysis System) to the LIGO Scientific Collaboration
performed using LDAS and compute and storage resources at Caltech, University of Southern California, University of Wisconsin Milwaukee.
ISI people involved: Gaurang Mehta, Sonal Patil, Srividya Rao, Gurmeet Singh, Karan VahiVisualization by Marcus Thiebaux
Southern California Earthquake Center
• Southern California Earthquake Center (SCEC), in collaboration with the USC Information Sciences Institute, San Diego Supercomputer Center, the Incorporated Research Institutions for Seismology, and the U.S. Geological Survey, is developing the Southern California Earthquake Center Community Modeling Environment (SCEC/CME).
•Create fully three-dimensional (3D) simulations of fault-system dynamics.
•Physics-based simulations can potentially provide enormous practical benefits for assessing and mitigating earthquake risks through Seismic Hazard Analysis (SHA).
•The SCEC/CME system is an integrated geophysical simulation modeling framework that automates the process of selecting, configuring, and executing models of earthquake systems.
Figure 1: Fréchet sensitivity Kernel showing travel path between a Yorba Linda earthquake and the TriNet Station DLA.
Acknowledgments :
Philip Maechling and Vipin Gupta
University Of Southern California