Adaptive Computing onthe Grid Using AppLeSAdaptive Computing onthe Grid Using AppLeS
Francine Berman, Richard Wolski, Henri Casanova,Walfredo Cirne, Holly Dail, Marcio Faerman,Silvia Figueira, Jim Hayes, Graziano Obertelli,Jennifer Schopf, Gary Shao, Shava Smallen,Neil Spring, Alan Su, and Dmitrii Zagorodnov
IEEE Transactions on Parallel and Distributed Systems, Vol. 14, No. 5, May 2003
AgendaAgenda
•Introduction
•Problems
•AppLeS and its components
•Result products
•Related works
•Discussions
•Conclusions
IntroductionIntroduction
•What is a Grid?
–A collection of resources that can be used asan ensemble
•What are resources?
–Computational devices, networks, onlineinstruments, storage archives, and etc
ProblemsProblems
•Heterogeneity
–Different performance
•Inconsistentcy
–Shared
–Fail
–Upgraded
AppLeS ProjectAppLeS Project
•Application Level Scheduling
•Goals
–Investigate adaptive scheduling for Gridcomputing
–Apply research results to applications forvalidating the efficacy of the approach andextracting Grid performance for the end-user
StepsSteps
(6) ScheduleAdaptation
(1) ResourceDiscovery
(2) ResourceSelection
(3) ScheduleGeneration
(4) ScheduleSelection
(5) ApplicationExecution
Resource DiscoveryResource Discovery
•Depend on the Grid
–A List of user’s logins
–Resource discovery services of each Grid
Resource SelectionResource Selection
•Simple SARA
–Synthetic ApertureRadar Atlas
–Developed by JPL andSDSC
–Provide access tosatellite imagesdistributed in variousrepositories
–End-to-end availablebandwidth is predictedusing NWS
Performance ModelingPerformance Modeling
•Jacobi 2D
•Main loop
–Loop until convergence
–For all matrix entriesAi,j
•Ai,j = ¼(Ai,j + Ai+1,j + Ai-1,j+ Ai,j+1 + Ai,j-1)
–Compute local error
•Model
–Ti = Areai * Operi *AvailCPUi + Ci ; 1 <= I<= p
i,j
i-1,j
i+1,j
i,j-1
i,j+1
Area - the size of the strip, Oper - execution time to compute one entry
AvailCPU - percentage of available CPU, C - Communication time
Scheduling GenerationScheduling Generation
•Complib
–A computational biology application
–Compare a library of unknown sequencesagainst a database of “known” sequencesusing FASTA scoring method
•Parallization
–Master/Worker
–Work size
•Small unit size (Self-scheduling) - high overhead
•Big unit size - load imbalance
AppLeS’s ApprochAppLeS’s Approch
Scheduling AdaptationScheduling Adaptation
•MCell
–A computationalneuroscienceapplication
–Study biochemicalinteractions withinliving cells at molecularlevel
–Multiple independenttasks
–Shared input
XSufferageXSufferage
•Based on Sufferage
•Sufferage value =second best - firstbest
•XSufferage concernsdata replication time(zero for locallyavailable)
OutcomeOutcome
•APST - AppLeS Parameter SweepTemplate
•AMWAT - AppLeS Master/WorkerApplication Template
•SA - Supercomputer AppLeS
APSTAPST
•Parameter SweepApplications
–Mostly independent
•Provide
–Transparent deployment
–Automatic scheduling
•Capabilities
–Launching tasks
–Moving and storing data
–Discovering and monitoringresources
AMWATAMWAT
•Master/Worker
•Provide
–APIs for
•Discovering
•Scheduling
•Predicting
SS - Self-Scheduling
FSC - Fixed Size Chunking
GSS - Guided Self-Schduling
TSS - Trapezoidal Self-Scheduling
FAC2 - Factoring
SASA
•Space-shared
•Moldable jobs
•Reduce responsetimes
Related WorksRelated Works
•Environment
–MARS and Dome - Run-time checkpointing environment
•Structure
–MARS - SPMD
–VDCE and SEA - Task graph
–IOS - Real-time, fine-grained, task graph
–Dome and SPP - Abstract language
•Dome - SPMD
•SPP - Task graph
•Performance model
–Depend on program structure
•Objective
–Minimize execution time
Related WorksRelated Works
Env
Struct
Perf
Approach
AppLeS
Any
Any
Provided
Adaptive
MARS
ChkPnt
SPMD
Statistics
Data Dist
Dome
ChkPnt
SPMD
Data Dist
Data Dist
VDCE
TG
Derived
List Sched
SPP
TG
Derived
SEA
TG
Data Flow
Expert Sys
IOS
TG
Derived
GA
GrADS
DiscussionsDiscussions
•Performance of distributed applicationsdepend on both application and platform-specific information
•Storage and service are usually separated
•Communication must be concerned in themodel
•Multi-applications environment has notbeen addressed
ConclusionsConclusions
•AppLeS
–An application-level scheduling framework
–Provide adaptive, flexible, and reusablecomponents
–being integrated into GrADS for building nextgeneration Grid applications
•Each part has been demonstrated itsimprovement