adaptive computing on the grid using apples
DESCRIPTION
Adaptive Computing on the 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. - PowerPoint PPT PresentationTRANSCRIPT
Adaptive Computing on Adaptive Computing on the Grid Using AppLeSthe 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 as
an ensemble
• What are resources?– Computational devices, networks, online
instruments, storage archives, and etc
ProblemsProblems
• Heterogeneity– Different performance
• Inconsistentcy– Shared– Fail– Upgraded
AppLeS ProjectAppLeS Project
• Application Level Scheduling
• Goals– Investigate adaptive scheduling for Grid
computing– Apply research results to applications for
validating the efficacy of the approach and extracting 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 Aperture
Radar Atlas– Developed by JPL and
SDSC– Provide access to
satellite images distributed in various repositories
– End-to-end available bandwidth is predicted using NWS
Performance ModelingPerformance Modeling
• Jacobi 2D• Main loop
– Loop until convergence– For all matrix entries
Ai,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,ji-1,j i+1,j
i,j-1
i,j+1
Area - the size of the strip, Oper - execution time to compute one entryAvailCPU - percentage of available CPU, C - Communication time
Scheduling GenerationScheduling Generation
• Complib– A computational biology application– Compare a library of unknown sequences
against a database of “known” sequences using 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 computational
neuroscience application
– Study biochemical interactions within living cells at molecular level
– Multiple independent tasks
– Shared input
XSufferageXSufferage
• Based on Sufferage• Sufferage value =
second best - first best
• XSufferage concerns data replication time (zero for locally available)
OutcomeOutcome
• APST - AppLeS Parameter Sweep Template
• AMWAT - AppLeS Master/Worker Application Template
• SA - Supercomputer AppLeS
APSTAPST
• Parameter Sweep Applications– Mostly independent
• Provide– Transparent deployment– Automatic scheduling
• Capabilities– Launching tasks– Moving and storing data– Discovering and monitoring
resources
AMWATAMWAT
• Master/Worker• Provide
– APIs for• Discovering• Scheduling• Predicting
SS - Self-SchedulingFSC - Fixed Size ChunkingGSS - Guided Self-SchdulingTSS - Trapezoidal Self-SchedulingFAC2 - Factoring
SASA
• Space-shared• Moldable jobs• Reduce response
times
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 applications depend on both application and platform-specific information
• Storage and service are usually separated
• Communication must be concerned in the model
• Multi-applications environment has not been addressed
ConclusionsConclusions
• AppLeS– An application-level scheduling framework– Provide adaptive, flexible, and reusable
components– being integrated into GrADS for building next
generation Grid applications
• Each part has been demonstrated its improvement