prophesy: analysis and modeling of parallel and distributed applications valerie taylor texas...
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Prophesy: Analysis and Modeling of Parallel Prophesy: Analysis and Modeling of Parallel and Distributed Applicationsand Distributed Applications
Valerie TaylorValerie TaylorTexas A&M UniversityTexas A&M University
Seung-Hye Jang, Mieke Prajugo, Xingfu Wu – TAMUSeung-Hye Jang, Mieke Prajugo, Xingfu Wu – TAMUEwa Deelman – ISI Ewa Deelman – ISI
Juan Gilbert – Auburn UniversityJuan Gilbert – Auburn UniversityRick Stevens – Argonne National LaboratoryRick Stevens – Argonne National Laboratory
SPONSORS: NSF, NASASPONSORS: NSF, NASA
http://prophesy.cs.tamu.eduhttp://prophesy.cs.tamu.edu22
Performance ModelingPerformance Modeling
Necessary for good performanceNecessary for good performance Requires significant time and effortRequires significant time and effort
MD Code Throughput
0
1
2
3
4
1 9 25 49 81 121
Number of Processors
Tim
este
ps/s
Exper.
Theo.
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OutlineOutlineOutlineOutline
Prophesy InfrastructureProphesy Infrastructure
Modeling TechniquesModeling Techniques
Case StudiesCase Studies
SummarySummary
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Problem StatementProblem StatementProblem StatementProblem Statement
Given:Given:• Performance models and analyses are criticalPerformance models and analyses are critical
– Requires significant development timeRequires significant development time
• Parallel and distributed systems are complexParallel and distributed systems are complex GoalGoal
Efficient execution of parallel & distributed Efficient execution of parallel & distributed applicationsapplications
Proposed SolutionProposed Solution• Automate as much as possibleAutomate as much as possible• Community involvementCommunity involvement
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Prophesy SystemProphesy SystemPROPHESY GUI
Profiling &
Instrument.
Actual
Execution
Performance Database
TemplateDatabase
SystemsDatabase
ModelBuilder
PerformancePredictor
DATACOLLECTION
DATABASES DATAANALYSIS
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Automated InstrumentationAutomated Instrumentation In-line data collectionIn-line data collection Instrument at one of several pre-Instrument at one of several pre-
defined levelsdefined levels Allow for user-specified Allow for user-specified
instrumentationinstrumentation
Profiling &
Instrument.
Actual
ExecutionT=E * f;T=E * f;for (I=1; I<N; I++){for (I=1; I<N; I++){ V(I) = A(I) * C(I);V(I) = A(I) * C(I); B(I) = A(2I + 4);B(I) = A(2I + 4);}}
T=E * f;T=E * f;INSTRUMENTATION CODEINSTRUMENTATION CODEfor (I=1; I<N; I++){for (I=1; I<N; I++){ V(I) = A(I) * C(I);V(I) = A(I) * C(I); B(I) = A(2I + 4);B(I) = A(2I + 4);}}INSTRUMENTATION CODEINSTRUMENTATION CODE
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DatabasesDatabases
Hierarchical organizationHierarchical organization Organized into 4 areas:Organized into 4 areas:
• ApplicationApplication• ExecutableExecutable• RunRun• Performance StatisticsPerformance Statistics
Performance Database
TemplateDatabase
SystemsDatabase
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Prophesy DatabaseProphesy Database
Application Executable RunApplication
Performance
Modules
Function Performance
Basic Unit Performance
Data Structure Performance
Inputs
Systems
Resource Connection
Functions
Module_Info
Control Flow
Compilers
Model Template Function_Info
LibraryModel_Info
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Data AnalysisData Analysis
Develop performance Develop performance modelsmodels
Make predictionsMake predictions Performance tune codesPerformance tune codes Identify best Identify best
implementation implementation Identify trendsIdentify trends
ModelBuilder
PerformancePredictor
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Automated Modeling TechniquesAutomated Modeling Techniques Utilize information in the template and Utilize information in the template and
system databasessystem databases Currently include three techniquesCurrently include three techniques
• Curve fittingCurve fitting• ParameterizationParameterization• Composition using coupling valuesComposition using coupling values
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Curve Fitting: UsageCurve Fitting: UsageCurve Fitting: UsageCurve Fitting: Usage
Application Performance
Function Performance
Basic Unit Performance
Data Structure
Performance
Model Template
Matrix-matrix multiply: Matrix-matrix multiply: LSF : 3LSF : 3
PerformancePerformanceDataData
Analytical EquationAnalytical Equation(Octave: LSF)(Octave: LSF)
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Matrix-matrix multiplication, 16P, IBM SPMatrix-matrix multiplication, 16P, IBM SP
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Parameterization: UsageParameterization: UsageParameterization: UsageParameterization: Usage
Model Template
Matrix-matrix multiply: Matrix-matrix multiply: Parameterization : Parameterization : Parameter(P, SGI Origin2000, N, ADDM, Parameter(P, SGI Origin2000, N, ADDM,
MPISR, MPIBC)MPISR, MPIBC)
Analytical EquationAnalytical Equation(Octave: Parameterization)(Octave: Parameterization)
Systems
Resource Connection
System Data:System Data:MPISR, MPIBC, ADDMMPISR, MPIBC, ADDM
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Modeling TechniquesModeling Techniques Curve FittingCurve Fitting
• Easy to generate the modelEasy to generate the model• Very few exposed parametersVery few exposed parameters
ParameterizationParameterization• Requires one-time manual analysisRequires one-time manual analysis• Exposes many parametersExposes many parameters• Explore different system scenariosExplore different system scenarios
Coupling Coupling • Builds upon previous techniquesBuilds upon previous techniques• Identify how to combine kernel modelsIdentify how to combine kernel models
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Kernel CouplingKernel Coupling
Two kernels (i & j)Two kernels (i & j) Three measurementsThree measurements
• PPii: performance of kernel i isolated: performance of kernel i isolated
• PPjj: performance of kernel j isolated: performance of kernel j isolated
• PPijij: performance of kernels i & j coupled: performance of kernels i & j coupled
Compute CCompute Cijij = = PjPi
Pij
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Coupling CategoriesCoupling Categories
CCijij = 1: no coupling = 1: no coupling
CCijij > 1: destructive coupling > 1: destructive coupling
CCijij < 1: constructive coupling < 1: constructive coupling
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Coupling CategoriesCoupling Categories
SharedSharedResourceResource
CCijij = 1: No Coupling = 1: No Coupling
CCijij < 1: Constructive Coupling < 1: Constructive CouplingCCijij > 1: Destructive Coupling > 1: Destructive Coupling
Kernel AKernel A Kernel BKernel B
SharedSharedResourceResource
Kernel AKernel A Kernel BKernel B
SharedSharedResourceResource
Kernel AKernel A Kernel BKernel B
Kernel AKernel A
Kernel BKernel B
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Using Coupling ParametersUsing Coupling Parameters Use weighted averages to determine how to Use weighted averages to determine how to
combine coupling values combine coupling values Example:Example:
• Given the pair-wise coupling valuesGiven the pair-wise coupling values
Kernel AKernel A
Kernel BKernel B
Kernel CKernel C
Want:Want: T = E T = EAA + E + EBB + E + ECC1 2 3 = (C= (CABAB * P * PABAB + C + CACAC * P * PAC AC ))
PPABAB + P + PACAC
1
= (C= (CABAB * P * PABAB + C + CBCBC * P * PBC BC ))
PPABAB + P + PBCBC
2
= (C= (CBCBC * P * PBCBC+ C+ CACAC * P * PAC AC ))
PPBCBC + P + PACAC
3
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Composition MethodComposition Method Synthetic kernels (array updates)Synthetic kernels (array updates)
Kernel A (196.44)Kernel A (196.44)
Kernel B (207.16)Kernel B (207.16)
Kernel C (574.19)Kernel C (574.19)
Kernel PairKernel Pair CouplingCoupling
A - BA - B 0.970.97
B - CB - C 0.750.75
C - AC - A 0.760.76
1 = 0.8472= 0.8472 2 = 0.8407= 0.8407 3 = 0.7591= 0.7591
Actual total timeActual total time: 799.63s: 799.63sCoupling timeCoupling time: 776.52s (Error: 2.89%): 776.52s (Error: 2.89%)Adding individual times: 971.81s (Error: 23%)Adding individual times: 971.81s (Error: 23%)
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Coupling Method: UsageCoupling Method: UsageCoupling Method: UsageCoupling Method: Usage
Run
Function Performance
Inputs
Systems
Functions
Control Flow
Adjacent KernelsAdjacent Kernels
Coupling Coupling Values and Values and
Performance Performance datadata
Coupling
Data and Data and System InfoSystem Info
Analytical EquationAnalytical Equation(Octave: Coupling)(Octave: Coupling)
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Case StudiesCase Studies
Predication: Resource AllocationPredication: Resource Allocation• Grid Physics Network (GriPhyN)Grid Physics Network (GriPhyN)• Utilizes Grid 2003 infrastructureUtilizes Grid 2003 infrastructure• GeoLIGO applicationGeoLIGO application
Prediction: Resource AllocationPrediction: Resource Allocation• AADMLSS: Educational ApplicationAADMLSS: Educational Application• Utilizes multiple serversUtilizes multiple servers
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Case 1: GEO LIGOCase 1: GEO LIGO (GriPhyN) (GriPhyN)
The pulsar search is a process of finding celestial objects that may emit gravitational waves
• GEO (German-English Observatory) LIGO (Laser Interferometer Gravitational-wave Observatory) pulsar search is the most frequent coherent search method that generates F-statistic for known pulsars
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GriPhyN GriPhyN Chimera
Virtual Data System
Prophesy
Grid Middleware Ganglia
GRID 2003
ResourceSelection
Monitoring
Transform using VDL
Submission
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Resource Selector Resource Selector
Application Name
Input Parameters,List of available sites
Rankings of sites
Weights of each site
Prophesy
Interface
Predictor
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Grid2003 TestbedGrid2003 Testbed
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Execution EnvironmentExecution EnvironmentSite NameSite Name CPUsCPUs BatchBatch Compute NodesCompute Nodes
ProcessorsProcessors Cache Cache SizeSize
MemoryMemory
alliance.unm.edu (UNM)alliance.unm.edu (UNM) 436436 PBSPBS 1 X PIII 731 GHz1 X PIII 731 GHz 256 KB256 KB 1 GB1 GB
atlas.iu.edu (IU)atlas.iu.edu (IU) 400400 PBSPBS 2 X Intel Xeon 2.4 GHz2 X Intel Xeon 2.4 GHz 512 KB512 KB 2.5 GB2.5 GB
pdsfgrid3.nersc.gov (PDSF)pdsfgrid3.nersc.gov (PDSF) 349349 LSFLSF 2 X PIII 650-1.8 GHz 2 X PIII 650-1.8 GHz 2 X AMD 2100+ - 2600+2 X AMD 2100+ - 2600+
256 KB256 KB 2 GB2 GB
atlas.dpcc.uta.edu (UTA)atlas.dpcc.uta.edu (UTA) 158158 PBSPBS 2 X Intel Xeon 2.4 – 2.6 GHz 2 X Intel Xeon 2.4 – 2.6 GHz 512 KB512 KB 2 GB2 GB
nest.phys.uwm.edu (UWM)nest.phys.uwm.edu (UWM) 296296 CONDORCONDOR 1 X PIII 1GHz1 X PIII 1GHz 256 KB256 KB 0.5 GB0.5 GB
boomer1.oscer.ou.edu (OU)boomer1.oscer.ou.edu (OU) 286286 PBSPBS 3 X Intel Xeon 2 GHz3 X Intel Xeon 2 GHz 512 KB512 KB 2 GB2 GB
cmsgrid.hep.wisc.edu cmsgrid.hep.wisc.edu (UWMadison)(UWMadison)
6464 CONDORCONDOR 1 X Intel Xeon 2.8 GHz1 X Intel Xeon 2.8 GHz 512 KB512 KB 2 GB2 GB
cluster28.knu.ac.kr (KNU)cluster28.knu.ac.kr (KNU) 104104 CONDORCONDOR 1 X AMD Athlon XP 1700+1 X AMD Athlon XP 1700+ 256 KB256 KB 0.8 GB0.8 GB
acdc.ccr.buffalo.edu acdc.ccr.buffalo.edu (Ubuffalo)(Ubuffalo)
7474 PBSPBS 1 X Intel Xeon 1.6 GHz1 X Intel Xeon 1.6 GHz 256 KB256 KB 3.7 GB3.7 GB
ParametersParameters Prediction-basedPrediction-based Load-basedLoad-based RandomRandom
AlphaAlpha FreqFreq SiteSiteTimeTime(sec)(sec) SiteSite Time (sec)Time (sec) ErrorError Selected SiteSelected Site
Time Time (sec)(sec) ErrorError
0.00650.0065 0.0020.002 PDSFPDSF 3863.66 3863.66 UWMadisonUWMadison 9435.80 9435.80 59.05%59.05% UWMilwaukeeUWMilwaukee 48065.83 48065.83 60.09%60.09%
0.00850.0085 0.0010.001 IUIU 2850.39 2850.39 UWMadisonUWMadison 11360.28 11360.28 74.91%74.91% KNUKNU 7676.56 7676.56 62.87%62.87%
0.00750.0075 0.0090.009 IUIU 22090.17 22090.17 PDSFPDSF 20197.88 20197.88 -9.37%-9.37% UNMUNM 77298.13 77298.13 71.42%71.42%
0.00550.0055 0.0090.009 IUIU 16216.25 16216.25 UTAUTA 27412.45 27412.45 40.84%40.84% UWMadisonUWMadison 31555.10 31555.10 48.61%48.61%
0.00050.0005 0.0090.009 PDSFPDSF 1365.51 1365.51 UbuffaloUbuffalo 3226.00 3226.00 57.67%57.67% UWMilwaukeeUWMilwaukee 16009.82 16009.82 91.47%91.47%
0.00750.0075 0.0030.003 PDSFPDSF 6723.30 6723.30 IUIU 7343.37 7343.37 8.44%8.44% KNUKNU 8287.77 8287.77 18.88%18.88%
0.00650.0065 0.0070.007 PDSFPDSF 13561.01 13561.01 PDSFPDSF 13561.01 13561.01 0.00%0.00% UNMUNM 52379.31 52379.31 74.65%74.65%
0.00850.0085 0.0040.004 PDSFPDSF 10121.27 10121.27 UbuffaloUbuffalo 19649.22 19649.22 48.49%48.49% IUIU 11158.72 11158.72 9.30%9.30%
0.00350.0035 0.0050.005 PDSFPDSF 5241.28 5241.28 UbuffaloUbuffalo 20799.05 20799.05 74.80%74.80% UWMUWM 51936.49 51936.49 89.91%89.91%
0.00650.0065 0.0090.009 IUIU 19184.36 19184.36 UWMadisonUWMadison 24995.94 24995.94 23.25%23.25% OUOU 23441.16 23441.16 18.16%18.16%
0.00450.0045 0.0090.009IUIU 13278.68 13278.68 UTAUTA 20453.30 20453.30 35.08%35.08% UWMadisonUWMadison 14137.44 14137.44 6.07%6.07%
0.00850.0085 0.0090.009 IUIU 25021.39 25021.39 UWMadisonUWMadison 26246.68 26246.68 4.67%4.67% OUOU 31538.22 31538.22 20.66%20.66%
AverageAverage 33.68%33.68% 58.62%58.62%
Experimental ResultsExperimental Results
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Case Study 2: AADMLSSCase Study 2: AADMLSS
African American Distributed Learning System (AADMLSS) developed by Dr. Juan E. GilbertAfrican American Distributed Learning System (AADMLSS) developed by Dr. Juan E. Gilbert
http://prophesy.cs.tamu.eduhttp://prophesy.cs.tamu.edu2929
Site Selection Process Site Selection Process
Measure NetworkPerformance
Measure Server Performance
Select server with bestoverall site performance
User logs out
NO
NO
YES
Pass Quiz?
Next concept(same instructor)
Current concept(different instructor)
Exit?
YES
Display Concept
User logsinto AADMLSS
Valid Usernameand Password?
NO
YES
NO YES
First timeaccess?
Get default conceptGet last concept
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Testbed OverviewTestbed Overview
CATEGORYCATEGORY SPECS SPECS Loner (TX)Loner (TX) Prophesy (TX)Prophesy (TX) Tina (MA)Tina (MA) Interact (AL)Interact (AL)
HardwareHardware
CPU Speed CPU Speed (MHz)(MHz)
997.62997.62 3056.853056.85 1993.561993.56 697.87697.87
Bus Speed Bus Speed (MB/s)(MB/s)
205205 856856 638638 214214
Memory (MB)Memory (MB) 256256 20482048 256256 256256
Hard Disk (GB)Hard Disk (GB) 3030 146146 4040 1010
SoftwareSoftware
O/SO/S Redhat Linux Redhat Linux 9.09.0
Redhat Linux Enterprise Redhat Linux Enterprise 3.03.0
Redhat Linux Redhat Linux 9.09.0
Redhat Linux Redhat Linux 9.09.0
Web ServerWeb Server Apache 2.0Apache 2.0 Apache 2.0Apache 2.0 Apache 2.0Apache 2.0 Apache 2.0Apache 2.0
Web Web ApplicationApplication
PHP 4.2PHP 4.2 PHP 4.3PHP 4.3 PHP 4.2PHP 4.2 PHP 4.1PHP 4.1
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Course/Module/Course/Module/ConceptConcept
DAYDAY
NIGHTNIGHT
SRT-LOAD SRT-LOAD (%)(%)
SRT-SRT-RANDOM RANDOM
(%)(%)SRT-LOAD SRT-LOAD
(%)(%)SRT-RANDOM SRT-RANDOM
(%)(%)
3/0/03/0/0 9.759.75 16.9716.97 8.768.76 13.5413.54
3/0/13/0/1 12.5812.58 24.7624.76 12.3012.30 22.5422.54
3/0/23/0/2 16.7516.75 29.7029.70 15.7515.75 28.9528.95
3/0/33/0/3 20.5420.54 27.1027.10 18.7518.75 25.5425.54
3/1/03/1/0 9.149.14 16.9216.92 8.768.76 13.9613.96
3/1/13/1/1 8.678.67 15.7615.76 8.018.01 14.1514.15
3/1/23/1/2 13.3813.38 23.5723.57 11.9411.94 20.6720.67
3/1/33/1/3 12.1612.16 19.7619.76 11.8711.87 19.1119.11
3/2/03/2/0 8.958.95 15.1515.15 8.648.64 15.0915.09
3/2/13/2/1 11.5711.57 17.4017.40 9.959.95 15.5415.54
3/2/23/2/2 10.9510.95 19.7519.75 9.609.60 15.2715.27
3/2/33/2/3 11.0411.04 23.0823.08 12.5412.54 22.8422.84
3/3/03/3/0 8.918.91 15.9415.94 7.697.69 15.9115.91
3/3/13/3/1 9.079.07 17.9017.90 8.478.47 16.9516.95
3/3/23/3/2 9.469.46 16.7716.77 9.319.31 15.7615.76
3/3/33/3/3 10.5510.55 19.5719.57 9.879.87 17.9517.95
AVERAGEAVERAGE 11.4711.47 20.0120.01 10.7610.76 18.3618.36
4-Servers4-Servers
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Results - 4 ServersResults - 4 Servers
0%
25%
50%
75%
100%
Random (D) Random (N) Load (D) Load (N) SRT (D) SRT (N)
Site Selection Distribution
Loner
Prophesy
Tina
Interact
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Results – 3 Servers Results – 3 Servers ConceptConcept SRT-LOAD (%)SRT-LOAD (%) SRT-RANDOM (%)SRT-RANDOM (%)
3/0/0 D3/0/0 D 6.216.21 14.0514.05
3/0/1 D3/0/1 D 12.1312.13 21.9421.94
3/0/2 N3/0/2 N 14.0214.02 25.8325.83
3/0/3 N3/0/3 N 18.1218.12 23.5223.52
3/1/0 N3/1/0 N 8.058.05 12.0412.04
3/1/1 N3/1/1 N 7.317.31 12.2512.25
3/1/2 N3/1/2 N 12.6012.60 18.7418.74
3/1/3 N3/1/3 N 10.9610.96 19.1119.11
3/2/0 N3/2/0 N 7.937.93 12.5812.58
3/2/1 N3/2/1 N 8.058.05 14.2514.25
3/2/2 N3/2/2 N 9.149.14 15.9715.97
3/2/3 D3/2/3 D 9.799.79 20.5820.58
3/3/0 D3/3/0 D 8.948.94 13.6413.64
3/3/1 D3/3/1 D 8.268.26 16.7416.74
3/3/2 D3/3/2 D 9.219.21 15.2115.21
3/3/3 D3/3/3 D 9.979.97 19.3619.36
AVERAGEAVERAGE 10.0410.04 17.2417.24
0%
25%
50%
75%
100%
Random (D) Random(N) Load (D) Load (N) SRT (D) SRT (N)
Site Selection Distribution
Loner
Tina
Interact
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Results – 3 ServersResults – 3 Servers
0
500
1000
1500
2000
2500
3000
msec
Lo
ner
Tin
aIn
tera
ct
Lo
ner
Tin
aIn
tera
ct
Lo
ner
Tin
aIn
tera
ct
Lo
ner
Tin
aIn
tera
ct
Lo
ner
Tin
aIn
tera
ct
Lo
ner
Tin
aIn
tera
ct
Lo
ner
Tin
aIn
tera
ct
Lo
ner
Tin
aIn
tera
ct
3/0/0 D 3/0/1 D 3/0/2 N 3/0/3 N 3/2/0 N 3/2/1 N 3/2/2 D 3/2/3D
Average Service Response Time - AGENT
Netw ork Delay
Server Access Time
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Results – 2 Servers Results – 2 Servers
0%
25%
50%
75%
100%
Random Load SRT
Site Selection Distribution (DAY)
Tina
Interact
ConceptConcept SRT-LOAD (%)SRT-LOAD (%) SRT-RANDOM (%)SRT-RANDOM (%)
3/0/0 D3/0/0 D 3.133.13 4.034.03
3/0/1 D3/0/1 D 4.264.26 5.975.97
3/0/2 D3/0/2 D 7.027.02 8.288.28
3/0/3 D3/0/3 D 8.648.64 9.029.02
3/1/0 D3/1/0 D 3.253.25 4.944.94
3/1/1 D3/1/1 D 3.273.27 4.104.10
3/1/2 D3/1/2 D 3.933.93 5.975.97
3/1/3 D3/1/3 D 3.643.64 4.084.08
3/2/0 D3/2/0 D 3.153.15 3.323.32
3/2/1 D3/2/1 D 4.394.39 5.205.20
3/2/2 D3/2/2 D 5.805.80 5.975.97
3/2/3 D3/2/3 D 6.526.52 6.956.95
3/3/0 D3/3/0 D 4.394.39 5.645.64
3/3/1 D3/3/1 D 4.164.16 5.205.20
3/3/2 D3/3/2 D 4.814.81 5.735.73
3/3/3 D3/3/3 D 5.025.02 5.585.58
AVERAGEAVERAGE 4.714.71 5.625.62
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SummarySummary ProphesyProphesy Two case studies with resource allocationTwo case studies with resource allocation
• Geo LIGO: on average 33% better than load-Geo LIGO: on average 33% better than load-based selectionbased selection
• AADMLSS: on average 4-11% better than load-AADMLSS: on average 4-11% better than load-based selectionbased selection
Future workFuture work• Continue extending application baseContinue extending application base• Work on queue wait time predictionsWork on queue wait time predictions
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Performance Analysis ProjectsPerformance Analysis Projects ProphesyProphesy
• http://prophesy.cs.tamu.eduhttp://prophesy.cs.tamu.edu• Published over 20 conference and journal papers Published over 20 conference and journal papers
PAPIPAPI• http://icl.cs.utk.edu/papi/http://icl.cs.utk.edu/papi/
SCALEA-GSCALEA-G• http://www.dps.uibk.ac.at/projects/scaleag/http://www.dps.uibk.ac.at/projects/scaleag/
PerfTrackPerfTrack• http://web.cecs.pdx.edu/~karavan/perftrackhttp://web.cecs.pdx.edu/~karavan/perftrack
ParadynParadyn• http://www.cs.wisc.edu/~paradyn/http://www.cs.wisc.edu/~paradyn/
Network Weather ServiceNetwork Weather Service• http://nws.cs.ucsb.eduhttp://nws.cs.ucsb.edu