about the capability of some parallel program metric prediction using neural network approach
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About the Capability of Some Parallel Program Metric Prediction Using Neural Network Approach. Vera Yu. Goritskaya [email protected] Nina N. Popova [email protected]. Problem Area. Rising complexity of multiprocessor systems Heterogeneous clusters - PowerPoint PPT PresentationTRANSCRIPT
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About the Capability of Some Parallel Program Metric Prediction Using Neural
Network Approach
Vera Yu. Goritskaya [email protected]
Nina N. Popova [email protected]
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Problem Area Rising complexity of multiprocessor systems
Heterogeneous clusters Distributed systems which include clusters and
other multiprocessors Parallel program specifics
Parallel program execution is affected by many factors
• communication environment loading• nodes on which application is scheduled can vary• …
Almost impossible to estimate how a program would behave when running on multiprocessor
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Parallel Program Metric Examples
Run time (flow time) Tflow = Tcomputation + Tcommunication +
Tidle Speedup = T1 / TN Efficiency = Speedup / N Scalability of a parallel system
Efficiency is the same for increasing the number of processors and the size of the problem
…
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Related Works Neural network mechanism for application performance
prediction Ipek, B.R. de Supinski, M. Schultz, S.A. McKee “An Approach to Performance
Prediction for Parallel Applications” // Euro-Par 2005 Parallel Processing, Volume 3648, 2005, p. 196-205, ISBN 978-3-540-28700-1, 2005
Analytic modeling for performance tuning of parallel programs
Crovella Mark E., Tomas J. LeBlanc. Parallel Performance Prediction Using Lost Cycles Analysis // Proceedings of Supercomputing’94, 1994. P. 600-610
Job execution time estimation based on program source analysis
V.V. Balashov, A.P. Kapitonova, V.A. Kostenko, R.L. Smelyanskiy, N.V. Yuschenko “Method for estimating platform-optimized application execution time based on its high-level language source code” // Proceedings of 1st international conference “Digital signal processing and its applications”, Volume IV, p. 203-220
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Project Features Parallel application flow time prediction
i.e. the time that program would spend inside the multiprocessor system
Consider large amount of parameters communication environment loading nodes characteristics scheduling features …
Neural network mechanism potentially can be applied to various parallel systems and applications
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Project Features (2) Processes data:
can be gathered without affecting source code of an application
can be gathered using standard OS and job managing system utilities
includes:• job submission moment• required processors• maximum required execution time• system loading at the submission moment• size of executable, etc.
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Project Features (3) Improving prediction accuracy:
we gather characteristics of multiple executions of an application (“execution history”)
sample “historical” characteristics:• average execution time for definite application with
fixed required processors number• average required time for definite application• average size of executable, etc.
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Data Pre-Processing Grouping parallel programs from input set
into categories according to the average execution time 4 groups in described case (<100 sec, 100-
1000 sec, 1000-5000 sec, >5000 sec) “Noise” data excluding
samples with max and min execution time values for each job were removed from input data sets
samples corresponding to rejected jobs were also excluded
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Target Platforms
IBM eServer pSeries 690 (“Regatta”) 16-processor SMP architecture
IBM eServer pSeries 360 (“Hill”) 10-processor cluster
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Neural Network Architectures Multilayer feedforward network
with sigmoid transfer function (1 hidden layer)
Elman backpropagation network
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Training ResultsJob group (according to the average execution time)
Performance (multiplayer feedforward network with sigmoid transfer function)
Performance (elman backpropagation network)
< 100 sec 10-2 – 10-3 10-3
100 – 1000 sec 10-4 10-4
1000 – 5000 sec 10-4 – 10-5 10-4
> 5000 sec 10-5 10-5
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Testing: Execution Time Prediction (“Regatta”)
Execution time prediction (feedforward NN) on “Regatta” Execution time prediction (Elman NN) on “Regatta”
predicted values
real values
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Target Platforms
IBM eServer pSeries 690 (“Regatta”) 16-processor SMP architecture
IBM eServer pSeries 360 (“Hill”) 10-processor cluster
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Neural Network Architecture Elman backpropagation network
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Testing: Execution Time Prediction (“Hill”)
predicted values
real values
tasks
time (sec)
•Hill’s flow is more homogeneous than Regatta’s flow
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Conclusions and Future Work Improving data processing methods
possibly will lead to more accurate results Using described approach new scheduling
algorithms can be developed Applying NN prediction mechanisms on
other multiprocessor platforms Problem Solving Environments
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Thank you!
Q & A ?