About the Capability of Some Parallel Program Metric Prediction Using Neural

Network Approach

Vera Yu. Goritskaya vera@angel.cs.msu.su

Nina N. Popova popova@cs.msu.su

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

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

…

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

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

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.

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.

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

Target Platforms

IBM eServer pSeries 690 (“Regatta”) 16-processor SMP architecture

IBM eServer pSeries 360 (“Hill”) 10-processor cluster

Neural Network Architectures Multilayer feedforward network

with sigmoid transfer function (1 hidden layer)

Elman backpropagation network

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

Testing: Execution Time Prediction (“Regatta”)

Execution time prediction (feedforward NN) on “Regatta” Execution time prediction (Elman NN) on “Regatta”

predicted values

real values

Target Platforms

IBM eServer pSeries 690 (“Regatta”) 16-processor SMP architecture

IBM eServer pSeries 360 (“Hill”) 10-processor cluster

Neural Network Architecture Elman backpropagation network

Testing: Execution Time Prediction (“Hill”)

predicted values

real values

tasks

time (sec)

•Hill’s flow is more homogeneous than Regatta’s flow

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

Thank you!

Q & A ?