extra-p tutorial slides - practical...

VIRTUAL INSTITUTE – HIGH PRODUCTIVITY SUPERCOMPUTING

Insightful Automatic Performance Modeling

Software

Alexandru Calotoiu1, Torsten Hoefler2, Martin Schulz3, Sergei Shudler1 and Felix Wolf1

1 TU Darmstadt , 2 ETH Zürich , 3 Lawrence Livermore National Laboratory


Installing Extra-P


Installing Extra-P

! Download and install QT4 ! Download and install Python3+ and PYQT ! Download and install Cube !  http://www.scalasca.org/software/cube-4.x/download.html

! Download Extra-P ! http://www.scalasca.org/software/extra-p/download.html

! Unpack & install Extra-P !  ./configure --prefix=<extra-p-install-dir> --with-cube=<cube-dir> CPPFLAGS=-I<python.h path>; make; make install

INSIGHTFUL AUTOMATIC PERFORMANCE MODELING TUTORIAL 3

5


Using Extra-P


Automatic performance modeling with Extra-P


Myapp.cpp main() { foo() bar() compute() }

Instrumentation & Measurement

Performance measurements (profiles)

Input

Output

Myapp.weak.p128.r1/profile.cubex

Score-P

Myapp.weak.p256.r1/profile.cubex Myapp.weak.p512.r1/profile.cubex

Myapp.weak.p1024.r1/profile.cubex Myapp.weak.p2048.r1/profile.cubex

Results.cubex Results.xtrap

Region 1: main Model: (3) + (3.14 * x^( 2 ))

...

Extra-P


Extra-P input in text form


! Useful to debug or when a small data set must be modeled

! Example provided in input.txt

POINTS 1000 2000 4000 8000 16000 EXPERIMENT ImportantData

DATA 1 1 1 1 1DATA 4 4 4 3.99 4.01DATA 16 15.999 16.01 16.01 15.99DATA 64 64 64 64.01 63.99DATA 256.01 255.99 256 256








Measurement points Use at least 4, preferably 5, but in general the more the better








Experiment identifier Acts as both identifier and separator for experiments and their data








Data points Each row corresponds to a point; all values in a row are considered repeat measurements of the same experiment








Measurement points

Experiment identifier

Data points


Results in text form


Extra-P output – Text form

Measurements and model data for each experiment and metric:

Callpath/Region: exp4 Metric: Test

Data: ( 1000, 1e+06) 95% CI [1.00001e+06, 999989]( 2000, 4e+06) 95% CI [4.00003e+06, 3.99998e+06]

( 4000, 1.6e+07) 95% CI [1.6e+07, 1.6e+07]( 8000, 6.4e+07) 95% CI [6.4e+07, 6.4e+07]( 16000, 2.56e+08) 95% CI [2.56e+08, 2.56e+08]

Model: 0+1*(p^2)

RSS: 3.35017 Adjusted R^2: 1


Measurements for each input element (e.g., #processes)

Adjusted R2 (explained previously)

Best-fit model

RSS: Residual sum of squares

Metric name; either Score-P metrics (time, bytes, etc.) or custom metrics


Modeling sets of Cube experiments


Extra-P Cube input description

! Modeling tool expects Cube files in the following format: <DIR>/<PREFIX><X><POSTFIX>.r<{1,..,REPS}>/<FILENAME>

! DIR, PREFIX, X, POSTFIX, REPS and FILENAME must all be defined.

!  X – value of varied parameter e.g. number of processes

!  REPS – number of repeated experiments with same parameter value



Extra-P Cube input description

<DIR>/<PREFIX><X><POSTFIX>.r<{1,..,REPS}>/<FILENAME>


Best effort approach to identify and populate the

fields automatically based on selected directory contents.


Visualization with Extra-P


Extra-P user interface


Call tree exploration Plot of the model

Selected kernel(s)


Extra-P call tree view


Call tree exploration

Model

Quality of fit metrics: Residual sum of squares

and Adjusted R2

Asymptotic view of model functions vs. value at

given value

Impact of each kernel on the metric at the selected process count compared to the other kernels

Metric selection


Extra-P model view


Measurement values

X axis scale control for prediction of behavior at other process counts

Models selected in the Call path view


Extra-P model generation tool


Search space definition

Note: When a new experiment is loaded the values here are used for the initial modeling

Number of terms. Recommended value 1, as it captures dominant behavior


Feedback

! What additional features would you like to see?

! What additional capabilities would you like to see?



Thank You!

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