Measuring code performance

Estimated time: 90 minutes

Overview

Questions

• “How do I know how fast my code is?”

Objectives

• “Measuring code performance by instrumenting the code.”

The code generated after Exercise 4 is the basic implementation of our simulation. We will use it as a benchmark, to see how much we can improve the performance when introducing the parallel programming features of the language in the following lessons.

But first, we need a quantitative way to measure the performance of our code. The easiest way to do it is to see how long it takes to finish a simulation. The UNIX command time could be used to this effect

BASH

time ./base_solution --rows=650 --cols=650 --x=200 --y=300 --tolerance=0.002 --outputFrequency=1000

OUTPUT

The simulation will consider a matrix of 650 by 650 elements,
it will run up to 10000 iterations, or until the largest difference
in temperature between iterations is less than 0.002.
You are interested in the evolution of the temperature at the
position (200,300) of the matrix...

and here we go...
Temperature at iteration 0: 25.0
Temperature at iteration 1000: 25.0
Temperature at iteration 2000: 25.0
Temperature at iteration 3000: 25.0
Temperature at iteration 4000: 24.9998
Temperature at iteration 5000: 24.9984
Temperature at iteration 6000: 24.9935
Temperature at iteration 7000: 24.9819

Final temperature at the desired position after 7750 iterations is: 24.9671
The greatest difference in temperatures between the last two iterations was: 0.00199985

real	0m20.381s
user	0m20.328s
sys	0m0.053s

The real time is what interests us. Our code is taking around 20 seconds from the moment it is called at the command line until it returns.

Some times, however, it could be useful to take the execution time of specific parts of the code. This can be achieved by modifying the code to output the information that we need. This process is called instrumentation of code.

An easy way to instrument our code with Chapel is by using the module Time. Modules in Chapel are libraries of useful functions and methods that can be used once the module is loaded. To load a module we use the keyword use followed by the name of the module. Once the Time module is loaded we can create a variable of the type stopwatch, and use the methods start,stopand elapsed to instrument our code.

use Time;
var watch: stopwatch;
watch.start();

//this is the main loop of the simulation
delta=tolerance;
while (c<niter && delta>=tolerance) do
{
...
}

watch.stop();

//print final information
writeln('\nThe simulation took ',watch.elapsed(),' seconds');
writeln('Final temperature at the desired position after ',c,' iterations is: ',temp[x,y]);
writeln('The greatest difference in temperatures between the last two iterations was: ',delta,'\n');

BASH

chpl base_solution.chpl
./base_solution --rows=650 --cols=650 --x=200 --y=300 --tolerance=0.002 --outputFrequency=1000

OUTPUT

The simulation will consider a matrix of 650 by 650 elements,
it will run up to 10000 iterations, or until the largest difference
in temperature between iterations is less than 0.002.
You are interested in the evolution of the temperature at the
position (200,300) of the matrix...

and here we go...
Temperature at iteration 0: 25.0
Temperature at iteration 1000: 25.0
Temperature at iteration 2000: 25.0
Temperature at iteration 3000: 25.0
Temperature at iteration 4000: 24.9998
Temperature at iteration 5000: 24.9984
Temperature at iteration 6000: 24.9935
Temperature at iteration 7000: 24.9819

The simulation took 20.1621 seconds
Final temperature at the desired position after 7750 iterations is: 24.9671
The greatest difference in temperatures between the last two iterations was: 0.00199985

Key Points

• “To measure performance, instrument your Chapel code using a stopwatch from the Time module.”