Using command-line arguments

Overview

Teaching: 60 min
Exercises: 30 min

Questions

• How do I use the same program for multiple use-cases?

Objectives

• First objective.

From the last run of our code, we can see that 500 iterations is not enough to get to a steady state (a state where the difference in temperature does not vary too much, i.e. curdif<mindif). Now, if we want to change the number of iterations we would need to modify niter in the code, and compile it again. What if we want to change the number of rows and columns in our grid to have more precision, or if we want to see the evolution of the temperature at a different point (x,y)? The answer would be the same, modify the code and compile it again!

No need to say that this would be very tedious and inefficient. A better scenario would be if we can pass the desired configuration values to our binary when it is called at the command line. The Chapel mechanism for this is to use config variables. When a variable is declared with the config keyword, in addition to var or const, like this:

config const niter = 500;    //number of iterations

>> chpl base_solution.chpl -o base_solution

it can be initialised with a specific value, when executing the code at the command line, using the syntax:

>> ./base_solution --niter=3000

The simulation will consider a matrix of 100 by 100 elements,
it will run up to 3000 iterations, or until the largest difference
in temperature between iterations is less than 0.0001.
You are interested in the evolution of the temperature at the 
position (1,100) of the matrix...

and here we go...
Temperature at iteration 0: 25.0
Temperature at iteration 20: 2.0859
Temperature at iteration 40: 1.42663
...
Temperature at iteration 2980: 0.793969
Temperature at iteration 3000: 0.793947

Final temperature at the desired position after 3000 iterations is: 0.793947
The greatest difference in temperatures between the last two iterations was: 0.000350086

Exercise 4

Make n, x, y, mindif, rows and cols configurable variables, and test the code simulating different configurations. What can you conclude about the performance of the code?

Solution

For example, lets use a 650 x 650 grid and observe the evolution of the temperature at the position (200,300) for 10000 iterations or until the difference of temperature between iterations is less than 0.002; also, let’s print the temperature every 1000 iterations.

>> ./base_solution --rows=650 --cols=650 --x=200 --y=300 --niter=10000 --mindif=0.002 --n=1000

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

Key Points

• Config variables accept values from the command line at runtime, without you having to recompile the code.