Profiling

Can I speed up my program?

Introduction

How fast does each part of my program take? Perhaps I could speed it up? As with debugging, we could add print statements, use a stopwatch, compile and re-run etc. etc. But that would be tedius and there are tools around which do the job way better.

Compiler Options

You probably want to make sure that you've added all the go-faster flags that are available before you embark on any profiling. Activating optimisations for speed can make your progam run a lot faster!

Using Valgrind

Valgrind is an excellent open-source tool for debugging and profiling.

Compile your program as normal, adding in any optimisation flags that you desire and also -g. Then run it through the callgrind tool, embedded in valgrind:

valgrind --tool=callgrind your-program [program options]

When your program has run you find a file called callgrind.out.xxxx in the current directory, where xxxx is replaced with a number (the process ID of the command you have just executed).

You can inspect the contents of this newly created file using a graphical display too call kcachegrind:

kcachegrind callgrind.out.xxxx

An Simple Example

svn co http://source.ggy.bris.ac.uk/subversion-open/profiling/trunk ./profiling
cd profiling/examples/example1
make
valgrind --tool=callgrind ./div.exe >& div.out
kcachegrind callgrind.out.xxxx

We can see from the graphical display given by kcachegrind that our inefficient division routine takes far more of the runtime that our efficient routine. Using this kind of information, we can focus our re-engineering efforts on the slower parts of our program.

OK, but how do I make my code run faster?

OK, let's assume that we've located a region of our program that is taking a long time. So far so good, but how can we address that? There are--of course--a great many reasons why some code may take a long time to run. One reason could be just that it has a lot to do! Let's assume, however, that the code can be made to run faster by applying a little more of the old grey matter. With this in mind, let's consider a few common examples of code which can tweaked to run faster.

A Common Situation Involoving Loops

For our first example, consider the way we loop over the contents of a 2-dimensional array--a pretty common situation!. Skip over a directory and compile the example programs:

cd ../example2
make

In this directory, we have two almost identical programs. Compare the contents of loopingA.f90 and loopingB.f90. You will see that inside each program a 2-dimensional array is declared and two nested loops are used to visit each of the cells of the array in turn (and an arithmetic value is dropped in). The only way in which the two programs differ is the order in which the cycle through the contents of the arrays. In loopingA.f90, the outer loop is over the columns of the array and the inner loop is over the rows (i.e. for a given value of ii, all values of jj and cycled over). The opposite is true for loopingB.f90.

Let's compare how long it takes these two programs to run. We can do this using the Linux time command:

time ./loopingA.exe

and now,

time ./loopingB.exe

Dude! loopingA.exe takes more than twice the time of loopingB.exe. What's the reason?

Well, Fortran stores it's 2-dimensional arrays in column-major order. Our 2-dimension array is actually stored in the computer memory as a 1-dimension array, where the cells in a given column are next to each other. For example, the array:

[math]\displaystyle{ \begin{bmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \end{bmatrix} }[/math]

would actually be stored in memory as:

1  4  2  5  3  6

That means is that our outer loop should be over the columns, and our inner loop over the rows. Otherwise we would end up hopping all around the memory, which takes extra time, and explains why loopingA takes longer.

The opposite situation is true for the C programming language, which stores its 2-dimensional arrays in row-major order. You can type:

make testC

To run some equivalent C code examples.

Not Doing the Same Thing Twice (or more!)

The following is the Manning formula drawn from the field of hydrology:

[math]\displaystyle{ V = \frac{1}{n} R_h ^\frac{2}{3} \cdot S^\frac{1}{2} }[/math]

where:

[math]\displaystyle{ V }[/math]	is the cross-sectional average velocity (m/s)
[math]\displaystyle{ n }[/math]	is the Gauckler–Manning coefficient (independent of units)
[math]\displaystyle{ R_h }[/math]	is the hydraulic radius (m)
[math]\displaystyle{ S }[/math]	is the slope of the water surface or the linear hydraulic head loss (m/m) ([math]\displaystyle{ S=h_f/L }[/math])

A program to compute estimates of [math]\displaystyle{ V }[/math], given an array of N hydraulic radii may contain the following loop:

  do ii=1,N
     V(ii) = (1/n) * (R(n)**(2.0/3.0)) * sqrt(S)
  end do

However, this loop may be rather wasteful. For example, if the value for the Gauckler–Manning coefficient, [math]\displaystyle{ n }[/math] is the same for all the hydraulic radii, we would be computing (1/n) needlessly each time through the loop and could take it outside:

  n_inv = (1/n)
  do ii=1,N
     V(ii) = n_inv * (R(n)**(2.0/3.0)) * sqrt(S)
  end do

This would make particular sense, since the division operator is an expensive one. We also don't need to compute (2.0/3.0)) over and over:

  n_inv = (1/n)
  exponent=2.0/3.0
  do ii=1,N
     V(ii) = n_inv * (R(n)**exponent) * sqrt(S)
  end do

Lastly, if