Improving Perl Application Performance

The four basic performance-tuning steps to improve an existing application's performance.
Hasn't Someone Already Done This?

A number of open-source Perl packages are available. Hopefully, I could find a standard deviation calculation that was faster than my best attempt so far. I found and downloaded a statistics package from CPAN called Statistics::Descriptive. I created a function called std_dev_pm that used the Statistics::Descriptive package. The code for this function is shown in Listing 4.

Using this function, however, produced a result of 6.80 s/iter; 48% worse than the baseline std_dev function. This is not altogether unexpected considering that the Statistics::Descriptive package uses an object interface. Each calculation includes the overhead of constructing and destructing a Statistics::Descriptive::Sparse object. This is not to say that Statistics::Descriptive is a bad package. It contains a considerable number of statistical calculations written in Perl and is easy to use for calculations that don't have to be fast. However, for our specific case, speed is more important.

An Out-of-Language Experience

All languages have good and bad qualities. Perl, for example, is a good general-purpose language but is not the best for number-crunching calculations. With this in mind, I decided to rewrite the standard deviation function in C to see if it improved performance.

In the case of the data collection application, it would be counter-productive to rewrite the entire project in C. Quite a few specific Perl utilities make it the best language for most of the application. An alternative to rewriting the application is to rewrite only the functions that specifically need performance improvement. This is done by wrapping a standard deviation function written in C into a Perl module. Wrapping the C function allows us to keep the majority of the program in Perl but allows us to mix in C and C++ where appropriate.

Writing a Perl wrapper over an existing C or C++ interface requires using XS. XS is a tool that is distributed with the Perl package, and it is documented in the perlxs Perl document. You also need some of the information located in the perlguts document. Using XS, I created a Perl package called OAFastStats containing a standard deviation function implemented in C. This function, shown in Listing 5, can then be called directly from Perl. For comparison purposes, this standard deviation function will be called std_dev_OAFast.

The comparison between the baseline standard deviation function and the C function wrapped with XS is presented in Table 2, showing a significant speedup. The C function (std_dev_ref_OAFast) is 1,175% faster than the baseline function (std_dev), and it is 395% faster than the best Perl implementation (std_dev_ref_sum).

Table 2. Baseline and Fastest Perl Implementations Compared with C



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standard deviations..

Nagilum's picture

use Statistics::Descriptive;
my $stat = Statistics::Descriptive::Sparse->new();
print $stat->standard_deviation() . "\n";

-> 2153.60937343181

sub std_dev_ref_sum {
my $ar = shift;
my $elements = scalar @$ar;
my $sum = 0;
my $sumsq = 0;
foreach (@$ar) {
$sum += $_;
$sumsq += ($_ **2);
return sqrt( $sumsq/$elements -
(($sum/$elements) ** 2));
print std_dev_ref_sum(\@scratch) . "\n";

-> 1758.41469005422

Someone makes a mistake here..

Difference between standard deviation, knowing full population

anonymous's picture

The difference between the two calculations:

The calculation in the Statistics::Descriptive package assumes that the data available is a sample from the population, does not contain the full population. See:
In the Statistics::Descriptive documentation, this is referenced by the note: "Returns the standard deviation of the data. Division by n-1 is used."

The calculation used in the article assumes that the data represents the full population.

Err... No.

Gordan Bobic's picture

In most cases, I have seen Perl performance that rivals C;

I would love to see you demonstrate even just one example where this is the case. The gain of _only_ 11.75x of your "C" over Perl in the case you describe is because you used XS for the implementation and not pure C with XS to just glue the two together. For big arrays you'll find it's faster to transcribe the Perl array into a C array of floats, and to do the work in pure C. Perl is usually about two orders of magnitude (100x) slower than C or decently coded C++.

What you say about object oriented interfaces slowing things down is also completely untrue. The only thing you'll save by using procedural rather than OO implementation is a pointer dereference when you call the std_dev method on the object - which is negligible compared to the calculations inside the function.

Re: Improving Perl Application Performance

Anonymous's picture

Hopefully, in the future, there will be less of a need for this sort of thing... With any luck, Perl6 and Parrot will prove to be faster, and far easier to integrate with C. In fact, the equivalent Parrot routines are already only about 3x slower than the equivalent C program, and both are far faster than Perl5 is today. (code follows)
-- pb

time N0 # time
mul N0, 1048576.0
mod N0, N0, 2000000000.0
set I0, N0 # seed
new P0, .Random # rng
set P0, I0 # seed the rng
set I0, 1000000 # array size
set I1, I0
set I2, 100 # loops
new P1, .SArray
set P1, I1
set N0, P0 # random numbers
mul N0, N0, I0
dec I1
set P1[I1], N0
time N4
set I1, P1 # array size
set N3, I1
div N3, 1, N3 # 1 / array size
set N1, 0
set N2, 0
dec I1
set N0, P1[I1]
add N1, N1, N0 # sum
mul N0, N0, N0
add N2, N2, N0 # sumsq
mul N1, N1, N3 # sum / array size
mul N1, N1, N1 # (squared)
mul N2, N2, N3 # sumsq / array size
sub N2, N2, N1 # -
pow N2, N2, 0.5 # sqrt
dec I2
time N5
sub N4, N5, N4
print N4 # time elapsed in bench loop
print "

That is parrot? That looks

Anonymous's picture

That is parrot? That looks like shit. I love perl but its as good as dead with this perl6 garbage.