Optimizing: things that matter

In my previous post, I explained how to optimize (broadly speaking) the initialization of a block of data.
I also mentioned that it was useless. There is at least two reasons for that:

- Initializing data can't (and shouldn't) be the costliest part of your program:
If you are doing some string processing, some computations on an array, initializing the array is unlikely the part that will require the most computer instructions. You should instead focus on optimizing your algorithms.

- do your best to never have to initialize an array to a uniform value more than once in a program's lifetime:
This may sound surprising, even unfeasible. However, if initializing happens very often in your program, then truth #1 doesn't hold anymore, and optimizing your algorithms now include optimizing the initialization: choosing an algorithm were there is no, or a very minimal need to initialize data.

This will lead us to our next article on optimizing scripting languages execution time.

Optimizing: bzero/memset, loops and beyond

Something nice about opensource is that one gets to see how others do things, and learn from it, sometimes to keep good ideas, to think why it was written that way, improve one's programs. I could go on on licences, but that will be for another time.

Let's consider we have a reasonably small block of data, say an array of characters we wish to initialize every byte to 0 and that we are using a lower level language such as C.

To do this, we could write it that way:

for (i=0; i<ArraySize; i++)
  data[i] = '\0';

Rather straightforward and efficient, gets the job done you say ?
Well, first, there is nothing really obvious when reading the code about what it's actually doing. Out of context, it's not necessarily self-explaining.
It's easy to see also that if your processor can handle words bigger than 8 bits, you could be needing two or four or height more times instructions than needed.

Let's write it that way:

// Initialize the array to the null character
memset (data, '\0', SizeArray * sizeof (char));

What have we gained ? Well, maybe we only added the cost of a function call. Let's take a look at OpenBSD's implentation of memset:

$ less /usr/src/lib/libc/string/memset.c
/* $OpenBSD: memset.c,v 1.5 2005/08/08 08:05:37 espie Exp $ */
(full file and licence header at:
http://www.openbsd.org/cgi-bin/cvsweb/src/lib/libc/string/memset.c
*/

#include <string.h>

void *
memset(void *dst, int c, size_t n)
{

if (n != 0) {
  char *d = dst;

  do
    *d++ = c;
  while (--n != 0);
}
return (dst);
}

OK, that's even more cryptic than our own version. In some way better though, as we aren't incrementing a 'useless' variable. But this iq disappointing optimization-wise. That was useless; or was it ? Let's look at the FreeBSD implementation of memset. Now things get interesting: memset is adapted to the machine's native word size. No more byte per byte initialization.

That's one lesson: if it's something common, there must exist someone who tried to write it right. There is no need on your part to take the risk of introducing new bugs or security breaches.

Let's consider we are the proud owner of an amd64 processor. Wait, there is a special implementation in assembly for that. Is it just C written in assembly ? The answer is no, the magic assembly instruction shrq does the right job.

Another lesson: if the action is common enough, the processor will know how to do it natively. And the guys who wrote your OS' libc will likely have noticed that (your mileage may vary).

The good thing is, by using these "high level" functions, you keep can keep your code readable, you do not need to spend as much time debugging it, it remains portable, you don't need to be able to speak assembly fluently with in-depth knowledge of the processor you are targetting your application to, and according to the OS' libc you get the opportunity to use clever improvements made in the libc code and at the assembly level.

Will you ever think about bypassing the libc functions again ? :)

Next, why this optimization was useless....

Reading

I stumbled upon very interesting article by Anna Patterson. It's one of the best articles I've read on the subject. It's not really technical, doesn't go into details, but is very encouraging, and when writing a search engine, there are high walls to break down.

Incidentally, I discovered this article because their crawler visited my websites.
It seems their company is the new buzz in town.
I have my doubts (probably disgruntled employees and revolutionizing app do not sound like a new google to me).

But, well, as her article points, work, technique and perseverance is key.

Being out of url

A friend of mine asked me one day if I wasn't afraid of running out of url: finally reaching dead ends.

After all, the question could be pertinent: one could imagine that with an identical global interest, this could happen: many sites, linking to a handful of well-known sites.

I would be glad if that were true: that would give me a stable pool of pages to work on.

The truth is, one of the first issues is coping with a huge quantities of url, internal and external. Check this blog for instance, it's liking to at least ten websites.

I will post a few statistics later on.

For now, the answer is "no, I'm far from being out of url".

building a spider in python

This was the first set of keywords that someone in the US typed to reach my blog. He must have been disappointed, there isn't much about writing a spider in Python, except that there is a useful class which wasn't that useful to me. Maybe he didn't care to know more than that, maybe he did.

Prototype 1 had its spider written in Python.

It is quite easy to do so, the httplib class is easy to use, it has all the useful options one might need (or at least that I needed to have), I couldn't find any bug in it, and its use is straightforward.

It gets the job done, leaving to the developer the opportunity to focus on what to do with the data.

If you are considering python to write a spider, go for it. And if you don't trust my word (yet), you will see a good number of major search engine using python to write that component.

Even fortune said so

43rd Law of Computing:
Anything that can go wr
fortune: Segmentation violation -- Core dumped

Garbage collectors and memory

Is a garbage collector as usually found in interpreted languages enough not to worry about memory ?

The tempting and common answer is yes, the garbage collector is here to take charge of this, and is here exactly to solve that problem.

However, when writing a software that need to be speed and memory efficient (and it's really easy to write a program which needs rapidly a bit of tuning, no rocket launch has to be involved), it is usually a good thing to keep in mind memory allocation: how many scattered objects, small and big, are being dealt with during the program execution, how often does new/delete happen, and so on. It's usually rather easy to write a beast which will need Gigabytes of memory, or one which allocate/free Gigabytes of memory every few seconds, or a code where the garbage collector will never get a chance to work.

Here's a few examples of memory going wrong:
Functions not tail recursive in Erlang will never give the garbage collector a chance to free the memory
- Creating and destroying objects, in Java for, say, processing high frequency requests, will make the garbage collector allocate and free huge chunks of memory
- Arrays that can get huge in a php command line script, this language being usually used to handle short term requests
- SQL results sets that get bigger with time (and eventually don't even fit in memory)
- Add your own...

Java programmers beware: I've seen that example happen more than once.
I had a good laugh. The author of the program did not.