Efficiency Tester

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Revision as of 04:51, 16 October 2007 by Ppaatt Lynagh (talk | contribs) (reword the link with LSL Script Efficiency)
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Q1: Want to see how small some code is?

A: Add three copies of your code to a script, call llGetFreeMemory to count free space, and start deleting copies. After deleting each copy, you should see a consistent savings in free space, i.e, the code space cost of your code.

Q2: Want to see how fast some code is?

A: Run your code inside code like this example to call your code time and again to measure the consequent change in llGetTimestamp.

//IMPORTANT: Only perform tests in an empty region.
// To reduce contamination and be sure to wearing no attachments.
// Preferably do tests in a private sim with one on it.
// Don't move while performing the test.
// There is a margin of error so run the tests multiple times to determine it.

integer time() { // count milliseconds since the day began
    string stamp = llGetTimestamp(); // "YYYY-MM-DDThh:mm:ss.ff..fZ"
    return (integer) llGetSubString(stamp, 11, 12) * 3600000 + // hh
           (integer) llGetSubString(stamp, 14, 15) * 60000 +  // mm
           llRound((float)llGetSubString(stamp, 17, -2) * 1000000.0)/1000; // ss.ff..f
}

default {
  state_entry() {

    //test variables
    float counter;

    //framework variables
    float i = 0;
    float j = 0;
    float max = 10000; // 2ms of work takes 20 seconds to repeat 10,000 times, plus overhead

    float t0 = time();
    do {

      //test
      counter += 1;
      
    }while (++i < max);
    float t1 = time();
    do ; while (++j < max);
    float t2 = time();//remove the time required by the framework
    float elapsed = ((t1 - t0) - (t2 - t1))/max;
    llOwnerSay("The function in the loop took a total of " + (string)elapsed + " milliseconds.");
  }
}

Copy-edited by Xaviar Czervik, then modified by Strife Onizuka, then further edited as the history of this article shows.

See the LSL Script Efficiency article for a less brief discussion. Please understand, we don't mean to be arguing for many different ways to measure the costs of code. We do mean to be building a consensus on best practices, in one considerately short article constructed from a neutral point of view.