I have been using Angelscript version 2.19.2 for quite a while. I am in the process of porting an application to Linux and decided to download the latest library 2.26.2. To test the two versions I was using the tutorial sample with the LineCallback not registered to increase performance (simple commented out the registration code). I modify the script.as file in the tutorial to include this loop

    double x = 0.00;    
    int64 startTime = GetSystemTime();
    for(int i=0; i<10000; i++)
    {
	    x += i * 1.23 + 4.56;
    }
	
    int64 endTime = GetSystemTime();
    
    Print("Total time = " + (endTime - startTime)/1000.0 + "\n");

In version 2.19.2 this takes about 1ms on a 2.8GHz machine. Version 2.26.2 takes about 4ms.

When compiling the tutorial for version 2.19.2 I got the following error. I simply commented out this line for now.

g++ -ggdb -I../../../../angelscript/include -D_LINUX_ -o obj/scriptstring.o -c ../../../../add_on/scriptstring/scriptstring.cpp
../../../../add_on/scriptstring/scriptstring.cpp: In function ‘void RegisterScriptString_Native(asIScriptEngine*)’:
../../../../add_on/scriptstring/scriptstring.cpp:646:86: error: invalid static_cast from type ‘<unresolved overloaded function type>’ to type ‘bool (*)(const string&, const string&) {aka bool (*)(const std::basic_string<char>&, const std::basic_string<char>&)}’
make: *** [obj/scriptstring.o] Error 1

I haven't tried any of the other versions yet. Were there any major changes that would affect the loop performance?

Thanks,

Tony

Sorry, this was my mistake. I had an extra zero in the loop. I was trying to optimize the g++ compiler using flags -O3 and -march=native. In the course of doing this I forgot that I updated the loop to 100000 because the time value was always returning 0.

When I went back to my actual project, I had the same issue. It turns out that there was a small difference between the two scripts. I'm not sure why it makes such a big difference in the timing of the loop.

This is the orginal script. The summation in the loop is using the variable "i".

 double x = 0.00;    
    int64 startTime = GetSystemTime();
    for(int i=0; i<10000; i++)
    {
	    x += i * 1.23 + 4.56;
    }
	
    int64 endTime = GetSystemTime();
    
    Print("Total time = " + (endTime - startTime)/1000.0 + "\n");

This is the typo which has bad performance on all versions that I tried.

 double x = 0.00;    
    int64 startTime = GetSystemTime();
    for(int i=0; i<10000; i++)
    {
	    x += x * 1.23 + 4.56;
    }
	
    int64 endTime = GetSystemTime();
    
    Print("Total time = " + (endTime - startTime)/1000.0 + "\n");

I have been using this script as a test case for my application when I changed Angelscript versions in the past. When I changed platforms to Linux I manually coded the script which is what caused the difference. When the timing difference was noticed I copied the script from my other computer which is when I realized the difference. Even though this is not a useful script, I would be interested to know why there is such a large difference in the time it takes to run the script.

For anyone interested, I tried to do the same basic loop with 100000 iterations in Python (2.7.3) and Angelcode (2.26.3) on the same Linux machine 2.8Ghz. The results are approximately: Angelcode 4ms, Python 37ms. The code I used for Python is shown below. Note: I'm not a Python programmer, I just thought it would be an interesting comparison. Modifying the loop with num replaced by x in Python increased the time to 73ms.

#!/usr/bin/python

import time

starttime = time.time()
x = 0.0;
for num in range(0,100000):
  x += num * 1.23 + 4.56
print 'Total time = ' , time.time() - starttime, ' seconds'
print x, 'is the answer' 

Since you are using Python 2.7.3 for that performance test, I suggest changing range to xrange. Especially for simple loop bodies it should be faster.

ThyReaper, I tried to change to xrange as suggested. The loop time remained 37ms.

I threw the script together in Python 3 (saw very similar time results to your python tests). The answer I got using num in the calculation was 6150394500.000001. The answer I got using x in the calculation was infinity.

I speculate that either the much larger value is causing the slowdown or the multiplication of a double in the equation vs an integer. Both of those are pretty much shooting from the hip though. No testing done to confirm or dispute them.

While I had the loop put together I did change the number of loops a few times. Python seemed linear in the time it took to finish additional runs through the loop. About 3.9 ms for 10000 times, about 39 ms for 100000 times, about 395 ms for 1000000 times, about 3.994 ms for 10000000 times. And I stopped that little experiment after about 39.59 for 100 million times.