Thread: Monte Carlo Simulation Optimization

Greetings,

I am a Physics graduate student working on a Monte Carlo simulation I wrote in C++. I've reached the point where my major concern is how fast the program runs since I need to do lots of data averaging to get clean results. I'm trying to optimize for speed and have a couple of specific questions. When I began writing the simulation I had more experience with Fortran than C++, and my code is likely somewhat oddly structured for an object oriented language.

My major question is if it makes a significant performance difference to make a function a member of a class rather than pass the class object to the function by pointer. My program is currently structured such that I have 3 classes that divide up different types of data, and I pass one or more of these class objects to global functions by pointer. The functions do work on the data. These functions perform the major work of the program and are called millions of times.

I've been considering reworking the whole thing, perhaps lumping all of the data structures into one class, and making all the functions member functions. This would be worth the effort if I gained a 10% performance increase, probably not worth it for a 1% increase.

Other than that I have heard that references can be better optimized by the compiler than pointers, and that arrays with dimensions that are a power of 2 can be manipulated more quickly. If anyone has any advice regarding these it would be appreciated.

I know which functions are slow due to their calculation burden, and it is possible no structural changes can significantly speed things up, but if anyone has any advice it would be appreciated.

Thanks.

Be nice to see some code.

As for arrays with a power of 2, it depends on what you mean by "manipulated easily". From an algorithmic perspective, they can be more easily dealt with in merge sort, and binary search algorithms due to their log(n) and n*log(n) asymptotic runtimes.

It also depends on the data structures you are using to store the data and how you are using the data. If you are searching more often than inserting data, then tree structures, such as binary, 2-3, red-black trees are better. If you are inserting data more, then using linked lists and arrays would be best.

As for compiler optimization, I'm not quite sure.

Thanks for your responses. kmdv thanks for the advice about reworking it, I won't go to the trouble. I'm not aware of any libraries that would be applicable to my situation though perhaps there is one. We're simulating chain molecules using a coarse grained method that is somewhat new, and the effectiveness of the method is part of the research interest. I do have linear algebra libraries for those sort of operations. I think I'm probably too far along to change languages.

jshowa thanks for the powers of 2 advice. I mostly insert and remove data from an array as the chain molecule grows and shrinks so powers of 2 might help. Also a fast function for advancing all data in an array by one index place could be useful, I'll try to look one up. I'm somewhat reluctant to be too specific or post code since the research isn't finished and also belongs to my research group.

One other thing I've been considering: in some places doubles are recast as ints when I take space locations from the continuum and round to nearest locations on a discrete grid. I have heard that this recasting can be slow. If anyone has any advice about that it would be appreciated. In this particular method I must go back and forth between the continuum and the grid.

Thanks much.