What are some good tips and/or techniques for optimizing and improving the performance of calculation heavy programs. I’m talking about things like complication graphics calculations or mathematical and simulation types of programming where every second saved is useful, as opposed to IO heavy programs where only a certain amount of speedup is helpful.
While changing the algorithm is frequently mentioned as the most effective method here,I’m trying to find out how effective different algorithms are in the first place, so I want to create as much efficiency with each algorithm as is possible. The “problem” I’m solving isn’t something thats well known, so there are few if any algorithms on the web, but I’m looking for any good advice on how to proceed and what to look for.
I am exploring the differences in effectiveness between evolutionary algorithms and more straightforward approaches for a particular group of related problems. I have written three evolutionary algorithms for the problem already and now I have written an brute force technique that I am trying to make as fast as possible.
Edit: To specify a bit more. I am using C# and my algorithms all revolve around calculating and solving constraint type problems for expressions (using expression trees). By expressions I mean things like x^2 + 4 or anything else like that which would be parsed into an expression tree. My algorithms all create and manipulate these trees to try to find better approximations. But I wanted to put the question out there in a general way in case it would help anyone else.
I am trying to find out if it is possible to write a useful evolutionary algorithm for finding expressions that are a good approximation for various properties. Both because I want to know what a good approximation would be and to see how the evolutionary stuff compares to traditional methods.
If your optimization problem is (quasi-)convex or can be transformed into such a form, there are far more efficient algorithms than evolutionary search.
If you have large matrices, pay attention to your linear algebra routines. The right algorithm can make shave an order of magnitude off the computation time, especially if your matrices are sparse.
Think about how data is loaded into memory. Even when you think you’re spending most of your time on pure arithmetic, you’re actually spending a lot of time moving things between levels of cache etc. Do as much as you can with the data while it’s in the fastest memory.
Try to avoid unnecessary memory allocation and de-allocation. Here’s where it can make sense to back away from a purely OO approach.