My question is specific to iPhone, iPod, and iPad, since I am assuming that the architecture makes a big difference. I’m hoping there is either a specification somewhere (for the various chips perhaps), or a reliable way to measure T for each specific instruction. I know I can use any number of tools to measure aggregate processor time used, memory used, etc. I want to quantify at a lower level.
So, I’m able to figure out how many times I go through the main part of the algorithm. For example, I iterate n * (n-1) times in a naive implementation, and between n (best case) and n + n * (n-1) (worst case) in another. I can also make a reasonable count of the total number of instructions (+ – = % * /, and logic statements), and I can compare those counts, but that’s assuming the weight of each operation is the same. Also, I don’t have any idea how to weight the actual time value of a logic statement (if, else, for, while) vs a mathematical operator… is “if” as much work as “+” each time I use it? I would love to know where to find this information.
So, for clarity, my goal is to discover how much processor time I am demanding of the CPU (or GPU or any U) so that I can design an optimal algorithm around processor time. Can someone give me an idea of where to start for iOS hardware?
Edit: This link to ClockServices.c and SIMD stuff in the developer portal might be a good start for people interested in this. A few more cups of coffee tonight and I might get through it 😉
On a modern platform, processor time isn’t the only limiting factor. Often, memory access is.
Still, processor time:
Your basic approach at an estimation for the processor load is OK, though, and is sensible: Make a rough estimate of the cost based on your knowledge of typical platforms.
In this article, Table 1 shows the times for typical primitive operations in .NET. While your platform may vary, the relative time is usually very similar. Maybe you can find – or even make – one for iStuff.
(I haven’t come across one so thorough for other platforms, except processor / instruction set manuals, but they deal with assembly instructions)
memory locality:
A cache miss can cost you hundreds of cycles, a disk access a thousand times as much. So controlling your memory access patterns (i.e. reducing the working set, restructuring and accessing data in a cache-friendly way) is an important part of evaluating an algorithm.