I was wondering if I could get some help to make this code run faster. I have a few ideas, which all involve taking out the RIDX function.

#define RIDX(i,j,n) ((i)*(n)+(j))

I think I can reduce the computation in this statement:

src[RIDX(i, j, dim)];

when i is 0, its basically counting j... so I was thinking, what if we changed the bounds of the loop to:
for (i = 0; i < (dim * dim); i++)

And just had a j++ instead of RIDX(i, j, dim)??

I'm not so sure what to do about dst[RIDX(dim-1-j, i, dim)] though... could you please help me out?

Thanks in advance!

void naive_rotate(int dim, pixel *src, pixel *dst) { int i, j; for (i = 0; i < dim; i++) for (j = 0; j < dim; j++) dst[RIDX(dim-1-j, i, dim)] = src[RIDX(i, j, dim)];}

First, I think that, you cannot use "for (i = 0; i < (dim * dim); i++)". Because, what if the the src or dst pointer's bound exceeds dim value? I think that dim is the total number of items in the src & dst pointers.

As u said, you can reduce 1 iteration for the i value = 0.
--------
for (j = 0, i=0; j < dim; j++, i++)
dst[(dim-1-j)*dim] = src[j];
--------

In the above code, u saved 1+1 add operation and 2 subtract & 1 multiply operations.

Then you can use the following statement...
--------
for(i=1; i<dim; i++)
for(j=0; j<dim;j++)
{
if(j==0)
{
dst[((dim - 1)*dim)+i]=src[i*dim];
}
else
{
dst[RIDX(dim-1-j, i, dim)] = src[RIDX(i, j, dim)];
}
}
-----
In the above statement, you saved 2 add operations when j=0. so, (dim-1)*2 add operations reduced by above code.

for (i = 0; i < dim; i++){
for( sj=&src[i][0],dj=dst[dim-1];sj<&src[i][dim];sj++,dj--){
dj[i]=*sj;
}
}
You might fold a little more, depending on how dst is declared, and how dst[0[ and dst[0][0] are alligned

Or a smart enough compiler might have optimized even the dst[RIDX(dim-1-j, i, dim)] = src[RIDX(i, j, dim)] loop even without any help

These kind of optimizations are compiler dependent. ie. they might give better performance on one specific compiler/platform, but worse performance on a different compiler/platform.

The only optimizations you should worry about, are algorithmic optimizations - leave all the rest (code reordering and the likes) to the compiler.
Only think about overriding the compiler's optimizations (or lack thereof) when an actual performance problem is discovered, which absolutely needs to be fixed (for a certain compiler/platform).

What reason for optimizing it did your professor give ? Did he point out which part he thinks is slow ? Did he give any clue as to the kind of optimization that he wants ? Does this have to be optimized for memory caching ? For fastest time ? Least data accesses ?

The primary reason for a function like this one to use this type of access pattern is when it's being called for images of different dimensions. The 2-d approaches are only useful if one of the dimensions (usually columns) is fixed in advance.

>>What reason for optimizing it did your professor give ? Did he point out which part he thinks is slow ? Did he give any clue as to the kind of optimization that he wants ? Does this have to be optimized for memory caching ? For fastest time ? Least data accesses ?

Far as reason goes, he just wants to see the best we can do, and it has to be optimized for the best processing time.

go a bit faster... but I was wondering if I'm understanding the code correctly:

for (i=0; i<dim; i++) { <=======this loop controls the array indexes, correct?
dst_idx = (dim-1)*dim + i; <=======this statement is a replacement for RIDX(dim-1-j, i, dim), right?
for (j=0; j<dim; j++) { <=======this loop is copying the source to the destination?
dst[dst_idx] = *src++; <=======I'm a little confused about the *src++ statement, it kept giving me errors when I tried to say something like *src + 2; is this a regular increment operator?
dst_idx -= dim; <=======since this is subtracting stuff, I'm thinking you are working backwards through the loop, right?
}
}

//how about
dstp=&dst[dim*dim-1];
for (i=dim; i>0; --i) {
dst=++dstp;
for (j=dim; j>0; --j) {
*(dst-=dim)= *src++;
}
}
//but note that if you have a dim*dim array object, creating pointers more than one past the end of the object is undefined behaviour, even if those pointers are not dereferenced.

What CPU are you working on ? Will your professor use the same CPU when he's testing your program ? Cache optimization is definitely platform specific, and good performance on your system might result in bad performance on his.

Also, will you deliver the binary ? Or the code ? In the latter case, will you be using the same compiler ?

>>What CPU are you working on ? Will your professor use the same CPU when he's testing your program ?

I'm not exactly sure what type of CPU it is, we are using a university server, so he will be using the same CPU. If the CPU on my computer has any impact on this, I'm using a AMD 64 X2 Dual 4200+ 2.2 ghz processor, and he will take the result I get on my computer into consideration.

So, you're using different CPU's, the same compiler (but possibly different versions of the compiler ?), but not necessarily the same compiler flags.

That's an impossible scenario for this kind of assignment. You need to perform platform dependent optimizations, and test it in a different environment than the one it's actually being run on.

Unless you're doing your development on the university server. If so, if you want to optimize for that server, you need to know some more details about the hardware. If you don't know the hardware, can you at least tell what OS it's running ?

>>So, you're using different CPU's, the same compiler (but possibly different versions of the compiler ?), but not necessarily the same compiler flags.

no, we'r using the same version of the compiler and the same flags, I've got a makefile for this assignment.

>>Unless you're doing your development on the university server. If so, if you want to optimize for that server, you need to know some more details about the hardware. If you don't know the hardware, can you at least tell what OS it's running ?

I don't know what the universty's hardware is like, but I know its running a sun solaris operating system.

>>That's an impossible scenario for this kind of assignment. You need to perform platform dependent optimizations, and test it in a different environment than the one it's actually being run on.

That's... not quite encouraging me =( lol. My professor is going to take the result I get on my computer into consideration.

Hm... I tried loop unrolling, I saw that the test cases were all multiples of 32, so this is what I got:

How would I apply the cache blocking concept to this? I'm still not sure what it is... but I did remember that my book said something about instructions finishing in one go? So does that mean I need to plan out how I word my statements so they fit the cache size?

int i, j; for (i = 0; i < dim; i++) for (j = 0; j < dim; j+=32){ dst[RIDX(dim-1-j, i, dim)] = src[RIDX(i, j, dim)]; dst[RIDX(dim-1-(j+1), i, dim)] = src[RIDX(i, (j+1), dim)]; dst[RIDX(dim-1-(j+2), i, dim)] = src[RIDX(i, (j+2), dim)]; dst[RIDX(dim-1-(j+3), i, dim)] = src[RIDX(i, (j+3), dim)]; dst[RIDX(dim-1-(j+4), i, dim)] = src[RIDX(i, (j+4), dim)]; dst[RIDX(dim-1-(j+5), i, dim)] = src[RIDX(i, (j+5), dim)]; dst[RIDX(dim-1-(j+6), i, dim)] = src[RIDX(i, (j+6), dim)]; dst[RIDX(dim-1-(j+7), i, dim)] = src[RIDX(i, (j+7), dim)]; dst[RIDX(dim-1-(j+8), i, dim)] = src[RIDX(i, (j+8), dim)]; dst[RIDX(dim-1-(j+9), i, dim)] = src[RIDX(i, (j+9), dim)]; dst[RIDX(dim-1-(j+10), i, dim)] = src[RIDX(i, (j+10), dim)]; dst[RIDX(dim-1-(j+11), i, dim)] = src[RIDX(i, (j+11), dim)]; dst[RIDX(dim-1-(j+12), i, dim)] = src[RIDX(i, (j+12), dim)]; dst[RIDX(dim-1-(j+13), i, dim)] = src[RIDX(i, (j+13), dim)]; dst[RIDX(dim-1-(j+14), i, dim)] = src[RIDX(i, (j+14), dim)]; dst[RIDX(dim-1-(j+15), i, dim)] = src[RIDX(i, (j+15), dim)]; dst[RIDX(dim-1-(j+16), i, dim)] = src[RIDX(i, (j+16), dim)]; dst[RIDX(dim-1-(j+17), i, dim)] = src[RIDX(i, (j+17), dim)]; dst[RIDX(dim-1-(j+18), i, dim)] = src[RIDX(i, (j+18), dim)]; dst[RIDX(dim-1-(j+19), i, dim)] = src[RIDX(i, (j+19), dim)]; dst[RIDX(dim-1-(j+20), i, dim)] = src[RIDX(i, (j+20), dim)]; dst[RIDX(dim-1-(j+21), i, dim)] = src[RIDX(i, (j+21), dim)]; dst[RIDX(dim-1-(j+22), i, dim)] = src[RIDX(i, (j+22), dim)]; dst[RIDX(dim-1-(j+23), i, dim)] = src[RIDX(i, (j+23), dim)]; dst[RIDX(dim-1-(j+24), i, dim)] = src[RIDX(i, (j+24), dim)]; dst[RIDX(dim-1-(j+25), i, dim)] = src[RIDX(i, (j+25), dim)]; dst[RIDX(dim-1-(j+26), i, dim)] = src[RIDX(i, (j+26), dim)]; dst[RIDX(dim-1-(j+27), i, dim)] = src[RIDX(i, (j+27), dim)]; dst[RIDX(dim-1-(j+28), i, dim)] = src[RIDX(i, (j+28), dim)]; dst[RIDX(dim-1-(j+29), i, dim)] = src[RIDX(i, (j+29), dim)]; dst[RIDX(dim-1-(j+30), i, dim)] = src[RIDX(i, (j+30), dim)]; dst[RIDX(dim-1-(j+31), i, dim)] = src[RIDX(i, (j+31), dim)]; }

neither of those loop unrolling blocks will work if the image isn't a multiple of 4.

You should break it up into one piece that is a multiple of N and another that takes care of the remainder. Otherwise, you've taken a function that works for all images and made it work only for those that are divisible by your unrolling factor.

search for "cache blocking".... your computer has a fast cache so that recently accessed memory locations are in a fast local cache. You want to avoid cache misses, so you should try to continue to access the same addresses multiple times if necessary, rather than jumping around amongst addresses. Cache sizes vary, but it's expensive memory on the processor, so typical ranges may be 256k to 4M <that's a bit of a guess>.

I don't see how cache blocking applies in this case. It does in the smoothing algorithm -- because each pixel ends up contributing to the avg of itself and all its neighbors.

Here, you're using each src and dst only once. There are other cache effects, such as disk block caching, but since you can access either the src or dst array in order, but not both at the same time, as long as you're doing one of them, you can't do any better.

hm... this goes against everything I've been taught... but I'm just brute forcing it right now...

The best general algorithm was the one by ozo:

dstp=&dst[dim*dim-1];
for (i=dim; i>0; --i) {
dst=++dstp;
for (j=dim; j>0; --j) {
*(dst-=dim)= *src++;
}
}

it gave this result:
Rotate: Version = rotate: Current working version:
Dim 64 128 256 512 1024 Mean
Your CPEs 5.0 4.6 15.9 25.6 165.5
Baseline CPEs 10.0 10.2 17.9 25.3 127.1
Speedup 2.0 2.2 1.1 1.0 0.8 1.3

But... with the brute force approach...
Rotate: Version = rotate: Current working version:
Dim 64 128 256 512 1024 Mean
Your CPEs 5.5 0.5 14.9 25.0 153.7
Baseline CPEs 10.0 10.2 17.9 25.3 127.1
Speedup 1.8 20.4 1.2 1.0 0.8 2.1

int i, j; for (i = 0; i < dim; i++){ if((dim%64) == 0) for (j = 0; j < dim; j+=64){ dst[RIDX(dim-1-(j), i, dim)] = src[RIDX(i, (j), dim)]; dst[RIDX(dim-1-(j+1), i, dim)] = src[RIDX(i, (j+1), dim)]; dst[RIDX(dim-1-(j+2), i, dim)] = src[RIDX(i, (j+2), dim)]; dst[RIDX(dim-1-(j+3), i, dim)] = src[RIDX(i, (j+3), dim)]; dst[RIDX(dim-1-(j+4), i, dim)] = src[RIDX(i, (j+4), dim)]; dst[RIDX(dim-1-(j+5), i, dim)] = src[RIDX(i, (j+5), dim)]; dst[RIDX(dim-1-(j+6), i, dim)] = src[RIDX(i, (j+6), dim)]; dst[RIDX(dim-1-(j+7), i, dim)] = src[RIDX(i, (j+7), dim)]; dst[RIDX(dim-1-(j+8), i, dim)] = src[RIDX(i, (j+8), dim)]; dst[RIDX(dim-1-(j+9), i, dim)] = src[RIDX(i, (j+9), dim)]; dst[RIDX(dim-1-(j+10), i, dim)] = src[RIDX(i, (j+10), dim)]; dst[RIDX(dim-1-(j+11), i, dim)] = src[RIDX(i, (j+11), dim)]; dst[RIDX(dim-1-(j+12), i, dim)] = src[RIDX(i, (j+12), dim)]; dst[RIDX(dim-1-(j+13), i, dim)] = src[RIDX(i, (j+13), dim)]; dst[RIDX(dim-1-(j+14), i, dim)] = src[RIDX(i, (j+14), dim)]; dst[RIDX(dim-1-(j+15), i, dim)] = src[RIDX(i, (j+15), dim)]; dst[RIDX(dim-1-(j+16), i, dim)] = src[RIDX(i, (j+16), dim)]; dst[RIDX(dim-1-(j+17), i, dim)] = src[RIDX(i, (j+17), dim)]; dst[RIDX(dim-1-(j+18), i, dim)] = src[RIDX(i, (j+18), dim)]; dst[RIDX(dim-1-(j+19), i, dim)] = src[RIDX(i, (j+19), dim)]; dst[RIDX(dim-1-(j+20), i, dim)] = src[RIDX(i, (j+20), dim)]; dst[RIDX(dim-1-(j+21), i, dim)] = src[RIDX(i, (j+21), dim)]; dst[RIDX(dim-1-(j+22), i, dim)] = src[RIDX(i, (j+22), dim)]; dst[RIDX(dim-1-(j+23), i, dim)] = src[RIDX(i, (j+23), dim)]; dst[RIDX(dim-1-(j+24), i, dim)] = src[RIDX(i, (j+24), dim)]; dst[RIDX(dim-1-(j+25), i, dim)] = src[RIDX(i, (j+25), dim)]; dst[RIDX(dim-1-(j+26), i, dim)] = src[RIDX(i, (j+26), dim)]; dst[RIDX(dim-1-(j+27), i, dim)] = src[RIDX(i, (j+27), dim)]; dst[RIDX(dim-1-(j+28), i, dim)] = src[RIDX(i, (j+28), dim)]; dst[RIDX(dim-1-(j+29), i, dim)] = src[RIDX(i, (j+29), dim)]; dst[RIDX(dim-1-(j+30), i, dim)] = src[RIDX(i, (j+30), dim)]; dst[RIDX(dim-1-(j+31), i, dim)] = src[RIDX(i, (j+31), dim)]; dst[RIDX(dim-1-(j+32), i, dim)] = src[RIDX(i, (j+32), dim)]; dst[RIDX(dim-1-(j+33), i, dim)] = src[RIDX(i, (j+33), dim)]; dst[RIDX(dim-1-(j+34), i, dim)] = src[RIDX(i, (j+34), dim)]; dst[RIDX(dim-1-(j+35), i, dim)] = src[RIDX(i, (j+35), dim)]; dst[RIDX(dim-1-(j+36), i, dim)] = src[RIDX(i, (j+36), dim)]; dst[RIDX(dim-1-(j+37), i, dim)] = src[RIDX(i, (j+37), dim)]; dst[RIDX(dim-1-(j+38), i, dim)] = src[RIDX(i, (j+38), dim)]; dst[RIDX(dim-1-(j+39), i, dim)] = src[RIDX(i, (j+39), dim)]; dst[RIDX(dim-1-(j+40), i, dim)] = src[RIDX(i, (j+40), dim)]; dst[RIDX(dim-1-(j+41), i, dim)] = src[RIDX(i, (j+41), dim)]; dst[RIDX(dim-1-(j+42), i, dim)] = src[RIDX(i, (j+42), dim)]; dst[RIDX(dim-1-(j+43), i, dim)] = src[RIDX(i, (j+43), dim)]; dst[RIDX(dim-1-(j+44), i, dim)] = src[RIDX(i, (j+44), dim)]; dst[RIDX(dim-1-(j+45), i, dim)] = src[RIDX(i, (j+45), dim)]; dst[RIDX(dim-1-(j+46), i, dim)] = src[RIDX(i, (j+46), dim)]; dst[RIDX(dim-1-(j+47), i, dim)] = src[RIDX(i, (j+47), dim)]; dst[RIDX(dim-1-(j+48), i, dim)] = src[RIDX(i, (j+48), dim)]; dst[RIDX(dim-1-(j+49), i, dim)] = src[RIDX(i, (j+49), dim)]; dst[RIDX(dim-1-(j+50), i, dim)] = src[RIDX(i, (j+50), dim)]; dst[RIDX(dim-1-(j+51), i, dim)] = src[RIDX(i, (j+51), dim)]; dst[RIDX(dim-1-(j+52), i, dim)] = src[RIDX(i, (j+52), dim)]; dst[RIDX(dim-1-(j+53), i, dim)] = src[RIDX(i, (j+53), dim)]; dst[RIDX(dim-1-(j+54), i, dim)] = src[RIDX(i, (j+54), dim)]; dst[RIDX(dim-1-(j+55), i, dim)] = src[RIDX(i, (j+55), dim)]; dst[RIDX(dim-1-(j+56), i, dim)] = src[RIDX(i, (j+56), dim)]; dst[RIDX(dim-1-(j+57), i, dim)] = src[RIDX(i, (j+57), dim)]; dst[RIDX(dim-1-(j+58), i, dim)] = src[RIDX(i, (j+58), dim)]; dst[RIDX(dim-1-(j+59), i, dim)] = src[RIDX(i, (j+59), dim)]; dst[RIDX(dim-1-(j+60), i, dim)] = src[RIDX(i, (j+60), dim)]; dst[RIDX(dim-1-(j+61), i, dim)] = src[RIDX(i, (j+61), dim)]; dst[RIDX(dim-1-(j+62), i, dim)] = src[RIDX(i, (j+62), dim)]; dst[RIDX(dim-1-(j+63), i, dim)] = src[RIDX(i, (j+63), dim)]; } else if((dim%32) == 0) for (j = 0; j < dim; j+=32){ dst[RIDX(dim-1-(j), i, dim)] = src[RIDX(i, (j), dim)]; dst[RIDX(dim-1-(j+1), i, dim)] = src[RIDX(i, (j+1), dim)]; dst[RIDX(dim-1-(j+2), i, dim)] = src[RIDX(i, (j+2), dim)]; dst[RIDX(dim-1-(j+3), i, dim)] = src[RIDX(i, (j+3), dim)]; dst[RIDX(dim-1-(j+4), i, dim)] = src[RIDX(i, (j+4), dim)]; dst[RIDX(dim-1-(j+5), i, dim)] = src[RIDX(i, (j+5), dim)]; dst[RIDX(dim-1-(j+6), i, dim)] = src[RIDX(i, (j+6), dim)]; dst[RIDX(dim-1-(j+7), i, dim)] = src[RIDX(i, (j+7), dim)]; dst[RIDX(dim-1-(j+8), i, dim)] = src[RIDX(i, (j+8), dim)]; dst[RIDX(dim-1-(j+9), i, dim)] = src[RIDX(i, (j+9), dim)]; dst[RIDX(dim-1-(j+10), i, dim)] = src[RIDX(i, (j+10), dim)]; dst[RIDX(dim-1-(j+11), i, dim)] = src[RIDX(i, (j+11), dim)]; dst[RIDX(dim-1-(j+12), i, dim)] = src[RIDX(i, (j+12), dim)]; dst[RIDX(dim-1-(j+13), i, dim)] = src[RIDX(i, (j+13), dim)]; dst[RIDX(dim-1-(j+14), i, dim)] = src[RIDX(i, (j+14), dim)]; dst[RIDX(dim-1-(j+15), i, dim)] = src[RIDX(i, (j+15), dim)]; dst[RIDX(dim-1-(j+16), i, dim)] = src[RIDX(i, (j+16), dim)]; dst[RIDX(dim-1-(j+17), i, dim)] = src[RIDX(i, (j+17), dim)]; dst[RIDX(dim-1-(j+18), i, dim)] = src[RIDX(i, (j+18), dim)]; dst[RIDX(dim-1-(j+19), i, dim)] = src[RIDX(i, (j+19), dim)]; dst[RIDX(dim-1-(j+20), i, dim)] = src[RIDX(i, (j+20), dim)]; dst[RIDX(dim-1-(j+21), i, dim)] = src[RIDX(i, (j+21), dim)]; dst[RIDX(dim-1-(j+22), i, dim)] = src[RIDX(i, (j+22), dim)]; dst[RIDX(dim-1-(j+23), i, dim)] = src[RIDX(i, (j+23), dim)]; dst[RIDX(dim-1-(j+24), i, dim)] = src[RIDX(i, (j+24), dim)]; dst[RIDX(dim-1-(j+25), i, dim)] = src[RIDX(i, (j+25), dim)]; dst[RIDX(dim-1-(j+26), i, dim)] = src[RIDX(i, (j+26), dim)]; dst[RIDX(dim-1-(j+27), i, dim)] = src[RIDX(i, (j+27), dim)]; dst[RIDX(dim-1-(j+28), i, dim)] = src[RIDX(i, (j+28), dim)]; dst[RIDX(dim-1-(j+29), i, dim)] = src[RIDX(i, (j+29), dim)]; dst[RIDX(dim-1-(j+30), i, dim)] = src[RIDX(i, (j+30), dim)]; dst[RIDX(dim-1-(j+31), i, dim)] = src[RIDX(i, (j+31), dim)]; }}

if src[RIDX(i, j, dim)] and src[RIDX(i, (j+1), dim)] are in the same cache block
then access can be out of fast memory
if dst[RIDX(dim-1-j, i, dim)] and dst[RIDX(dim-1-(j+1), i, dim)] are in different cache blocks
then a cache miss may mean slower access.

reordering the way you access memory to minimize this may be a more sophisticated operation than might be expected of someone with the level of programing experience that you seem to be exhibiting.

>>reordering the way you access memory to minimize this may be a more sophisticated operation than might be expected of someone with the level of programing experience that you seem to be exhibiting.

Yes... I know, like I said "this goes against everything I've been taught", but this is due in 2 days and I have to also optimize the smooth function =(. Time is not my friend.

you find that it has an L1 cache of 8kB per core and an L2 cache of 3MB.

>> That's... not quite encouraging me =( lol. My professor is going to take the result I get on my computer into consideration.

Ok. Do test and optimize it on the university server would be my advice.

>> we'r using the same version of the compiler and the same flags

I would doubt that, unless you're also running a Solaris system, on the same architecture, with the same exact version of the compiler.

>> That's... not quite encouraging me =( lol.

I'm sorry about that ;) All I want to make clear is that these kind of optimizations are very platform dependent, and might decrease the performance on one platform, while increasing performance on another. So, keep that in mind.

>>you find that it has an L1 cache of 8kB per core and an L2 cache of 3MB.

Ok... say I have that much, I still haven't had my question answered about how this works out in code, and I've asked it about 5 or 6 times by now... =(

But I'm trying to get a definite yes or no for this question...

"The first idea behind register blocking is to reorganize the matrix data structure to expose small dense blocks whose size corresponds roughly with the number of machine registers. This is shown schematically in fig. 5 (top). As implemented in SPARSity, we create register blocks by imposing a ``mask'' of tiles on top of the matrix; any tile with at least one non-zero in it is stored as a block in the new matrix. We may need to add extra zeros to make the blocks dense--this is referred to as fill.

The second idea is to keep little blocks of y and x in registers, and to access the blocks of A with unit-stride. We note that an element of y need only be referenced twice if it can be allocated to a register, instead of twice per column (one reference to load, and one to store, so that we can accumulate into the block). For an block, we save 2(c-1) accesses per "

We are supposed to break up a matrix data structure into small blocks that correspond to the size of the cache??

And could you please give a small example of how this works out in code? Like if
dst[RIDX(dim-1-j, i, dim)] = src[RIDX(i, j, dim)]; was the unmodified thing that wasn't optimized for cache blocking... what would be a way to use cache blocking on that statement?

a = RIDX(dim-1-j, i, dim);
b = RIDX(i, j, dim);
dst[a] = src[b];

Is this the basic idea? split up the computation so it can all get loaded into cache memory at once and get completed??

You have to find out how much you ACTUALLY have ;)

>> I still haven't had my question answered about how this works out in code, and I've asked it about 5 or 6 times by now... =(

All the responses made by the other experts were dealing with that, no ?

>> Is this the basic idea? split up the computation so it can all get loaded into cache memory at once and get completed??

The idea is to minimize the amount of cache misses. Make sure that each element in the matrix is loaded in the cache as little as possible (preferably only once), and more importantly, that optimal advantage is taken of the neighboring data that is loaded in the cache at the same time.

But I can't repeat it enough : until you find out what CPU you're working with, and how its caches are managed, you can only guess at what the best strategy would be (you can make an educated guess, but it's a guess nonetheless).

I'm also not too sure that you'll be able to get a big speed-up for this code.

>>You have to find out how much you ACTUALLY have ;)

yeah, i know, I was just trying to get a general idea of it.

>>All the responses made by the other experts were dealing with that, no ?

well, not quite all of them, I asked the question once or twice, got ignored, asked it a few more times, on one of those occasions I've been told to search cache blocking, I did t hat, didn't understand what it was, got my question ignored a few more times. Basically I just wanted a yes or no answer to my question =(. Or maybe I've been asking many places... I can't remember anymore... its all a big painful blur...

>>The idea is to minimize the amount of cache misses. Make sure that each element in the matrix is loaded in the cache as little as possible (preferably only once), and more importantly, that optimal advantage is taken of the neighboring data that is loaded in the cache at the same time.

That does translate to what I've kinda been blabbering about... right? limit the size of the statements so they fit in the cache?

Preface
I don't like visual development tools that are supposed to write a program for me. Even if it is Xcode and I can use Interface Builder. Yes, it is a perfect tool and has helped me a lot, mainly, in the beginning, when my programs were smallâ€¦

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This is a very simple tutorial on finding the user's current location easily. In this tutorial, you will learn hoâ€¦