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Michael Zucchi

 B.E. (Comp. Sys. Eng.)

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Saturday, 01 October 2011, 09:26

Images vs Arrays 3

So i've been working on some code that works with lots of 2d array data: which gives me the option of using arrays or images.

And ... arrays won out: for simple memory access and writing they are somewhat faster than using images. And that's before you add the ability to batch-process: with images you're pretty much stuck with having to pass each one at a time and only pack up to 4 values in each element (3D image writes are not supported on my platform atm). With arrays you can process multiple 2D levels at once, or even flatten them if they are element-by-element - which can allow you to better fit the problem to the available CUs.

In some cases the improvements were dramatic where a lot of writes to different arrays were required (but the writes were otherwise independent).

Anyway, one particular area I thought images would still be a noticeable win is with some interpolation code I had to implement. I need to do fixed power of 2 scaling up and down. Apart from the bi-linear interpolation 'for free', there is also an interesting note in graphics gems 2 about using the bi-linear interpolation of the texture unit to perform bi-cubic interpolation using only 4 texture fetches rather than 16.

So I ran some tests with both an image and array implementation of the following algorithms:

  1. Bi-linear interopolation.
  2. Fast Bi-cubic using the graphics gems algorithm with a 64-element lookup table (I found the lookup-table version significantly faster than the calculated one).
  3. Bi-cubic using 64-element lookup tables generated from the convolution algorithm in wikipedia.

In both cases I was using float data, a 512x512 image, and 4x scaling in X and Y, and the numbers are in uS from the Nvidia profiler. The array implementation is doing CLAMP_TO_EDGE.

The results were quite interesting.

                        Image           Array
bi-linear               40              36
fast bi-cubic           56              79
table bi-cubic          106             63

With this sort of regular access, the array version of the bi-linear interpolation is actually slightly faster than the image version, although they approach each other as the scale approaches 1. This is a bit surprising.

Images do win out for bi-cubic interpolation, but the array version isn't too far off.

And in either case, the bi-cubic interpolation is really fairly cheap: only about 1.5x the cost of bi-linear which is 'pretty cool' considering how much more work is being done.

I also started to investigate a bi-cubic interpolator that uses local memory to cache the region being processed by the local work-group. Since the actual memory lookups are very regular and the block will always access at most worksize+3 elements of data (for scaling=1) it seemed like a good fit. I just tried a single 64x1 workgroup and managed around 60uS with some slightly-broken code: so perhaps the gap could be closed further.

Actually one problem I have is a little more complicated than this anyway: the samples I need to work on are not the base samples, but offset by half a pixel first to produce N+1 of them. With arrays I can use local memory to cache this calculation without having to either run a separate step or do many more lookups: so in this case it will almost certainly end up faster than the image version and I will have to get that local array version working.

For float4 data the images are only about 1.5x faster for this interpolation stuff: which for my problems is not enough to make up for the slower direct access. And the bicubic resampling is also 2-3 slower than the bi-linear, the amount of extra arithmetic is catching up.

Conclusions

Well, about all I conclude is that Nvidia's OpenCL implementation sucks at texture access. I've looked at some of the generated code and each image lookup generates a large chunk of code that appears to be a switch statement. For very big problems most of this can be hidden with overlapped processing but for smaller problems it can be fairly significant. I'm surprised that they, or OpenCL doesn't have some way of telling the compiler that a given image2d_t is always a specific type: the access could be optimised then. FWIW I'm using a driver from a few months ago.

Also I guess: the global memory cache isn't too bad if you have a good regular memory access pattern. Even optimised code that resulted in 4 simple coalesced global memory accesses per thread vs 16 was only a slight improvement.

Of course the other conclusion is that it's a fairly simple problem and no amount of 'cache' optimisation will hide the fact that at some point you still need to go to main memory, for the same amount of data.

I should really do some timings on AMD HW for comparison ... but the computer is in the next room which is also cold and dark.

Final Thought

If you really are doing image stuff with affine transformations and so on, then images are going to win because the access pattern will be regular but it wont be rectangular. The data-types available also match images.

But for scientific computing where you are accessing arrays, images are not going to give you any magical boost on current hardware and can sometimes be more difficult to use. They also add more flexible memory management (e.g. i can use the same memory buffer for smaller or multiple images) and the ability to batch in the 3rd dimension.

Tagged opencl, socles.
Images vs Arrays 4 | LU Decomposition 2
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