I'd like to welcome guest blogger Anand Raja for today's post. Anand is a developer on the Image Processing Toolbox team. -Steve
Many desktop computers and laptops now come with fairly powerful Graphics Processing Units (GPU's). Initially, GPU's were mostly used to power computations for graphics applications, but soon people realized that they are just as useful for any kind of numerical computing.
GPU's are made of a large number of processing units which by themselves aren't very powerful, but become formidable when used in tandem. So, if you have processing to be done that is parallelizable, the GPU will be a great fit.
With that in mind, isn't it almost obvious that image processing is a great fit for GPU's! A lot of image processing algorithms are data-parallel, meaning the same task/computation needs to be performed on many elements of the data. Lots of image processing algorithms either operate on pixels independantly or rely only on a neighborhood around pixels (like image filtering).
So, lets get down to it. My desktop computer has a GPU, and I want to do some image processing using my favorite software (no prizes for guessing), MATLAB. Note that in order to interact with the GPU from MATLAB, you require the Parallel Computing Toolbox.
I can use the gpuDevice function to get information about my GPU.
ans = CUDADevice with properties: Name: 'Tesla C2075' Index: 1 ComputeCapability: '2.0' SupportsDouble: 1 DriverVersion: 5.5000 ToolkitVersion: 5 MaxThreadsPerBlock: 1024 MaxShmemPerBlock: 49152 MaxThreadBlockSize: [1024 1024 64] MaxGridSize: [65535 65535 65535] SIMDWidth: 32 TotalMemory: 5.6368e+09 FreeMemory: 5.5362e+09 MultiprocessorCount: 14 ClockRateKHz: 1147000 ComputeMode: 'Default' GPUOverlapsTransfers: 1 KernelExecutionTimeout: 0 CanMapHostMemory: 1 DeviceSupported: 1 DeviceSelected: 1
Seeing that I have a supported GPU, I can read an image and transfer the image data to my GPU using the constructor for the gpuArray class. The gpuArray object is used to access and work with data on the GPU.
im = imread('concordaerial.png'); imGPU = gpuArray(im); imshow(imGPU);
So imGPU is a gpuArray object containing data of type uint8.
ans = gpuArray ans = uint8
A number of the functions in the Image Processing Toolbox have support for GPU processing in R2013b. This means you can accelerate existing MATLAB scripts and functions with minimal changes. To find the list of functions that are supported for GPU processing in the Image Processing Toolbox, you can visit this page. Some of the basic image processing algorithms like image filtering, morphology and edge detection have GPU support and this list is going to grow in the coming releases.
Let's look at a small example to set the ball rolling. Inspired by Brett Schoelson's guest post a few months back about Photoshop-like effects in MATLAB, I thought I might do one of my own. I call it the canvas effect . The canvas effect gives an image the feel of a canvas painting. I had created this little function that does it.
function out = canvasEffect(im) % Filter the image with a Gaussian kernel. h = fspecial('gaussian'); imf = imfilter(im,h); % Increase image contrast for each color channel. ima = cat( 3, imadjust(imf(:,:,1)), imadjust(imf(:,:,2)), imadjust(im(:,:,3)) ); % Perform a morphological closing on the image with a 11x11 structuring % element. se = strel('disk',9); out = imopen(ima,se);
It's fairly straight-forward. I first smooth the image with a Gaussian kernel to round off some edges. Then to give the effect of more vivid colors, I increase the contrast for each color channel and finally a morphological opening gives it the canvas painting look. Ofcourse, you could add more bells and whistles by providing additional inputs for the filter kernel size and structuring element, but I wanted to keep it simple.
The script below reads an aerial image and gives it that canvas painting effect.
% Read the image. im = imread('concordaerial.png'); % Produce canvas effect. canvas = canvasEffect(im); %Display the canvas-ed image. figure; imshow(canvas);
All the processing in the script above was done on the CPU. To move the computation to the GPU, I need to transfer the image from the CPU to the GPU using the gpuArray constructor. So the new script would like this:
type canvasAerialGPU run canvasAerialGPU
% Read the image. im = imread('concordaerial.png'); % Transfer data to the GPU. imGPU = gpuArray(im); % Produce canvas effect. canvasGPU = canvasEffect(imGPU); % Gather data back from the GPU. canvas = gather(canvasGPU); %Display the canvas-ed image. figure; imshow(canvas);
Wasn't that easy! All I had to do was convert the image to a gpuArray and gather data back after all the computation was done. The function canvasEffect did not have to change at all. This was because all functions used in canvasEffect were supported for GPU computing.
Let's see how much of a win this is in terms of performance. For a few years now I've been using the timeit function that Steve put on the File Exchange. From R2013b, the timeit function is part of MATLAB.
cpuTime = timeit(@()canvasEffect(im), 1)
cpuTime = 3.1311
This function however can only be used to benchmark computations undertaken by the CPU. For the GPU, a special benchmarking function gputimeit has been provided. This function ensures that all computations have completed on the GPU before recording the finish time.
gpuTime = gputimeit(@()canvasEffect(imGPU), 1)
gpuTime = 0.2130
So with these small changes, I was able to get a considerable speed-up. Imagine having to do this on an entire data set of images. Working with the GPU would save a lot of processing time.
speedup = cpuTime/gpuTime
speedup = 14.6990
This is not the complete picture though. I have not accounted for the time it takes to transfer data from the CPU to the GPU and back. This may or may not be significant, depending on how long the computations themselves take. As a rule of thumb, minimize data transfers to and from the device.
transferTimeToGPU = gputimeit(@()gpuArray(im), 1) transferTimeToCPU = gputimeit(@()gather(canvasGPU), 1) gpuTime = transferTimeToGPU + gpuTime + transferTimeToCPU; speedup = cpuTime/gpuTime
transferTimeToGPU = 0.0037 transferTimeToCPU = 0.0074 speedup = 13.9753
I'm going to end with some pointers about the performance of GPU processing.
- We've seen in the simple example above that you can get a significant speed-up using the supported functions. However, this speed-up is highly dependent on your hardware. If you have a very capable CPU with multiple cores and a not-so-good GPU, the speed-up can appear to be poor because functions like imfilter and imopen are multi-threaded on the CPU. Similarly, if you have a reasonable GPU on a not-so-capable CPU, you're speed-up can make you're GPU execution look faster than it is.
- The speed-up achieved is dependent on image size. At smaller image sizes, the overhead of parsing input arguments and moving data to and from the GPU contribute to lower speed-ups. Here's an example that demonstrates this.
% Define image sizes over which to measure performance. sizes = [100 500 2000 4000]; % Preallocate timing arrays. [cpuTime,gpuTime,transferTimeToGPU,transferTimeToCPU] = deal(zeros('like',sizes)); for n = 1 : numel(sizes) size = sizes(n); % Resize image to size x size. im_scaled = imresize(im,[size size]); % Transfer resized image to GPU. imGPU_scaled = gpuArray(im_scaled); % Process image on GPU. canvasGPU_scaled = canvasEffect(imGPU_scaled); % Time CPU execution. cpuTime(n) = timeit(@()canvasEffect(im_scaled), 1); % Time GPU execution. transferTimeToGPU(n) = gputimeit(@()gpuArray(im_scaled) , 1); gpuTime(n) = gputimeit(@()canvasEffect(imGPU_scaled), 1); transferTimeToCPU(n) = gputimeit(@()gather(canvasGPU_scaled) , 1); end gpuTotalTime = transferTimeToGPU+gpuTime+transferTimeToCPU; % Plot CPU vs GPU execution figure; plot(sizes, cpuTime, 'rx--',... sizes, gpuTotalTime,'bx--',... 'LineWidth',2); legend('cpu time','gpu time'); xlabel('image size [n x n]'); ylabel('execution time'); title('cpu time vs gpu time'); figure; plot(sizes,cpuTime./gpuTotalTime,'LineWidth',2); xlabel('image size [n x n]'); ylabel('speed up'); title('Speed up');
I hope this got you as excited about image processing with GPU's as it did me!
Get the MATLAB code
Published with MATLAB® R2013b
8 CommentsOldest to Newest
Could you comment on what happens when the image size begins to approach (and exceed) the memory available on the GPU?
How is memory management done with gpu computing? If I have other software concurrently utilizing gpu memory, do I need to preallocate gpu memory in Matlab to ensure I get it when I need it? What happens if I don’t, etc. etc.
I work with a large images and have not utilized GPU processing in the past to it’s full advantage primarily due to memory management uncertainties. Any general advice is welcome!
SNICK, you would need to manage the memory available on the GPU yourself. When you cross the memory available on the GPU, MATLAB will issue an error indicating so. You can keep a tab on the amount of memory available using the gpuDevice function. The returned object has a property ‘FreeMemory’ which would indicate the amount of memory available.
If another software application is concurrently utilizing GPU memory, MATLAB and the other software will share the available memory. Memory requests are handled on a per-request basis, depending on availability. There is no way to pre-allocate GPU memory for the MATLAB application.
Hope that helps.
Some functions although they said they work with gpu support but they don’t actually work like edge for example and histeq and some others.
Amr, the functions you mentioned were added in R2013b release of the Image Processing Toolbox. It looks like you may have an earlier release.
The following links show functions supported by release:
If you find that these functions don’t work in the appropriate release, get back with the exact error message and I’ll be glad to help.
Okay , i see the problem with the matlab release , but how about doing some computer vision , is it supported like
Viola jones, feature extraction and matching for example?
Capabilities for Viola Jones Haar Cascade classifiers, feature extraction and matching are all available in the Computer Vision System Toolbox (http://www.mathworks.com/help/vision/), but are not supported for processing on the GPU.
I’d be glad to send this over as an enhancement request to the Computer Vision Team.
Anand – thanks for your reply. That’s what I expected – but was hoping for some trick ;-)
Thanks Anand very much , that was very helpful. i hope that Bioinformatics is GPU enabled too.