How do I use my ATi 5470M for computation on MATLAB 2010a
Also, Can I enable my CPU and GPU to do the thing in parallel, together?
I'm new to GPU computing.
You can start by looking at the OpenCL Toolbox. You can also try out Jacket.
Related
I am trying to get into deep learning using the neural network library in matlab. A good starting step seems to be training an autoencoder. In that respect, it would be good to see whether I am getting the msot out of my gpu.
In this connection, When I run
tic
autoenc1 = trainAutoencoder(allSets,5,...
'L2WeightRegularization',0.001,...
'SparsityRegularization',1,...
'SparsityProportion',0.2,...
'DecoderTransferFunction','logsig',...
'useGPU',true)
toc
I get "Elapsed time is 19.680823 seconds.".
However, not using the gpu (setting 'useGPU' to false) it only takes 8.272708 seconds.
I am puzzled by this, since I am assuming that using the gpu for neural networks will speed things up? Does anyone know of any way to check whether matlab and cuda are properly interfacing, or see how matlab is actually using the resources?
I have cuda 8.1 installed, and am using a GeForce GTX 960M (compute capability 5.0). The matlab version is 2016b.
EDIT: as has been pointed out, there is as of yet no cuda 8.1. What I do have is 8.0, and cudnn 5.1.
As pointed out in the comments, performing computations on the GPU is not necessarily faster. Instead, the impact on performance depends on the additional overhead of data conversion and transfer.
Usually, the overhead can be influenced via the batch size, but the trainAutoencoder function does not provide that option.
For general measurement and improvement of GPU performance in MATLAB, see this link.
I'm using theano to implement a convolution neural network. My CPU RAM is 32G and GPU RAM is 2G, but the data is also very big -- almost 5G training data.
When the program is running, the computer seems to be frozen and each operation is really slow, even didn't respond. And the CPU mode seems to be at least 2x faster than GPU mode.
Is there any way to speed up the GPU convolutional neural network?
Make sure to use Theano 0.7 with cudnn, this speed up convolution heavily:
http://deeplearning.net/software/theano/library/sandbox/cuda/dnn.html
In order to use GPU accelleration first thing you need to install CUDA.
On the level of Theano configuration(Theano flags/TheanoRC) there are few ways you can speed-up your model with GPU:
Specify usage of GPU "device = gpu"
Enable Cuda memory allocation (CnMem) "cnmem = 0.75"
Enable CUDNN optimization "optimizer = cudnn"
You can read more about Theano config here
AccelerEyes announced in December 2012 that it works with Mathworks on the GPU code and has discontinued its product Jacket for MATLAB:
http://blog.accelereyes.com/blog/2012/12/12/exciting-updates-from-accelereyes/
Unfortunately they do not sell Jacket licences anymore.
As far as I understand, the Jacket GPU Array solution based on ArrayFire was much faster than the gpuArray solution provided by MATLAB.
I started working with gpuArray, but I see that many functions are implemented poorly. For example a simple
myArray(:) = 0
is very slow. I have written some custom CUDA-Kernels, but the poorly-implemented standard MATLAB functionality adds a lot of overhead, even if working with gpuArrays consistently throughout the code. I fixed some issues by replacing MATLAB code with hand written CUDA code - but I do not want to reimplement the MATLAB standard functionality.
Another feature I am missing is sparse GPU matrices.
So my questions are:
How do is speed up the badly implemented default GPU implementations provided by MATLAB? In particular, how do I speed up sparse matrix operations in MATLAB using the GPU?
MATLAB does support CUDA based GPU. You have to access it from the "Parallel Computing Toolbox". Hope these 2 links also help:
Parallel Computing Toolbox Features
Key Features
Parallel for-loops (parfor) for running task-parallel algorithms on multiple processors
Support for CUDA-enabled NVIDIA GPUs
Full use of multicore processors on the desktop via workers that run locally
Computer cluster and grid support (with MATLAB Distributed Computing Server)
Interactive and batch execution of parallel applications
Distributed arrays and single program multiple data (spmd) construct for large dataset handling and data-parallel algorithms
MATLAB GPU Computing Support for NVIDIA CUDA-Enabled GPUs
Using MATLAB for GPU computing lets you accelerate your applications with GPUs more easily than by using C or Fortran. With the familiar MATLAB language you an take advantage of the CUDA GPU computing technology without having to learn the intricacies of GPU architectures or low-level GPU computing libraries.
You can use GPUs with MATLAB through Parallel Computing Toolbox, which supports:
CUDA-enabled NVIDIA GPUs with compute capability 2.0 or higher. For releases 14a and earlier, compute capability 1.3 is sufficient.
GPU use directly from MATLAB
GPU-enabled MATLAB functions such as fft, filter, and several linear algebra operations
GPU-enabled functions in toolboxes: Image Processing Toolbox, Communications System Toolbox, Statistics and Machine Learning Toolbox, Neural Network Toolbox, Phased Array Systems Toolbox, and Signal Processing Toolbox (Learn more about GPU support for signal processing algorithms)
CUDA kernel integration in MATLAB applications, using only a single line of MATLAB code
Multiple GPUs on the desktop and computer clusters using MATLAB workers in Parallel Computing Toolbox and MATLAB Distributed Computing Server
I had the pleasure of attending a talk by John, the founder of AccelerEyes. They did not get the speedup because they just removed poorly written code and replaced it with code that saved a few bits here and there. Their speedup was mostly from exploiting the availability of cache and doing a lot of operations in-memory (GPU's). Matlab relied on transferring data between GPU and CPU, if I remember correctly, and hence the speedup was crazy.
I tried with GPUmat, but the neural network toolbox from mathworks does´t support it. Otherwise I must change the nn-toolbox by myself. But it´s too hard for me. Any suggestion for me?
I don't know whether this will accelerate the Neural Network Toolbox in particular, but the Mathworks now offers CUDA GPU support via the Parallel Computing Toolbox:
http://www.mathworks.com/discovery/matlab-gpu.html?s_cid=HP_MI_tech_gpu
Matlab provides its own toolbox for training neural networks on GPU, see here.
As an author, I also advice to use my toolbox ConvNet, that uses kernels of Alex Krizhevsky's library cuda-convnet2. It also has pure CPU and Matlab versions, that work identically. There is also another toolbox for Matlab, called MatConvNet, but I have not checked it.
I work on an audio processing project that needs to do a lot of basic computations (+, -, *) like a FFT (Fast Fourier Transform) calculation.
We're considering using a graphics card to accelerate these computations. But we don't know if this is the best solution. Our desired solution needs to be a good computation system costing less than $500.
We use Matlab programming, and we have a sound card acquisition which have to be plug in the system.
Do you know a solution other than graphics card + motherboard to do lot of calculus?
You can use the free Matlab CUDA library to perform the computations on the GPU. $500 will give you a very decent NVIDIA GPU. Beware that GPU's have limited video memory and will run out of memory with large data volumes even faster than Matlab.
I have benchmarked an 8core intel CPU against an 8800 Nvidia GPU (128streams) with GPUMat , for 512Kb datasets the GPU spun out at the same speed as the 8 core intel at 2Ghz, including transfer times to the GPU memory. For serious GPU work I recommend a dedicated card compared to the one you are using to drive the monitor. Use the motherboard cheapie intel video to drive the monitor and pass the array computes to the Nvidia.
Parallel Computing Toolbox from MathWorks now includes GPU support. In particular, elementwise operations and arithmetic are supported, as well as 1- and 2-dimensional FFTs (along with a whole bunch of other stuff to support hand-written CUDA code if you have that). If you're interested in performing calculations in double-precision, the recent Tesla and Quadro branded cards will give you the best performance.
Here's a trivial example showing how you might use the GPU in MATLAB using Parallel Computing Toolbox:
gA = gpuArray( rand(1000) );
gB = fft( 1 + gA * 3 );