I have to port a pre-existing “host-only” backpropagation implementation to CUDA. I think the nature of the algorithm doesn’t matter here, so I won’t give much explanation about the way it works. What I think matter though, is that it uses 3-dimensional arrays, whose all three dimensions are dynamically allocated.
I use VS2010, with CUDA 5.0. And my device is a 2.1. The original host-only code can be downloaded here
→[url]http://files.getwebb.org/view-cre62u4d.html[/url]
Main points of the code:
- patterns from adult.data are loaded into memory, using the Data structure, present in “pattern.h”.
- several multi-dimensional arrays are allocated
- the algorithm is ran over the patterns, using the arrays allocated just before.
If you want to try to run the code don’t forget to modify the PATH constant at the beginning of kernel.cu. I also advise you to use “2” layers, “5” neurons, and a learning rate of “0.00001”. As you can see, this work perfectly. The “MSE” is improving. For those who have no clue about what does this algorithms, let’s simply say that it learns how to predict a target value, based on 14 variables present in the patterns. The “MSE” decrease, meaning that the algorithm makes less mistakes after each “epoch”.
I spent a really long time trying to run this code on the device. And I’m still unsuccessful. Last attempt was done by simply copying the code initializing the arrays and running the algorithm into a big kernel. Which failed again. This code can be downloaded there
→ [url]http://files.getwebb.org/view-cre62u4c.html[/url]
To be precise, here are the differences with the original host-only code:
- f() and fder(), which are used by the algorithm, become device functions.
- parameters are hardcoded: 2 layers, 5 neurons, and a learning rate of 0.00001
- the “w” array is initialized using a fixed value (0.5), not rand() anymore
- a Data structure is allocated in device’s memory, and the data are sent in device’s memory after they have been loaded from adult.data in host’s memory
I think I did the minimal amount of modifications needed to make the code run in a kernel. The “kernel_check_learningData” kernel, show some informations about the patterns loaded in device’s memory, proving the following code, sending the patterns from the host to the device, did work:
Data data;
Data* dev_data;
int* dev_t;
double* dev_x;
…
input_adult(PathFile, &data);
…
cudaMalloc((void**)&dev_data, sizeof(Data));
cudaMalloc((void**)&dev_t, data.N * sizeof(int));
cudaMalloc((void**)&dev_x, data.N * data.n * sizeof(double));
// Filling the device with t and x’s data.
cudaMemcpy(dev_t, data.t, data.N * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_x, data.x, data.N * data.n * sizeof(double), cudaMemcpyHostToDevice);
// Updating t and x pointers into devices Data structure.
cudaMemcpy(&dev_data->t, &dev_t, sizeof(int*), cudaMemcpyHostToDevice);
cudaMemcpy(&dev_data->x, &dev_x, sizeof(double*), cudaMemcpyHostToDevice);
// Copying N and n.
cudaMemcpy(&dev_data->N, &data.N, sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(&dev_data->n, &data.n, sizeof(int), cudaMemcpyHostToDevice);
It apparently fails at the beginning of the forward phase, when reading the “w” array. I can’t find any explanation for that.
I see two possibilities:
- the code sending the patterns into device’s memory is bugged, despite the fact it seems to work properly, and provoke a bug way further, when beginning the forward phase.
- the CUDA API is not behaving like it should!
I’m desperately searching for my mistake for a very long time. So I wondered if the community could provide me with some help.
Thanks.