Implementing Neural Network using CUDA - cuda

Question

I am trying to create a Neural Network using CUDA:
My kernel looks like :
__global__ void feedForward(float *input, float *output, float **weight) {
//Here the threadId uniquely identifies weight in a neuron
int weightIndex = threadIdx.x;
//Here the blockId uniquely identifies a neuron
int neuronIndex = blockIdx.x;
if(neuronIndex<NO_OF_NEURONS && weightIndex<NO_OF_WEIGHTS)
output[neuronIndex] += weight[neuronIndex][weightIndex]
* input[weightIndex];
}
While copying the output back to host, I'm getting an error
Error unspecified launch failure at line xx
At line xx :
CUDA_CHECK_RETURN(cudaMemcpy(h_output, d_Output, output_size, cudaMemcpyDeviceToHost));
Am I doing something wrong here?
Is it because of how I'm using both the block index as well as thread index to reference the weight matrix.
Or does the problem lie elsewhere ?
I'm allcoating the weight matrix as follows:
cudaMallocPitch((void**)&d_Weight, &pitch_W,input_size,NO_OF_NEURONS);
My kernel call is:
feedForward<<<NO_OF_NEURONS,NO_OF_WEIGHTS>>>(d_Input,d_Output,d_Weight);
After that i call:
cudaThreadSynchronize();
I am new to programming with CUDA.
Any help would be appreciated.
Thanks

Answer 1

There is a problem in output code. Though it won't produce the error described, it will produce incorrect results.
int neuronIndex = blockIdx.x;
if(neuronIndex<NO_OF_NEURONS && weightIndex<NO_OF_WEIGHTS)
output[neuronIndex] += weight[neuronIndex][weightIndex] * input[weightIndex];
We can see that all threads in single block are writing concurrently into one memory cell. So udefined results are expected. To avoid this I suggest reduce all values within a block in shared memory and perform a single write to global memory. Something like this:
__global__ void feedForward(float *input, float *output, float **weight) {
int weightIndex = threadIdx.x;
int neuronIndex = blockIdx.x;
__shared__ float out_reduce[NO_OF_WEIGHTS];
out_reduce[weightIndex] =
(weightIndex<NO_OF_WEIGHTS && neuronIndex<NO_OF_NEURONS) ?
weight[neuronIndex][weightIndex] * input[weightIndex]
: 0.0;
__syncthreads();
for (int s = NO_OF_WEIGHTS; s > 0 ; s >>= 1)
{
if (weightIndex < s) out_reduce[weightIndex] += out_reduce[weightIndex + s];
__syncthreads();
}
if (weightIndex == 0) output[neuronIndex] += out_reduce[weightIndex];
}
It turned out that I had to rewrite half of you small kernel to help with reduction code...

Answer 2

I build a very simple MLP network using CUDA. You can find my code over here if it may interest you: https://github.com/PirosB3/CudaNeuralNetworks/
For any questions, just shoot!
Daniel

Answer 3

You're using cudaMallocPitch, but don't show how the variables are initialized; I'd be willing to bet this is where your error stems from. cudaMallocPitch is rather tricky; the 3rd parameter should be in bytes, while the 4th parameter is not. i.e.
int width = 64, height = 64;
float* devPtr;
size_t pitch;
cudaMallocPitch(&device_Ptr, &pitch, width * sizeof(float), height);
Is your variable input_size in bytes? If not, then you might be allocating too little memory (i.e. you'll think you're requesting 64 elements, but instead you'll be getting 64 bytes), and as such you'll be accessing memory out of range in your kernel. In my experience, an "unspecified launch failure" error usually means I have a segfault