1
edit
Changes
→Assignment 3
Now I realized I did not use shared memory for assignment 2. So I changed that. In my assignment 3 I am using shared memory. As for assignment 2 I am doing 1000 times image negate. Last lectures for this course helped a lot to learn to optimize. Specially coalesced access. It is amazing! In assignment 2 my negate function calculations became faster almost twice just because I was using the kernel. I was thinking there is nothing to be done more. And now my negate function calculations became faster more than twice than in assignment 2 thanks to the optimization. This is my profile for assignment 3:
[[Image:Profiler_scr.png]]
The code for Kernel is here:
<pre>
__global__ void cudaNegateImage2D_Coalescence(int *result, const int *work, int ni, int nj)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
__shared__ float cache_a[NTPB][NTPB];
__shared__ float cache_b[NTPB][NTPB];
cache_a[threadIdx.y][threadIdx.x] = work[j * ni + i];
//cache_b[threadIdx.x][threadIdx.y] = b[j * NTPB + j];
__syncthreads();
if (i < ni && j < nj) {
//result[i * nj + j] = -(work[i * nj + j]) + 255;
cache_b[threadIdx.y][threadIdx.x] = -cache_a[threadIdx.y][threadIdx.x] + 255;
}
__syncthreads();
result[j * ni + i] = cache_b[threadIdx.y][threadIdx.x];
}
</pre>
