Reputation: 1975
Here is the kernel that I am launching for calculating some array in parallel.
__device__ bool mult(int colsize,int rowsize,int *Aj,int *Bi)
{
for(int j = 0; j < rowsize;j++)
{
for(int k = 0;k < colsize;k++)
{
if(Aj[j] == Bi[k])
{
return true;
}
}
}
return false;
}
__global__ void kernel(int *Aptr,int *Aj,int *Bptr,int *Bi,int rows,int cols,int *Cjc)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int i;
if(tid < cols)
{
int beg = Bptr[tid];
int end = Bptr[tid+1];
for(i = 0;i < rows;i++)
{
int cbeg = Aptr[i];
int cend = Aptr[i+1];
if(mult(end - beg,cend - cbeg,Aj+cbeg,Bi+beg))
{
Cjc[tid+1] += 1;
//atomicAdd(Cjc+tid+1,1);
}
}
}
}
My launch configurations and kernel call are as follows.
int numBlocks,numThreads;
if(q % 32 == 0)
{
numBlocks = q/32;
numThreads = 32;
}
else
{
numBlocks = (q+31)/32;
numThreads = 32;
}
findkernel<<<numBlocks,numThreads>>>(devAptr,devAcol,devBjc,devBir,m,q,d_Cjc);
I have to admit, this kernel is running pretty slow.Once I get the array back to host side, I use thrust::inclusive_scan
to find my resultant array.
My question is, is there any room for improvement / optimization for my kernel? I tried using shared memory but its producing either wrong answers or throwing runtime exceptions.
Also, how does the dynamically allocated shared memory ( which is allocated by third parameter in kernel launch ) is distributed among the blocks?
Any help/hints/insinuations will be appreciated. Thanks in advance.
Upvotes: 0
Views: 159
Reputation: 6753
As for the shared memory allocated using kernel<<<blocks,threads,mem>>>
mem is the amount of memory allocated each block. So each block gets mem
amount of memory.
For your code, I don't understand why are there 2 for loops in the mult function. Just want to point out that each thread will be executing these 2 for loops. Moreover, as you also have a for loop in the kernel
function, it means that each thread will be executing the 2 for
loops in the mult function several times. THis is slow. Moreover, doing
int beg = Bptr[tid];
int end = Bptr[tid+1];
is not exactly coalesced access. Non coalesced access is slow.
Upvotes: 1