Hello,
My CUDA code runs 10 times slower when I use shared memory. However, I am not sure as to why. I have it setup so that each thread in the block just reads or writes to an exclusive memory in the array (in shared memory). Maybe I am not using it as it is meant to be. I am new to CUDA and this is my first kernel writing attempt.
So, in my CUDA kernel I have something like this:
[codebox]
#define NUM_BLOCKS 320
global void MyKernel()
{
shared int compareCode [NUM_BLOCKS];
compareCode [threadIdx.x] = someValue;
// Now later in the code, I have something like this:
if (compareCode[threadIdx.x] > currentCode]
{
compareCode[threadIdx.x] = currentCode;
}
}
[/codebox]
This is basically the essense of the code. As you can see, my kernel allocates the array in shared memory and each thread should be writing and reading from its its own exclusive location (determined by threadIdx.x). This, runs very very slow. When I have something like:
[codebox]
global void MyKernel()
{
int compareCode = someValue;
if (compareCode > currentCode]
{
compareCode = currentCode;
}
}
[/codebox]
This runs 10 times faster! Am I supposed to allocate shared memory some other way or are there some conflicts going on??! I fail to understand how though. Each thread should make a request for an exclusive index and no other thread should be asking for that index. Completely has me stumped.
Would really appreciate any help because right now I am using a lot of registers and this is preventing me from using the power of the GPU.
Many thanks,
xarg
If any NVIDIA developers read this, the visual profiler is so awesome! Thanks for that wonderful tool.