gpu-shared-memory

Today I added four more __local variables to my kernel to dump intermediate results in. But just adding the four more variables to the kernel's signature and adding the corresponding Kernel arguments renders all output of the kernel to "0"s. None of the cl functions returns an error code. I further tried only to add one of the two smaller variables. If I add only one of them, it works, but if I add both of them, it breaks down. So could this behavior of OpenCL mean, that I allocated to much __local memory? How do I find out, how much __local memory is usable by me? Kyle Lutz The amount of

The size of the shared memory ("local memory" in OpenCL terms) is only 16 KiB on most nVIDIA GPUs of today. I have an application in which I need to create an array that has 10,000 integers. so the amount of memory I will need to fit 10,000 integers = 10,000 * 4b = 40kb. How can I work around this? Is there any GPU that has more than 16 KiB of shared memory ? Think of shared memory as explicitly managed cache. You will need to store your array in global memory and cache parts of it in shared memory as needed, either by making multiple passes or some other scheme which minimises the number of

i am trying to allocate shared memory by using a constant parameter but getting an error. my kernel looks like this: __global__ void Kernel(const int count) { __shared__ int a[count]; } and i am getting an error saying error: expression must have a constant value count is const! Why am I getting this error? And how can I get around this? const doesn't mean "constant", it means "read-only". A constant expression is something whose value is known to the compiler at compile-time. CUDA supports dynamic shared memory allocation. If you define the kernel like this: __global__ void Kernel(const int