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Cuda very expensive cudalaunch calls
Cuda very expensive cudalaunch calls






None of my variables have the shared prefix, nor do I use the third kernel launch argument. Therefore, I dug a little deeper and noticed that the error (0x2) might be because of shared memory requirement, but haven’t found any way I can determine how much does my kernel use. I find it hard to believe our kernel managed to gobble up nearly a GB worth of space.

#Cuda very expensive cudalaunch calls code#

The following line fails never reaching the first lines of code inside the kernel:ĬalculationKernell>( arrLen, arr, kernelData, results, log)

  • The point where the code fails is upon entry to the kernel itself, not within it.
  • Outside of processing, the changes affected the pre-allocated memory and added one statically-defined boolean inside the kernel.
  • The system worked prior to a few changes.
  • All memory for the kernel is pre-allocated before the call.
  • The error persists even when the amount of free memory, per cudaMemGetInfo, is up to 900mb.
  • cuda very expensive cudalaunch calls

    I’m not at liberty to post the contents of the program, and it is very long. So, as Fazar pointed out, the answer is yes. S ( cudaStreamt) specifies the associated stream, is an optional parameter which defaults to 0. Warning: Cuda API error detected: cudaLaunch returned (0x2)Īccording to the documentation, Error 0x2 means the API call failed because it was unable to allocate enough memory to perform the requested operation. Ns ( sizet) specifies the number of bytes in shared memory that is dynamically allocated per block for this call in addition to the statically allocated memory. When debugging, I also see the following error: Now, I have no idea how it got name for the kernel but that seems besides the point.

    cuda very expensive cudalaunch calls

    When attempting to run a kernel from host, we get this:ĬUDA error while running kernel: /home/Velo/History/2013/.dat, err: out of memory

    cuda very expensive cudalaunch calls

    I’ve searched for similar problems, but no suggestion seemed to help. I’ve encountered something weird, and am unsure whether it is a bug, misuse of the hardware or just a misunderstanding.






    Cuda very expensive cudalaunch calls