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Unable to access CUDA memory after device swap
This is an interesting problem and I'm still scratching my head over it. I have two systems, both of them have a multi-core CPU, each CPU has 8 cores, each core has eight threads and each core has its own CUDA enabled GPU. I have a host code which is part of a larger application, it has to do a kernel launch on each of the 16 GPUs and for each of the kernels it does half of the work. On each of the cores that are acting as a host, it allocates a resource of about 2GB which is used as a buffer for the kernels. There are 2GB per GPU for each of the kernels so that they don't have to be serialized and there are 16 of these.
Each kernel launch then launches a number of threads which is determined by the number of total threads that will be allocated across all the devices, assuming that the number of work items per thread is 1 (the same as the number of cores).
The problem is that in the first instance, the CUDA memory accesses work fine, the buffers are used and the kernels run without issues. However, after I swap one of the GPU devices, the kernels are still launched in parallel, but they receive memory pointer values that are garbage.
Here is a picture of how it looks before the device swap:
Here is a picture after the device swap:
To make sure that the memory pointer that are being sent to the kernel is correct, I had the host code calculate the same number it was sending to the kernel and it printed out the correct number. Furthermore, I gave the kernel access to the whole of the buffer directly, instead of using shared memory as shown in the host code below, and it still gave the correct number.
(deviceCount is the number of GPU devices and sharedMemorySizeBytes is the size of the memory buffer allocated per thread)
This means that, I have narrowed the problem down to something in the kernel itself, but not sure what it could be. The kernel was written by me using OpenMPI and it can be run successfully when I don't swap a GPU device.