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- Is cudaMemcpyAsync inside a kernel controlled by the GPU?
Hey experts! I have this code snippet which copies data from the CPU to the GPU from within a kernel __global__ void kernel( int* host_data, int* device_data, size_t size ) { cudaMemcpyAsync( host_data, device_data, size * sizeof( int ), cudaMemcpyDefault ); cudaDeviceSynchronize(); } I was wondering whether the GPU instantiates the transfer? I e The GPU tells its DMA engines to transfer the
- Synchronization of cudaMemcpyAsync for pageable memory
Recently I came across the following document for cuda 11 4 regarding synchronization of cudaMemcpy* calls CUDA Runtime API :: CUDA Toolkit Documentation which mentions However, from my experience cudaMemcpyAsync for host-to-device transfer of pageable memory always blocks on the stream until the transfer is finished A simple test program with cuda 11 4 does not show asynchronous copy
- Small Memory Transfers with CudaMemcpyAsync - CUDA Programming and . . .
That is why, as a best practice, cudaMemcpyAsync should always use programmer-allocated pinned memory allocations The reason fixing processor and memory affinity with numactl can improve performance is because the number of hops in CPU-CPU interconnect (either between CPU sockets or between core complexes inside a single CPU) traversed per
- multi-gpu and cudamemcpyasync - NVIDIA Developer Forums
Greetings, I am using 2 GPUs with pthreads I am trying to use CudaMemcpyAsync from host to device for both of the GPUs (different CPU data) via cudaStreams but this doesn’t seem to work The code works fine when I replace CudaMemcpyAsync with CudaMemcpy Can we use cudamemcpyasync with multi-GPUs? If so, what might be causing my problem? If not, why can’t we use asynchronous copies with
- cudaMemcpyAsync makes code faster even when using the default stream 0 . . .
cudaMemcpyAsync can be asynchronous, as the name suggests It can return before the transfer is finished This allows better overlap between gpu work and cpu work (cuda api overhead) In contrast, cudaMemcpy will block the current cpu thread until the transfer is complete This is not directly related to cuda stream semantics You should be able to verify the different behaviours in a profile
- CudaMemcpyAsync wait long time to launch - CUDA Programming and . . .
My kernel and Cuda API seems wait a long time to launch, And I don’t know why And It seems happen when my GPU heavily used, maybe around 80% utilization rate
- cudaMemcpyAsync - CUDA Programming and Performance - NVIDIA Developer . . .
Hello If I have a for loop invoking cudaMemcpyAsync where I always use the zero stream (the default stream), can I expect the data to be copied to the destination in parallel and asynchronously, and therefore see a speedup in my program? Or do I need to associate a distinct stream with each value of i to see a speedup? For example: for(int i=0;i<100;i++){ cudaMemcpyAsync(dest[i],src[i],size
- cudaMemcpyAsync, unexpected behaviour while using cudaStreamNonBlocking . . .
cudaMemcpyAsync usually enforces safety by requiring pinned memory or it will perform as a synchronous call instead, however when using a stream set with the cudaStreamNonBlocking flag, it does not make this enforcement and allow asynchronous calls with non-pinned host memory
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