I want to test whether cudaMalloc and cudaFree are synchronous calls, so I did some modification to the "simpleMultiGPU.cu" sample code in CUDA SDK. Following is the part I changed (the added lines are not indented):
float *dd[GPU_N];;
for (i = 0; i < GPU_N; i++){cudaSetDevice(i); cudaMalloc((void**)&dd[i], sizeof(float));}
//Start timing and compute on GPU(s)
printf("Computing with %d GPUs...\n", GPU_N);
StartTimer();
//Copy data to GPU, launch the kernel and copy data back. All asynchronously
for (i = 0; i < GPU_N; i++)
{
//Set device
checkCudaErrors(cudaSetDevice(i));
//Copy input data from CPU
checkCudaErrors(cudaMemcpyAsync(plan[i].d_Data, plan[i].h_Data, plan[i].dataN * sizeof(float), cudaMemcpyHostToDevice, plan[i].stream));
//Perform GPU computations
reduceKernel<<<BLOCK_N, THREAD_N, 0, plan[i].stream>>>(plan[i].d_Sum, plan[i].d_Data, plan[i].dataN);
getLastCudaError("reduceKernel() execution failed.\n");
//Read back GPU results
checkCudaErrors(cudaMemcpyAsync(plan[i].h_Sum_from_device, plan[i].d_Sum, ACCUM_N *sizeof(float), cudaMemcpyDeviceToHost, plan[i].stream));
cudaMalloc((void**)&dd[i],sizeof(float));
cudaFree(dd[i]);
//cudaStreamSynchronize(plan[i].stream);
}
By commenting out the cudaMalloc line and cudaFree line respectively in the large loop, I found that for a 2-GPU system, the GPU processing time are 30 milliseconds and 20 milliseconds respectively, so I concluded that cudaMalloc is an asynchronous call and cudaFree is a synchronous call. Not sure this is true or not, and not sure what is the designing concern of the CUDA architecture. My computation capability is 2.0, and I tried both cuda4.0 and cuda5.0.
Both functions are synchronous.
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