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How to understand “All threads in a warp execute the same instruction at the same time.” in GPU?

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问题 I am reading Professional CUDA C Programming, and in GPU Architecture Overview section: CUDA employs a Single Instruction Multiple Thread (SIMT) architecture to manage and execute threads in groups of 32 called warps. All threads in a warp execute the same instruction at the same time. Each thread has its own instruction address counter and register state, and carries out the current instruction on its own data. Each SM partitions the thread blocks assigned to it into 32-thread warps that it

Concurrency in CUDA multi-GPU executions

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I'm running a cuda kernel function on a multiple GPUs system, with 4 GPUs. I've expected them to be launched concurrently, but they are not. I measure the starting time of each kernel, and the second kernel starts after the first one finishes its execution. So launching the kernel on 4 GPUs is not faster than 1 single GPU. How can I make them work concurrently? This is my code: cudaSetDevice(0); GPU_kernel<<< gridDim, threadsPerBlock >>>(d_result_0, parameterA +(0*rateA), parameterB + (0*rateB)); cudaMemcpyAsync(h_result_0, d_result_0, mem_size_result, cudaMemcpyDeviceToHost); cudaSetDevice(1)

Concurrency in CUDA multi-GPU executions

阅读更多关于 Concurrency in CUDA multi-GPU executions

问题 I'm running a cuda kernel function on a multiple GPUs system, with 4 GPUs. I've expected them to be launched concurrently, but they are not. I measure the starting time of each kernel, and the second kernel starts after the first one finishes its execution. So launching the kernel on 4 GPUs is not faster than 1 single GPU. How can I make them work concurrently? This is my code: cudaSetDevice(0); GPU_kernel<<< gridDim, threadsPerBlock >>>(d_result_0, parameterA +(0*rateA), parameterB + (0