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I read that one can use kernel launches to synchronize different blocks i.e., If i want all blocks to complete operation 1 before they go on to operation 2, I should place operation 1 in one kernel and operation 2 in another kernel. This way, I can achieve global synchronization between blocks. However, the cuda c programming guide mentions that kernel calls are asynchronous ie. the CPU does not wait for the first kernel call to finish and thus, the CPU can also call the second kernel before the 1st has finished. However, if this is true, then we cannot use kernel launches to synchronize blocks. Please let me know where i am going wrong
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CUDA GPUs run kernels using blocks of threads that are a multiple of 32 in size, so 256 threads is a reasonable size to choose. add<<<1, 256>>>(N, x, y); If I run the code with only this change, it will do the computation once per thread, rather than spreading the computation across the parallel threads.
Basically, a child CUDA kernel can be called from within a parent CUDA kernel and then optionally synchronize on the completion of that child CUDA Kernel. The parent CUDA kernel can consume the output produced from the child CUDA kernel, all without CPU involvement [136].
kernel cannot allocate, and only isbits types in device arrays: CUDA C has no garbage collection, and Julia has no manual deallocations, let alone on the device to deal with data that live independently of the CuArray. no try-catch-finally in kernel: CUDA C does not support exception handling on device (v11.
Figure 1 shows that the CUDA kernel is a function that gets executed on GPU. The parallel portion of your applications is executed K times in parallel by K different CUDA threads, as opposed to only one time like regular C/C++ functions. Figure 1. The kernel is a function executed on the GPU.
Kernel calls are asynchronous from the point of view of the CPU so if you call 2 kernels in succession the second one will be called without waiting for the first one to finish. It only means that the control returns to the CPU immediately.
On the GPU side, if you haven't specified different streams to execute the kernel they will be executed by the order they were called (if you don't specify a stream they both go to the default stream and are executed serially). Only after the first kernel is finished the second one will execute.
This behavior is valid for devices with compute capability 2.x which support concurrent kernel execution. On the other devices even though kernel calls are still asynchronous the kernel execution is always sequential.
Check the CUDA C programming guide on section 3.2.5 which every CUDA programmer should read.
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