![]() Note, however, that the profiler shows the kernel run time separate from the migration time, since the migrations happen before the kernel runs. That’s what happens in my program when I run it on K80 or my Macbook Pro. This means there is potentially migration overhead on each kernel launch. Since these older GPUs can’t page fault, all data must be resident on the GPU just in case the kernel accesses it (even if it won’t). On pre-Pascal GPUs, upon launching a kernel, the CUDA runtime must migrate all pages previously migrated to host memory or to another GPU back to the device memory of the device running the kernel 2. Check for errors (all values should be 3.0f) Wait for GPU to finish before accessing on host Int index = blockIdx.x * blockDim.x + threadIdx.x CUDA kernel to add elements of two arrays To understand how, I’ll have to tell you a bit more about Unified Memory.įor reference in what follows, here’s the complete code to add_grid.cu from last time. Don’t be discouraged, though we can fix this. Hmmmm, that’s under 6 GB/s: slower than running on my laptop’s Kepler-based GeForce GPU. Now let’s try running on a really fast Tesla P100 accelerator, based on the Pascal GP100 GPU. First, I’ll reprint the results of running on two NVIDIA Kepler GPUs (one in my laptop and one in a server). The second reason is that it provides a great opportunity to learn more about Unified Memory. First, because Pascal GPUs such as the NVIDIA Titan X and the NVIDIA Tesla P100 are the first GPUs to include the Page Migration Engine, which is hardware support for Unified Memory page faulting and migration. (I was hoping that readers would try it and comment on the results, and some of you did!). ![]() I finished that post with a few simple “exercises”, one of which encouraged you to run on a recent Pascal-based GPU to see what happens. Unified Memory is a single memory address space accessible from any processor in a system. To do this, I introduced you to Unified Memory, which makes it very easy to allocate and access data that can be used by code running on any processor in the system, CPU or GPU. ![]() My previous introductory post, “ An Even Easier Introduction to CUDA C++“, introduced the basics of CUDA programming by showing how to write a simple program that allocated two arrays of numbers in memory accessible to the GPU and then added them together on the GPU.
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