Unified Memory For CUDA Newcomers
", launched the fundamentals of CUDA programming by displaying how to write down a easy program that allotted two arrays of numbers in memory accessible to the GPU and then added them together on the GPU. To do that, I introduced you to Unified Memory, which makes it very simple to allocate and entry information that may be used by code running on any processor in the system, CPU or GPU. I completed that put up with a number of easy "exercises", one of which encouraged you to run on a recent Pascal-based GPU to see what happens. I hoped that readers would try it and comment on the outcomes, and a few of you probably did! I prompt this for 2 reasons. First, because Pascal GPUs such as the NVIDIA Titan X and the NVIDIA Tesla P100 are the primary GPUs to incorporate the Page Migration Engine, which is hardware support for Unified Memory web page faulting and migration.
The second cause is that it supplies an amazing alternative to study more about Unified Memory. Quick GPU, Quick Reminiscence… Proper! However let’s see. First, I’ll reprint the outcomes of working on two NVIDIA Kepler GPUs (one in my laptop and one in a server). Now let’s try operating on a really quick Tesla P100 accelerator, based mostly on the Pascal GP100 GPU. Hmmmm, that’s below 6 GB/s: slower than working on my laptop’s Kepler-based mostly GeForce GPU. Don’t be discouraged, though; we are able to fix this. To grasp how, I’ll should tell you a bit more about Unified Memory. What's Unified Memory? Unified Memory is a single memory handle space accessible from any processor in a system (see Figure 1). This hardware/software program expertise permits purposes to allocate information that can be learn or written from code operating on both CPUs or GPUs. Allocating Unified Memory is as simple as replacing calls to malloc() or new with calls to cudaMallocManaged(), an allocation perform that returns a pointer accessible from any processor (ptr in the following).
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When code working on a CPU or GPU accesses information allotted this manner (usually called CUDA managed knowledge), the CUDA system software program and/or the hardware takes care of migrating memory pages to the memory of the accessing processor. The important level right here is that the Pascal GPU structure is the primary with hardware assist for digital memory page faulting and migration, through its Web page Migration Engine. Older GPUs primarily based on the Kepler and Maxwell architectures additionally help a extra restricted form of Unified Memory. What Occurs on Kepler Once i call cudaMallocManaged()? On methods with pre-Pascal GPUs just like the Tesla K80, calling cudaMallocManaged() allocates size bytes of managed memory on the GPU gadget that's active when the call is made1. Internally, the driver additionally units up web page table entries for all pages covered by the allocation, in order that the system is aware of that the pages are resident on that GPU. So, in our instance, running on a Tesla K80 GPU (Kepler architecture), x and y are each initially absolutely resident in GPU memory.
Then within the loop starting on line 6, the CPU steps via both arrays, initializing their parts to 1.0f and 2.0f, respectively. For the reason that pages are initially resident in system memory, a page fault occurs on the CPU for every array web page to which it writes, and the GPU driver migrates the page from system Memory Wave Audio to CPU memory. After the loop, all pages of the two arrays are resident in CPU memory. After initializing the data on the CPU, the program launches the add() kernel to add the weather of x to the weather of y. On pre-Pascal GPUs, upon launching a kernel, the CUDA runtime should migrate all pages previously migrated to host memory or to another GPU again to the machine memory of the device working the kernel2. Since these older GPUs can’t web page fault, all data must be resident on the GPU simply in case the kernel accesses it (even when it won’t).
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