Unified Memory For CUDA Learners: Difference between revisions

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Created page with "<br>", launched the fundamentals of CUDA programming by showing how to jot down a easy program that allotted two arrays of numbers in memory accessible to the GPU after which added them together on the GPU. To do this, I launched you to Unified Memory, which makes it very simple to allocate and access knowledge that can be used by code working on any processor in the system, CPU or GPU. I completed that publish with a number of easy "exercises", one in all which encourag..."
 
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Latest revision as of 18:39, 13 October 2025


", launched the fundamentals of CUDA programming by showing how to jot down a easy program that allotted two arrays of numbers in memory accessible to the GPU after which added them together on the GPU. To do this, I launched you to Unified Memory, which makes it very simple to allocate and access knowledge that can be used by code working on any processor in the system, CPU or GPU. I completed that publish with a number of easy "exercises", one in all which encouraged you to run on a latest Pascal-based mostly GPU to see what happens. I was hoping that readers would try it and touch upon the outcomes, and some of you probably did! I instructed this for 2 causes. First, as a result of Pascal GPUs such as the NVIDIA Titan X and the NVIDIA Tesla P100 are the first GPUs to incorporate the Web page Migration Engine, which is hardware support for Unified Memory web page faulting and migration.



The second purpose is that it offers an amazing alternative to study more about Unified Memory. Fast GPU, Quick Memory… Right! However let’s see. First, I’ll reprint the outcomes of working on two NVIDIA Kepler GPUs (one in my laptop computer and one in a server). Now let’s strive operating on a very fast Tesla P100 accelerator, based on the Pascal GP100 GPU. Hmmmm, that’s underneath 6 GB/s: slower than operating on my laptop’s Kepler-based mostly GeForce GPU. Don’t be discouraged, although; we will repair this. To understand how, I’ll must inform you a bit more about Unified Memory. What is Unified Memory Wave? Unified Memory is a single Memory Wave deal with space accessible from any processor in a system (see Determine 1). This hardware/software technology permits purposes to allocate information that can be learn or written from code working on either CPUs or GPUs. Allocating Unified Memory is so simple as changing calls to malloc() or new with calls to cudaMallocManaged(), an allocation function that returns a pointer accessible from any processor (ptr in the following).



When code operating on a CPU or GPU accesses knowledge allotted this fashion (often referred to as 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 vital level here is that the Pascal GPU architecture is the primary with hardware assist for virtual memory page faulting and migration, by way of its Page Migration Engine. Older GPUs based on the Kepler and Maxwell architectures also assist a more restricted type of Unified Memory. What Happens on Kepler After i name cudaMallocManaged()? On programs with pre-Pascal GPUs like the Tesla K80, calling cudaMallocManaged() allocates size bytes of managed memory on the GPU machine that's energetic when the call is made1. Internally, the driver additionally sets up web page table entries for all pages coated by the allocation, so that the system is aware of that the pages are resident on that GPU. So, in our instance, operating on a Tesla K80 GPU (Kepler architecture), x and y are each initially totally resident in GPU memory.



Then within the loop beginning on line 6, the CPU steps by means of both arrays, initializing their components to 1.0f and 2.0f, respectively. Since the pages are initially resident in system memory, a page fault happens on the CPU for each array page to which it writes, and the GPU driver migrates the page from device memory to CPU memory. After the loop, all pages of the two arrays are resident in CPU memory. After initializing the information on the CPU, the program launches the add() kernel to add the weather of x to the elements of y. On pre-Pascal GPUs, upon launching a kernel, the CUDA runtime must migrate all pages previously migrated to host memory or to a different GPU back to the device Memory Wave System of the gadget working the kernel2. Since these older GPUs can’t page fault, all data should be resident on the GPU simply in case the kernel accesses it (even if it won’t).