Beyond GPU Memory Limits with Unified Memory On Pascal > 자유게시판

Trendy laptop architectures have a hierarchy of reminiscences of varying measurement and efficiency. GPU architectures are approaching a terabyte per second memory bandwidth that, coupled with excessive-throughput computational cores, creates an excellent system for Memory Wave data-intensive tasks. Nevertheless, everyone is aware of that quick memory is costly. Modern applications striving to solve bigger and larger problems may be restricted by GPU memory capacity. Because the capacity of GPU memory is significantly decrease than system memory, it creates a barrier for developers accustomed to programming just one memory space. With the legacy GPU programming mannequin there is no such thing as a straightforward option to "just run" your application when you’re oversubscribing GPU memory. Even if your dataset is just barely bigger than the out there capability, you'd still must handle the energetic working set in GPU memory. Unified Memory is a much more intelligent memory management system that simplifies GPU growth by offering a single Memory Wave Workshop house straight accessible by all GPUs and CPUs in the system, with automatic page migration for information locality.

Migration of pages allows the accessing processor to profit from L2 caching and the lower latency of local memory. Moreover, migrating pages to GPU memory ensures GPU kernels reap the benefits of the very excessive bandwidth of GPU memory (e.g. 720 GB/s on a Tesla P100). And web page migration is all utterly invisible to the developer: the system automatically manages all information motion for Memory Wave Workshop you. Sounds nice, right? With the Pascal GPU structure Unified Memory is much more powerful, Memory Wave Workshop due to Pascal’s bigger digital memory address area and Page Migration Engine, enabling true virtual memory demand Memory Wave paging. It’s additionally worth noting that manually managing memory movement is error-prone, which affects productiveness and delays the day when you'll be able to lastly run your entire code on the GPU to see these nice speedups that others are bragging about. Builders can spend hours debugging their codes due to memory coherency points. Unified memory brings big advantages for developer productiveness. On this submit I'll present you how Pascal can allow purposes to run out-of-the-box with bigger memory footprints and achieve great baseline performance.

For a moment you possibly can completely forget about GPU memory limitations while developing your code. Unified Memory was introduced in 2014 with CUDA 6 and the Kepler architecture. This relatively new programming mannequin allowed GPU purposes to make use of a single pointer in each CPU functions and GPU kernels, which enormously simplified memory management. CUDA 8 and Memory Wave Workshop the Pascal architecture considerably improves Unified Memory functionality by including 49-bit digital addressing and Memory Wave Workshop on-demand page migration. The large 49-bit digital addresses are adequate to allow GPUs to access the complete system memory plus the memory of all GPUs within the system. The Web page Migration engine allows GPU threads to fault on non-resident memory accesses so the system can migrate pages from wherever in the system to the GPUs memory on-demand for environment friendly processing.