Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

1 results

  • 9 - Scaling Up

  • Richard Ansorge, University of Cambridge
  • Book:
    Programming in Parallel with CUDA
    Published online:
    04 May 2022
    Print publication:
    02 June 2022, pp 293-324

  • Summary

    Chapter 9 discusses how to share a single calculation between multiple GPUs on a workstation. CUDA provides a number of tools to both manage individual devices and for memory management so that multiple devices can see a common shared memory pool. CUDA unified virtual addressing (UVA) is an example of this. Transfers of data between the host and GPU memory can also be automated or eliminated using unified memory (UM) or zero-copy memory. To scale beyond a single workstation the well-known message passing interface (MPI) library is often used and this is described with a simple example.