1 / 8

Parallel Computing in Matlab

An Introduction. Parallel Computing in Matlab. Overview. Offload work from client to workers Run as many as eight workers (newest version) Can keep client session free for interactive work. Parallel for-loops (parfor). No iterations may depend on other iterations

welcome
Télécharger la présentation

Parallel Computing in Matlab

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Introduction Parallel Computing in Matlab

  2. Overview • Offload work from client to workers • Run as many as eight workers (newest version) • Can keep client session free for interactive work

  3. Parallel for-loops (parfor) • No iterations may depend on other iterations • No global variables may be changed in the parfor loop • There is some overhead

  4. Using parfor • Use “matlabpool open local 2” to open two workers (duo core) • Use parfor like a for loop • When finished, use matlabpool close • See example: Parforloop.m • See example: test.m

  5. Batch Job • Offload work to another session • Continue using the client interactively • Requires a few more commands than parfor

  6. Batch Job • job=batch('script_name') • wait(job) • load(job,'variable_name') • destroy(job) • See example three

  7. Batch Parallel Loop • Offload work • Run in parallel

  8. Batch Parallel Loop • job=batch('script_name','matlabpool',1) • Here we have one worker in addition to the one running the batch script for a total of two • wait(job) • load(job,'variable_name') • destroy(job)

More Related