multi core full control

1 次查看（过去 30 天）
Anas A.Salam
Anas A.Salam 2013-4-18
hello i want to ask if there is a capability in MATLAB to have full control on CPU cores so if you have any info. about this subject please tell me :) thanks
  4 个评论
显示 2更早的评论
Walter Roberson
Walter Roberson 2013-4-18
You could use a distributed array.
Anas A.Salam
Anas A.Salam 2013-4-18
how to use this tool could you learn me ??

请先登录，再进行评论。

请先登录，再回答此问题。

回答（2 个）

Sean de Wolski
Sean de Wolski 2013-4-18
With the Parallel Computing Toolbox you have direct access to the MPI library low-level functionality. This enables you to use functions such as labSend to explicitly send something to a worker on a CPU core.
What are you trying to do?
  3 个评论
显示 1更早的评论
Sean de Wolski
Sean de Wolski 2013-4-18
I think the Parallel Computing Toolbox with parfor or spmd would do that.
http://www.mathworks.com/help/releases/R2013a/distcomp/spmd.html
Anas A.Salam
Anas A.Salam 2013-4-18
i have to read it and then i will reply to you :) thank you so much

请先登录，再进行评论。

Jason Ross
Jason Ross 2013-4-18
编辑：Jason Ross 2013-4-18
MATLAB does not provide control at the core level. That behavior is passed off to the operating system to do with as it pleases.
What MATLAB does is let you have a collection of worker processes, which can be located on the same machine or on a cluster, and then provides a variety of methods to distribute work to those workers.
It sounds like the approach which might appeal most to you is the job/task workflow, where you can create a job (or series of jobs) that will execute in a certain order with certain sub-tasks that need to be executed in a certain manner. You might find within these tasks that other parallel language contstructs like parfor or spmd provide the types of processing you are looking for.
I'd encourage you to read the documentation and review the examples for the Parallel Computing Toolbox, as you already have an outline of your algortithm and when you look at the various commands available, you will likely determine which ones map most closely to the parts of your algorithm and assemble your program from there.
In terms of monitoring your cores, the various commands will return errors and handle the work distribution, and montior the job status, which are the job of the scheduler you are using. The "local" scheduler uses the cores on your local machine, but it's also possible to use compute clusters in the same manner, with little or no modifications to the code that works on "local".
What this means is that it's possible that you don't (and shouldn't) worry about how the frames are split up for processing. You could, for example, do that processing using parfor, and the work will be distributed to the workers by the scheduler. This will mean that your code will be identical on a 4 core machine or a 128 node cluster since you don't really care about the specifics of processing and hand that off to the scheduler to take care of, then move onto the next step in processing.
http://www.mathworks.com/help/distcomp/

类别

在 Help Center 和 File Exchange 中查找有关 Job and Task Creation 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by