MATLAB PARALLEL COMPUTING TOOLBOX 4 Guide de l'utilisateur télécharger pdf (Page 121)

17-5

Compatibility Considerations

In the previous version of the toolbox, the distributor function was used to define how

an array was distributed. In many cases, you can replace a call to distributor with a

call to darray. For example, if you used distributor without arguments as an input to

an array constructor,

rand(m, n, distributor());

you can update the code to read,

rand(m, n, darray());

rand Seeding Unique for Each Task or Lab

The random generator seed is now initialized based on the task ID for distributed jobs,

or the labindex for parallel jobs (including pmode). This ensures that the set of random

numbers generated for each task or lab within a job is unique, even when you have more

than 82 tasks or labs.

Compatibility Considerations

In the previous version of the distributed computing products, the rand function could by

default generate the same set of numbers for some tasks or labs when these exceeded 82

for a job.

Single-Threaded Computations on Workers

Despite the ability in MATLAB software to perform multithreaded computations on

multiple-CPU machines, the workers and labs running distributed and parallel jobs

perform only single-threaded computations, so that multiprocessor cluster machines can

better accommodate multiple workers or labs.

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MATLAB PARALLEL COMPUTING TOOLBOX 4 Guide de l'utilisateur Page 121