quick start parallel computing for statistics and machine learning toolbox -凯发k8网页登录

quick start parallel computing for statistics and machine learning toolbox

note

to use parallel computing, you must have a parallel computing toolbox™ license.

parallel statistics and machine learning toolbox functionality

you can use any of the statistics and machine learning toolbox™ functions with parallel computing toolbox constructs such as parfor (parallel computing toolbox) and (parallel computing toolbox). however, some functions, such as those with interactive displays, can lose functionality in parallel. in particular, displays and interactive usage are not effective on workers (see vocabulary for parallel computation).

additionally, some statistics and machine learning toolbox functions are enhanced to use parallel computing internally. for example, some model fitting functions perform hyperparameter optimization in parallel. for a complete list of statistics and machine learning toolbox functions that support parallel computing, see function list (automatic parallel support). for the usage notes and limitations of each function, see the automatic parallel support section on the function reference page.

how to compute in parallel

this section gives the simplest way to use the enhanced functions in parallel. for more advanced topics, including the issues of reproducibility and nested parfor loops, see the other topics in speed up statistical computations.

for information on parallel statistical computing at the command line, enter

help parallelstats

to have a function compute in parallel:

set up a parallel environment

to run a statistical computation in parallel, first set up a parallel environment.

note

setting up a parallel environment can take several seconds.

for a multicore machine, enter the following at the matlab^® command line:

parpool(n)

n is the number of workers you want to use.

you can also run parallel code in matlab online™. for details, see (parallel computing toolbox).

set the `useparallel` option to `true`

create an options structure with the function. to run in parallel, set the useparallel option to true:

paroptions = statset('useparallel',true);

call the function using the options structure

call your function with syntax that uses the options structure. for example:

% run crossval in parallel
cvmse = crossval('mse',x,y,'predfun',regf,'options',paroptions);
% run bootstrp in parallel
sts = bootstrp(100,@(x)[mean(x) std(x)],y,'options',paroptions);
% run treebagger in parallel
b = treebagger(50,meas,spec,'oobpred','on','options',paroptions);

for more complete examples of parallel statistical functions, see , implement jackknife using parallel computing, implement cross-validation using parallel computing, and implement bootstrap using parallel computing.

after you have finished computing in parallel, close the parallel environment:

delete mypool

tip

to save time, keep the pool open if you expect to compute in parallel again soon.

quick start parallel computing for statistics and machine learning toolbox -凯发k8网页登录