Parallel Computing Fundamentals
Parallel computing can help you to solve big computing problems in different ways. MATLAB®and Parallel Computing Toolbox™ provide an interactive programming environment to help tackle your computing tasks. If your code runs too slowly, you can profile it, vectorize it, and use built-in MATLAB parallel computing support. Then you can try to accelerate your code by usingparfor
on multiple MATLAB workers in a parallel pool. If you have big data, you can scale up using distributed arrays or数据存储
. You can also execute a task without waiting for it to complete, usingparfeval
, so that you can carry on with other tasks. You can use different types of hardware to solve your parallel computing problems, including desktop computers, GPUs, clusters, and clouds.
Functions
Key functions in Parallel Computing Toolbox
parfor |
Execute for-loop iterations in parallel on workers in parallel pool |
parfeval |
Execute function asynchronously on parallel pool worker |
gpuArray |
Create array on GPU |
distributed |
Access elements of distributed arrays from client |
batch |
Run MATLAB script or function on worker |
parpool |
Create parallel pool on cluster |
ticBytes |
Start counting bytes transferred within parallel pool |
tocBytes |
Read how many bytes have been transferred since calling ticBytes |
Examples and How To
Choose a Parallel Computing Solution
Discover the most important functionalities offered by MATLAB and Parallel Computing Toolbox to solve your parallel computing problem.
Interactively Run a Loop in Parallel Using parfor
Convert a slowfor
-loop into a fasterparfor
-loop.
Usebatch
to offload work from your MATLAB session to run in the background.
Evaluate Functions in the Background Using parfeval
Break out of a loop early and collect results as they become available.
Identify and Select a GPU Device
UsegpuDevice
to identify and select which device you want to use.
When your data array is too big to fit into the memory of a single machine, you can create adistributed
array
Concepts
Learn about MATLAB and Parallel Computing Toolbox
Learn about starting and stopping parallel pools, pool size, and cluster selection.
Scale Up parfor-Loops to Cluster and Cloud
Developparfor
-loops on your desktop, and scale up to a cluster without changing your code.
Discover key parallel computing concepts