Fundamentos de computación paralela
La computación paralela puede ayudarle a resolver grandes problemas informáticos de diferentes maneras. MATLAB® y Parallel Computing Toolbox™ proporcionan un entorno de programación interactivo para ayudarle a abordar sus tareas informáticas. Si su código se ejecuta demasiado lento, puede perfilarlo, vectorizarlo y utilizar el soporte informático paralelo integrado MATLAB. Luego puede intentar acelerar su código usando parfor en varios MATLAB en un grupo paralelo. Si tiene big data, puede ampliar utilizando arreglos distribuidos o datastore. También puedes ejecutar una tarea sin esperar a que se complete, usando parfeval, para poder continuar con otras tareas. Puede utilizar diferentes tipos de hardware para resolver sus problemas de computación paralela, incluidas computadoras de escritorio, GPU, clústeres y nubes. Para comenzar, consulte Quick Start Parallel Computing in MATLAB.
Funciones
Temas
Lo esencial
- Choose a Parallel Computing Solution
Discover the most important functionalities offered by MATLAB and Parallel Computing Toolbox to solve your parallel computing problem. - Parallel Language Decision Tables
Discover example use cases for common parallel computing language features. - Quick Start Parallel Computing in MATLAB
Learn about parallel computing in MATLAB and Parallel Computing Toolbox. - Run MATLAB Functions with Automatic Parallel Support
Take advantage of parallel computing resources without requiring any extra coding. - Interactively Run Loops in Parallel Using parfor
Convert afor-loop into a scalableparfor-loop. - Choose How to Manage Data in Parallel Computing
Determine the data management approach that meets your parallel computing requirements. - Plot During Parameter Sweep with parfor
Perform a parameter sweep in parallel and plot progress during parallel computations. - Scale Up from Desktop to Cluster
Develop your parallel MATLAB® code on your local machine and scale up to a cluster. - Run Batch Parallel Jobs
Use batch to offload work from your MATLAB session to run in the background. - Process Big Data in the Cloud
This example shows how to access a large data set in the cloud and process it in a cloud cluster using MATLAB® capabilities for big data. - Evaluate Functions in the Background Using parfeval
Break out of an optimizing loop early and collect results as they become available. - Run MATLAB Functions on a GPU
Supply agpuArrayargument to automatically run functions on a GPU. - Train Network in the Cloud Using Automatic Parallel Support (Deep Learning Toolbox)
This example shows how to train a convolutional neural network using MATLAB® automatic support for parallel training.
Aprende más
- What Is Parallel Computing?
Learn about MATLAB and Parallel Computing Toolbox. - Run Code on Parallel Pools
Learn about starting and stopping parallel pools, pool size, and parallel environment selection. - Choose Between Thread-Based and Process-Based Environments
With Parallel Computing Toolbox, you can run your parallel code in different parallel environments, such as thread-based or process-based environments. - Ejecutar funciones de MATLAB en entornos basados en subprocesos
Compruebe la compatibilidad de las funciones de MATLAB que desea ejecutar en segundo plano. - Set Environment Variables on Workers
Copy system environment variables from the client to workers in a cluster. - Write Portable Parallel Code
Write parallel code that can use parallel resources if you have Parallel Computing Toolbox and that still runs if you do not have Parallel Computing Toolbox.
Ejemplos destacados
Illustrating Three Approaches to GPU Computing: The Mandelbrot Set
Adapt your MATLAB® code to compute the Mandelbrot Set using a GPU.
Use parfor to Speed Up Monte-Carlo Code
Speed up Monte-Carlo code by using parfor-loops. Monte-Carlo methods are found in many fields, including physics, mathematics, biology, and finance. Monte-Carlo methods involve executing a function many times with randomly distributed inputs. With Parallel Computing Toolbox™, you can replace a for-loop with a parfor-loop to easily speed up code.
