Programación paralela asincrónica
parfevalPuede evaluar una función en segundo plano sin esperar a que se complete, usando parfeval. En muchos casos, puede resultar conveniente salir temprano de un bucle for. Por ejemplo, en un procedimiento de optimización, puede detener el ciclo antes de tiempo cuando el resultado sea lo suficientemente bueno. Puede hacer esto en uno o todos los workers del grupo paralelo, usando parfeval o parfevalOnAll. Esto puede resultar útil si desea poder representar resultados intermedios. Tenga en cuenta que esto es diferente a usar parfor, donde debe esperar a que se complete el ciclo.
Utilice send y poll juntos para enviar y sondear mensajes o datos de diferentes workers utilizando una cola de datos. Puede usar afterEach para agregar una función para llamar cuando se reciben nuevos datos de una cola de datos.
Utilice afterEach y afterAll para invocar automáticamente funciones después de que se complete cada uno o después de que se completen todos los elementos de un arreglo Future. Esta arreglo puede contener futuros devueltos por parfeval, parfevalOnAll, afterEach o afterAll.
Funciones
Temas
- Evaluate Functions in the Background Using parfeval
Break out of an optimizing loop early and collect results as they become available.
- Query and Cancel parfeval Futures
This example shows how to query the state of
parfevalfutures and cancel them. - Use afterEach and afterAll to Run Callback Functions
Automatically run functions after
Futureobjects finish running on parallel pools. - Plot During Parameter Sweep with parfeval
This example shows how to perform a parallel parameter sweep with
parfevaland send results back during computations with aDataQueueobject. - Update User Interface Asynchronously Using afterEach and afterAll
This example shows how to update a user interface as computations complete.
- Perform Webcam Image Acquisition in Parallel with Postprocessing
This example shows how to perform frame acquisition from a webcam in parallel with data postprocessing.
- Perform Image Acquisition and Parallel Image Processing
This example shows how to perform image acquisition from a webcam and postprocess data in parallel.
- Train Deep Learning Networks in Parallel (Deep Learning Toolbox)
This example shows how to run multiple deep learning experiments on your local machine.
- Use parfeval to Train Multiple Deep Learning Networks (Deep Learning Toolbox)
This example shows how to use
parfevalto perform a parameter sweep on the depth of the network architecture for a deep learning network and retrieve data during training. - Choose Between spmd, parfor, and parfeval
Compare and contrast
spmdagainst other parallel computing functionality such asparforandparfeval. - Receive Communication on Workers
This example shows how to set up a data queue on the workers to receive data. (Desde R2023b)
- Interactively Import and Process Data in Parallel
This example shows how to import and process data simultaneously in an interactive parallel pool. (Desde R2023b)
Ejemplos destacados
Plot During Parameter Sweep with parfeval
Perform a parallel parameter sweep with parfeval and send results back during computations with a DataQueue object.
Train Deep Learning Networks in Parallel
Run multiple deep learning experiments on your local machine. Using this example as a template, you can modify the network layers and training options to suit your specific application needs. You can use this approach with a single or multiple GPUs. If you have a single GPU, the networks train one after the other in the background. The approach in this example enables you to continue using MATLAB® while deep learning experiments are in progress.
