Arreglos distribuidos
Parallel Computing Toolbox™ admite arreglos distribuidos para particionar arreglos grandes entre varios workers de MATLAB®. Usted opera en todo el arreglo como una sola entidad; sin embargo, los workers operan solo en su parte del arreglo y transfieren datos automáticamente entre ellos cuando es necesario. La ejecución simultánea está respaldada por la construcción del lenguaje de datos múltiples de un solo programa (spmd) para facilitar la comunicación entre los workers. Utilice operaciones y funciones que admiten arreglos distribuidos para trabajar directamente con estos arreglos sin modificaciones adicionales. Puede utilizar arreglos distribuidos en Parallel Computing Toolbox para ejecutar aplicaciones de big data utilizando la memoria combinada de su clúster.
Funciones
Clases
Ejemplos y procedimientos
- Create and Use Distributed Arrays
When your data array is too big to fit into the memory of a single machine, you can create a
distributedarray. - Run MATLAB Functions with Distributed Arrays
MATLAB functions that operate on distributed arrays.
- Distributing Arrays to Parallel Workers
Use
datastoreordistributedto create distributed arrays and partition the data among your workers. - Run Single Programs on Multiple Data Sets
Use
spmdstatements to run the same code on multiple datasets and control codistributed arrays. - Access Worker Variables with Composites
Composite objects in the MATLAB client session let you directly access data values on the workers.
- Train Network in Parallel with Custom Training Loop (Deep Learning Toolbox)
This example shows how to set up a custom training loop to train a network in parallel.
- Use spmdReduce to Achieve MPI_Allreduce Functionality
In this example, we look at the
spmdReducefunction and the functions that build on it:spmdPlusandspmdCat. - Numerical Estimation of Pi Using Message Passing
This example shows the basics of working with spmd statements, and how they provide an interactive means of performing parallel computations.
- Choose Between spmd, parfor, and parfeval
Compare and contrast
spmdagainst other parallel computing functionality such asparforandparfeval.
Conceptos
- Nondistributed Versus Distributed Arrays
Describes the various types of arrays used in communicating jobs.
- Working with Codistributed Arrays
Describes how to use codistributed arrays for calculation.
- Looping over a Distributed Range (for-drange)
Describes how to program a
for-loop with codistributed arrays. - Trabajar con datos remotos
Trabaje con datos remotos en Amazon S3™, Azure® Blob Storage o HDFS™.
Ejemplos destacados
Use Distributed Arrays to Solve Systems of Linear Equations with Direct Methods
Solve a system of linear equations of the form in parallel with a direct method using distributed arrays.
Use Distributed Arrays to Solve Systems of Linear Equations with Iterative Methods
For large-scale mathematical computations, iterative methods can be more efficient than direct methods. This example shows how you can solve systems of linear equations of the form in parallel using distributed arrays with iterative methods.
Solve Differential Equation Using Multigrid Preconditioner on Distributed Discretization
Solve Poisson's equation using a preconditioned iterative solver and distributed arrays. By using distributed arrays, you can scale up the calculation using the memory of a cluster of machines, not just the memory of a single machine. You can scale up without changing your code.
