Parallel Computing Toolbox
Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB®applications without CUDA or MPI programming. You can use the toolbox with Simulink®to run multiple simulations of a model in parallel.
The toolbox lets you use the full processing power of multicore desktops by executing applications on workers (MATLAB computational engines) that run locally. Without changing the code, you can run the same applications on a computer cluster or a grid computing service (usingMATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.
Getting Started
Learn the basics of Parallel Computing Toolbox
Parallel Computing Fundamentals
Choose a parallel computing solution
Parallel for-Loops (parfor)
Use parallel processing by runningparfor
on workers in a parallel pool
Asynchronous Parallel Programming
Evaluate functions in the background usingparfeval
Big Data Processing
Analyze big data sets in parallel using distributed arrays, tall arrays, datastores, ormapreduce
, on Spark®and Hadoop®clusters
Batch Processing
Offload execution of functions to run in the background
GPU Computing
加速哟ur code by running it on a GPU
Clusters and Clouds
Discover cluster resources, and work with cluster profiles.
Performance Profiling
提高并行代码的性能