## Parallel Computing Toolbox |

Do you… | You can… | See |
---|---|---|

Utilize a multicore machine? | Use Parallel Computing Toolbox | Parallel Computing Toolbox |

Utilize a cluster? | Use MATLAB Distributed Computing Server with Parallel Computing Toolbox | MATLAB Distributed Computing Server |

Run Monte Carlo simulations? Test your designs against variations in several parameters? Have for-loops that take a long time to run? |
Accelerate computations | Accelerating Calculations Through Parallel for-Loops |

Work with data sets that are too big to process on a single computer? | Spread data sets across multiple computers and process in parallel | Handling Large Data Sets with Distributed Distributed Arrays and Parallel MATLAB Functions spmd (single program multiple data) statements |

Find it cumbersome to debug parallel applications? Need to access intermediate results when you run your parallel code? |
Interactively develop your parallel applications | Interactive Environment matlabpool command Parallel Command Window |

Want to see how Parallel Computing Toolbox works? | Download or request a trial | Trial Software |

The Parallel Computing Toolbox product offers several features that simplify the development of parallel applications in MATLAB. It offers programming constructs such as parallel loops and distributed arrays that let you extend your serial programming into a parallel domain. You can use these constructs without the requirement of learning a complex parallel language or making significant changes to your existing code. The toolbox supports interactive development, which lets you connect to your cluster from a MATLAB session to interactively perform parallel computations.

Integration of the toolbox with Optimization Toolbox, Global Optimization Toolbox, and SystemTest lets you use parallel computing capabilities directly without writing any parallel code. In addition, MATLAB Compiler and related builder products let you create executables and shared libraries from your parallel MATLAB applications. These executables and shared libraries can connect with MATLAB Distributed computing Server for high performance computing, and they can be shared royalty-free.

Learn more about features for parallel programming in MATLAB:

- Parallel for-loops (parfor) – Perform parallel calculations by processing a for-loop simultaneously across multiple workers.
- Distributed arrays and associated functions – Allocate matrices of any data type across workers and perform parallel computations on these data structures
- Interactive environment – Interactively develop algorithms in the familiar MATLAB environment using a pool of MATLAB workers (matlabpool) or the Parallel Command Window
- Message-passing functions – Use low-level message-passing functions for complex parallel algorithms