加快统计计算速度
Statistics and Machine Learning Toolbox™ 允许您使用并行计算来加快某些统计计算的速度。在并行计算中,一个 MATLAB® 客户端会话将代码段分配给多个工作进程进行独立处理,然后将各个结果合并起来完成计算。使用并行计算,可以加快重抽样方法(例如自助法和刀切法、决策树提升法和装袋法、交叉验证法、聚类算法等)的速度。有关 Statistics and Machine Learning Toolbox 中支持并行计算的完整函数列表,请参阅函数列表(自动并行支持)。
有些函数接受 gpuArray
(Parallel Computing Toolbox) 输入参量,因此您可以通过在图形处理单元 (GPU) 上运行来加快代码执行速度。有关接受 GPU 数组的 Statistics and Machine Learning Toolbox 函数的完整列表,请参阅函数列表(GPU 数组)。
您必须拥有 Parallel Computing Toolbox™ 许可证才能使用并行计算功能和 GPU 数组。
主题
- Quick Start Parallel Computing for Statistics and Machine Learning Toolbox
Get started with parallel statistical computing.
- Concepts of Parallel Computing in Statistics and Machine Learning Toolbox
Overview of the ideas in parallel statistical computations.
- When to Run Statistical Functions in Parallel
Deciding when to call functions in parallel.
- Working with parfor
Parallel computing using
parfor
with statistics functions. - Implement Jackknife Using Parallel Computing
Speed up the jackknife using parallel computing.
- Implement Cross-Validation Using Parallel Computing
Speed up cross-validation using parallel computing.
- Implement Bootstrap Using Parallel Computing
Speed up the bootstrap using parallel computing.
- Reproducibility in Parallel Statistical Computations
How to obtain identical results from repeated parallel computations.
- Analyze and Model Data on GPU
Accelerate your code by using GPU array input arguments.
- Accelerate Linear Model Fitting on GPU
This example shows how you can accelerate regression model fitting by running functions on a graphical processing unit (GPU).