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Choose a Parallel Computing Solution

Process your data faster or scale up your big data computation using the capabilities of MATLAB^®, Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™.

Problem	Solutions	Required Products	More Information
Do you want to process your data faster?	Profile your code.	MATLAB	Profile to Improve Performance (MATLAB)
	Vectorize your code.	MATLAB	Vectorization (MATLAB)
	Use built-in parallel computing support in MathWorks products.	MATLAB Parallel Computing Toolbox	Built-in Parallel Computing Support
	If you have a GPU, try `gpuArray`.	MATLAB Parallel Computing Toolbox	Identify and Select a GPU Device
	Use `parfor`.	MATLAB Parallel Computing Toolbox	Interactively Run a Loop in Parallel Using parfor
Are you looking for other ways to speed up your processing?	Try `parfeval`.	MATLAB Parallel Computing Toolbox	Evaluate Functions in the Background Using parfeval
Are you looking for other ways to speed up your processing?	Try `spmd`.	MATLAB Parallel Computing Toolbox	Run Single Programs on Multiple Data Sets
Do you want to scale up your big data calculation?	To work with out-of-memory data with any number of rows, use tall arrays. This workflow is well suited to data analytics and machine learning.	MATLAB	Big Data Workflow Using Tall Arrays and Datastores
	Use tall arrays in parallel on your local machine.	MATLAB Parallel Computing Toolbox	Use Tall Arrays on a Parallel Pool
	Use tall arrays in parallel on your cluster.	MATLAB Parallel Computing Toolbox MATLAB Distributed Computing Server	Use Tall Arrays on a Spark Enabled Hadoop Cluster
	If your data is large in multiple dimensions, use `distributed` instead. This workflow is well suited to linear algebra problems.	MATLAB Parallel Computing Toolbox MATLAB Distributed Computing Server	Distributing Arrays
Do you want to offload to a cluster?	Use `batch` to run your code on clusters and clouds.	MATLAB Distributed Computing Server	Run Batch Parallel Jobs

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