작업 및 태스크 생성

작업 생성 및 작업의 태스크 정의

함수

`parcluster`	cluster 객체 만들기
`batch`	워커에서 MATLAB 스크립트 또는 함수 실행
`createJob`	클러스터에서 독립 작업 만들기
`createCommunicatingJob`	Create communicating job on cluster
`recreate`	Create new job from existing job
`createTask`	Create new task in job
`parallel.defaultProfile`	Examine or set default cluster profile (R2022b 이후)
`parallel.listProfiles`	List available cluster profiles (R2022b 이후)
`parallel.importProfile`	Import cluster profiles from file
`poolStartup`	File for user-defined options to run on each worker when parallel pool starts
`jobStartup`	File for user-defined options to run when job starts
`taskStartup`	User-defined options to run on worker when task starts
`taskFinish`	User-defined options to run on worker when task finishes
`pctconfig`	Configure settings for Parallel Computing Toolbox client session or MATLAB Parallel Server workers
`mpiLibConf`	Location of MPI implementation
`mpiSettings`	Configure options for MPI communication

`parallel.Cluster`	Access cluster properties and behaviors
`Future`	Function scheduled to run
`parallel.Job`	작업 속성 및 동작에 액세스
`parallel.Task`	태스크 속성 및 동작에 액세스

Choose Batch Processing Function
Decide which batch programming construct to use to offload work to a cluster.
Program a Job on a Local Cluster
Manually create and run jobs.
독립 작업 프로그래밍
독립 작업의 태스크는 서로 직접 통신하지 않으며 독립적입니다.
Program Communicating Jobs
Discover the differences between independent and communicating jobs.
Share Code with Workers
Find out how to pass data and code to and from the workers.
Apply Callbacks to MATLAB Job Scheduler Jobs and Tasks
The MATLAB^® Job Scheduler has the ability to trigger callbacks in the client session whenever jobs or tasks in the MATLAB Job Scheduler cluster change to specific states.

How Parallel Computing Software Runs a Job
Explore the life cycle of a job.
Programming Tips
Provides helpful hints for good programming practice.
Job Monitor
Manage your jobs using the Job Monitor.
Control Random Number Streams on Workers
The random number generation functions rand, randi, and randn behave differently for parallel calculations compared to your MATLAB client.