Parallel Computing Toolbox Functions

Parallel Computing Fundamentals

`parfor`	Execute for-loop iterations in parallel on workers in parallel pool
`parfeval`	Execute function asynchronously on parallel pool worker
`gpuArray`	Create array on GPU
`distributed`	Access elements of distributed arrays from client
`batch`	Run MATLAB script or function on worker
`parpool`	Create parallel pool on cluster
`ticBytes`	Start counting bytes transferred within parallel pool
`tocBytes`	Read how many bytes have been transferred since calling ticBytes

Parallel for-Loops (parfor)

`parfor`	Execute for-loop iterations in parallel on workers in parallel pool
`parpool`	Create parallel pool on cluster
`parfeval`	Execute function asynchronously on parallel pool worker
`ticBytes`	Start counting bytes transferred within parallel pool
`tocBytes`	Read how many bytes have been transferred since calling ticBytes
`send`	Send data from worker to client using a data queue
`afterEach`	Define a function to call when new data is received
`parallel.Pool`	Access parallel pool
`parallel.pool.DataQueue`	Class that enables sending and listening for data between client and workers

Asynchronous Parallel Programming

`parfeval`	Execute function asynchronously on parallel pool worker
`parfevalOnAll`	Execute function asynchronously on all workers in parallel pool
`ticBytes`	Start counting bytes transferred within parallel pool
`tocBytes`	Read how many bytes have been transferred since calling ticBytes
`send`	Send data from worker to client using a data queue
`poll`	Retrieve data sent from a worker
`afterEach`	Define a function to call when new data is received
`fetchOutputs (FevalFuture)`	Retrieve all output arguments from Future
`fetchNext`	Retrieve next available unread FevalFuture outputs
`cancel (FevalFuture)`	Cancel queued or running future
`isequal (FevalFuture)`	True if futures have same ID
`wait (FevalFuture)`	Wait for futures to complete
`parallel.Future`	Request function execution on parallel pool workers
`parallel.Pool`	Access parallel pool
`parallel.pool.DataQueue`	Class that enables sending and listening for data between client and workers
`parallel.pool.PollableDataQueue`	Class that enables sending and polling for data between client and workers

Big Data Processing

Distributed Arrays

`distributed`	Create distributed array from data in client workspace
`gather`	Transfer distributed array or gpuArray to local workspace
`spmd`	Execute code in parallel on workers of parallel pool
`Composite`	Create Composite object
`parallel.pool.Constant`	Build parallel.pool.Constant from data or function handle
`codistributed`	Create codistributed array from replicated local data
`parpool`	Create parallel pool on cluster
`delete (Pool)`	Shut down parallel pool
`redistribute`	Redistribute codistributed array with another distribution scheme
`codistributed.build`	Create codistributed array from distributed data
`for`	for-loop over distributed range
`getLocalPart`	Local portion of codistributed array
`globalIndices`	Global indices for local part of codistributed array
`gop`	Global operation across all workers
`write`	Write distributed data to an output location
`distributed`	Access elements of distributed arrays from client
`codistributed`	Access elements of arrays distributed among workers in parallel pool
`Composite`	Access nondistributed variables on multiple workers from client
`codistributor1d`	1-D distribution scheme for codistributed array
`codistributor2dbc`	2-D block-cyclic distribution scheme for codistributed array
`parallel.Pool`	Access parallel pool

Tall Arrays and Mapreduce

`tall`	Create tall array
`datastore`	Create datastore for large collections of data
`mapreduce`	Programming technique for analyzing data sets that do not fit in memory
`mapreducer`	Define parallel execution environment for mapreduce and tall arrays
`partition`	Partition a datastore
`numpartitions`	Number of datastore partitions
`parpool`	Create parallel pool on cluster
`gcp`	Get current parallel pool
`parallel.Pool`	Access parallel pool
`parallel.cluster.Hadoop`	Hadoop cluster for mapreducer, mapreduce and tall arrays

Batch Processing

Simple Batch Processing

`batch`	Run MATLAB script or function on worker
`diary`	Display or save Command Window text of batch job
`load`	Load workspace variables from batch job
`wait`	Wait for job to change state
`delete`	Remove job or task object from cluster and memory

Detailed Job and Task Control

Job and Task Creation

`parcluster`	Create cluster object
`batch`	Run MATLAB script or function on worker
`createJob`	Create independent job on cluster
`createCommunicatingJob`	Create communicating job on cluster
`recreate`	Create new job from existing job
`createTask`	Create new task in job
`parallel.defaultClusterProfile`	Examine or set default cluster profile
`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
`mpiLibConf`	Location of MPI implementation
`mpiSettings`	Configure options for MPI communication
`parallel.Cluster`	Access cluster properties and behaviors
`parallel.Future`	Request function execution on parallel pool workers
`parallel.Job`	Access job properties and behaviors
`parallel.Task`	Access task properties and behaviors

Job Submission and Results

`batch`	Run MATLAB script or function on worker
`submit`	Queue job in scheduler
`wait`	Wait for job to change state
`fetchOutputs`	Retrieve output arguments from all tasks in job
`diary`	Display or save Command Window text of batch job
`load`	Load workspace variables from batch job

Queue Management and Job Information

`pause`	Pause MATLAB job scheduler queue
`resume`	Resume processing queue in MATLAB job scheduler
`cancel`	Cancel job or task
`delete`	Remove job or task object from cluster and memory
`promote`	Promote job in MJS cluster queue
`demote`	Demote job in cluster queue
`changePassword`	Prompt user to change MJS password
`logout`	Log out of MJS cluster
`findJob`	Find job objects stored in cluster
`findTask`	Task objects belonging to job object
`getDebugLog`	Read output messages from job run in CJS cluster
`getJobClusterData`	Get specific user data for job on generic cluster
`setJobClusterData`	Set specific user data for job on generic cluster

Task Control and Worker Communication

`addAttachedFiles`	Attach files or folders to parallel pool
`labindex`	Index of this worker
`numlabs`	Total number of workers operating in parallel on current job
`gcat`	Global concatenation
`gop`	Global operation across all workers
`gplus`	Global addition
`pload`	Load file into parallel session
`psave`	Save data from communicating job session
`labBarrier`	Block execution until all workers reach this call
`labBroadcast`	Send data to all workers or receive data sent to all workers
`labProbe`	Test to see if messages are ready to be received from other worker
`labReceive`	Receive data from another worker
`labSend`	Send data to another worker
`labSendReceive`	Simultaneously send data to and receive data from another worker
`getCurrentJob`	Job object whose task is currently being evaluated
`getCurrentCluster`	Cluster object that submitted current task
`getCurrentTask`	Task object currently being evaluated in this worker session
`getCurrentWorker`	Worker object currently running this session
`getAttachedFilesFolder`	Folder into which AttachedFiles are written
`updateAttachedFiles`	Update attached files or folders on parallel pool
`parallel.Task`	Access task properties and behaviors
`parallel.Worker`	Access worker that ran task

GPU Computing

GPU Computing in MATLAB

`gpuArray`	Create array on GPU
`gather`	Transfer distributed array or gpuArray to local workspace
`existsOnGPU`	Determine if gpuArray or CUDAKernel is available on GPU
`gpuDevice`	Query or select GPU device
`gpuDeviceCount`	Number of GPU devices present
`gputimeit`	Time required to run function on GPU
`reset`	Reset GPU device and clear its memory
`wait (GPUDevice)`	Wait for GPU calculation to complete
`arrayfun`	Apply function to each element of array on GPU
`bsxfun`	Binary singleton expansion function for gpuArray
`pagefun`	Apply function to each page of array on GPU
`gpuArray`	Array stored on GPU
`GPUDevice`	Graphics processing unit (GPU)
`GPUDeviceManager`	Manager for GPU Devices

GPU CUDA and MEX Programming

`mexcuda`	Compile MEX-function for GPU computation
`parallel.gpu.CUDAKernel`	Create GPU CUDA kernel object from PTX and CU code
`feval`	Evaluate kernel on GPU
`setConstantMemory`	Set some constant memory on GPU
`mxGPUCopyFromMxArray`	Copy mxArray to mxGPUArray
`mxGPUCopyGPUArray`	Duplicate (deep copy) mxGPUArray object
`mxGPUCopyImag`	Copy imaginary part of mxGPUArray
`mxGPUCopyReal`	Copy real part of mxGPUArray
`mxGPUCreateComplexGPUArray`	Create complex GPU array from two real gpuArrays
`mxGPUCreateFromMxArray`	Create read-only mxGPUArray object from input mxArray
`mxGPUCreateGPUArray`	Create mxGPUArray object, allocating memory on GPU
`mxGPUCreateMxArrayOnCPU`	Create mxArray for returning CPU data to MATLAB with data from GPU
`mxGPUCreateMxArrayOnGPU`	Create mxArray for returning GPU data to MATLAB
`mxGPUDestroyGPUArray`	Delete mxGPUArray object
`mxGPUGetClassID`	mxClassID associated with data on GPU
`mxGPUGetComplexity`	Complexity of data on GPU
`mxGPUGetData`	Raw pointer to underlying data
`mxGPUGetDataReadOnly`	Read-only raw pointer to underlying data
`mxGPUGetDimensions`	mxGPUArray dimensions
`mxGPUGetNumberOfDimensions`	Size of dimension array for mxGPUArray
`mxGPUGetNumberOfElements`	Number of elements on GPU for array
`mxGPUIsSame`	Determine if two mxGPUArrays refer to same GPU data
`mxGPUIsSparse`	Determine if mxGPUArray contains sparse GPU data
`mxGPUIsValidGPUData`	Determine if mxArray is pointer to valid GPU data
`mxIsGPUArray`	Determine if mxArray contains GPU data
`mxInitGPU`	Initialize MATLAB GPU library on currently selected device
`mxGPUCopyFromMxArray`	Copy mxArray to mxGPUArray
`mxGPUCopyGPUArray`	Duplicate (deep copy) mxGPUArray object
`mxGPUCopyImag`	Copy imaginary part of mxGPUArray
`mxGPUCopyReal`	Copy real part of mxGPUArray
`mxGPUCreateComplexGPUArray`	Create complex GPU array from two real gpuArrays
`mxGPUCreateFromMxArray`	Create read-only mxGPUArray object from input mxArray
`mxGPUCreateGPUArray`	Create mxGPUArray object, allocating memory on GPU
`mxGPUCreateMxArrayOnCPU`	Create mxArray for returning CPU data to MATLAB with data from GPU
`mxGPUCreateMxArrayOnGPU`	Create mxArray for returning GPU data to MATLAB
`mxGPUDestroyGPUArray`	Delete mxGPUArray object
`mxGPUGetClassID`	mxClassID associated with data on GPU
`mxGPUGetComplexity`	Complexity of data on GPU
`mxGPUGetData`	Raw pointer to underlying data
`mxGPUGetDataReadOnly`	Read-only raw pointer to underlying data
`mxGPUGetDimensions`	mxGPUArray dimensions
`mxGPUGetNumberOfDimensions`	Size of dimension array for mxGPUArray
`mxGPUGetNumberOfElements`	Number of elements on GPU for array
`mxGPUIsSame`	Determine if two mxGPUArrays refer to same GPU data
`mxGPUIsSparse`	Determine if mxGPUArray contains sparse GPU data
`mxGPUIsValidGPUData`	Determine if mxArray is pointer to valid GPU data
`mxIsGPUArray`	Determine if mxArray contains GPU data
`CUDAKernel`	Kernel executable on GPU
`mxGPUArray`	Type for MATLAB gpuArray

Clusters and Clouds

`parcluster`	Create cluster object
`parpool`	Create parallel pool on cluster
`gcp`	Get current parallel pool
`shutdown`	Shut down cloud cluster
`start`	Start cloud cluster
`wait (cluster)`	Wait for cloud cluster to change state
`parallel.defaultClusterProfile`	Examine or set default cluster profile
`parallel.exportProfile`	Export one or more profiles to file
`parallel.importProfile`	Import cluster profiles from file
`saveProfile`	Save modified cluster properties to its current profile
`saveAsProfile`	Save cluster properties to specified profile
`pctconfig`	Configure settings for Parallel Computing Toolbox client session
`parallel.Pool`	Access parallel pool
`parallel.Cluster`	Access cluster properties and behaviors
`pctRunOnAll`	Run command on client and all workers in parallel pool

Performance Profiling

Parallel Profiler and Code Improvement

mpiprofile Profile parallel communication and execution times

Interactive Parallel Development (pmode)

pmode Interactive Parallel Command Window

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Documentation

Parallel Computing Toolbox Functions

Parallel Computing Fundamentals

Parallel for-Loops (parfor)

Asynchronous Parallel Programming

Big Data Processing

Distributed Arrays

Tall Arrays and Mapreduce

Batch Processing

Simple Batch Processing

Detailed Job and Task Control

Job and Task Creation

Job Submission and Results

Queue Management and Job Information

Task Control and Worker Communication

GPU Computing

GPU Computing in MATLAB

GPU CUDA and MEX Programming

Clusters and Clouds

Performance Profiling

Parallel Profiler and Code Improvement

Interactive Parallel Development (pmode)

Parallel Computing Toolbox Documentation

Other Documentation

Support