Parallel Computing Toolbox Functions

Alphabetical List By Category

A

`addAttachedFiles`	Attach files or folders to parallel pool
`afterEach`	Define a function to call when new data is received
`arrayfun`	Apply function to each element of array on GPU

B

`batch`	Run MATLAB script or function on worker
`bsxfun`	Binary singleton expansion function for gpuArray

C

`cancel`	Cancel job or task
`cancel (FevalFuture)`	Cancel queued or running future
`changePassword`	Prompt user to change MJS password
`classUnderlying`	Class of elements within gpuArray or distributed array
`clear`	Remove objects from MATLAB workspace
`codistributed`	Create codistributed array from replicated local data
`codistributed`	Access elements of arrays distributed among workers in parallel pool
`codistributed.build`	Create codistributed array from distributed data
`codistributed.cell`	Create codistributed cell array
`codistributed.colon`	Distributed colon operation
`codistributed.spalloc`	Allocate space for sparse codistributed matrix
`codistributed.speye`	Create codistributed sparse identity matrix
`codistributed.sprand`	Create codistributed sparse array of uniformly distributed pseudo-random values
`codistributed.sprandn`	Create codistributed sparse array of uniformly distributed pseudo-random values
`codistributor`	Create codistributor object for codistributed arrays
`codistributor1d`	1-D distribution scheme for codistributed array
`codistributor2dbc`	2-D block-cyclic distribution scheme for codistributed array
`codistributor2dbc.defaultLabGrid`	Default computational grid for 2-D block-cyclic distributed arrays
`Composite`	Create Composite object
`Composite`	Access nondistributed variables on multiple workers from client
`createCommunicatingJob`	Create communicating job on cluster
`createJob`	Create independent job on cluster
`createTask`	Create new task in job
`CUDAKernel`	Kernel executable on GPU

D

`datastore`	Create datastore for large collections of data
`delete`	Remove job or task object from cluster and memory
`delete (Pool)`	Shut down parallel pool
`demote`	Demote job in cluster queue
`diary`	Display or save Command Window text of batch job
`distributed`	Access elements of distributed arrays from client
`distributed`	Create distributed array from data in client workspace
`distributed.cell`	Create distributed cell array
`distributed.spalloc`	Allocate space for sparse distributed matrix
`distributed.speye`	Create distributed sparse identity matrix
`distributed.sprand`	Create distributed sparse array of uniformly distributed pseudo-random values
`distributed.sprandn`	Create distributed sparse array of normally distributed pseudo-random values
`dload`	Load distributed arrays and Composite objects from disk
`dsave`	Save workspace distributed arrays and Composite objects to disk

E

`exist`	Check whether Composite is defined on workers
`existsOnGPU`	Determine if gpuArray or CUDAKernel is available on GPU
`eye`	Identity matrix

F

`false`	Array of logical 0 (false)
`fetchNext`	Retrieve next available unread FevalFuture outputs
`fetchOutputs`	Retrieve output arguments from all tasks in job
`fetchOutputs (FevalFuture)`	Retrieve all output arguments from Future
`feval`	Evaluate kernel on GPU
`findJob`	Find job objects stored in cluster
`findTask`	Task objects belonging to job object
`for`	for-loop over distributed range

G

`gather`	Transfer distributed array or gpuArray to local workspace
`gcat`	Global concatenation
`gcp`	Get current parallel pool
`getAttachedFilesFolder`	Folder into which AttachedFiles are written
`getCodistributor`	Codistributor object for existing codistributed array
`getCurrentCluster`	Cluster object that submitted current task
`getCurrentJob`	Job object whose task is currently being evaluated
`getCurrentTask`	Task object currently being evaluated in this worker session
`getCurrentWorker`	Worker object currently running this session
`getDebugLog`	Read output messages from job run in CJS cluster
`getJobClusterData`	Get specific user data for job on generic cluster
`getJobFolder`	Folder on client where jobs are stored
`getJobFolderOnCluster`	Folder on cluster where jobs are stored
`getLocalPart`	Local portion of codistributed array
`getLogLocation`	Log location for job or task
`globalIndices`	Global indices for local part of codistributed array
`gop`	Global operation across all workers
`gplus`	Global addition
`gpuArray`	Create array on GPU
`gpuArray`	Array stored on GPU
`gpuDevice`	Query or select GPU device
`GPUDevice`	Graphics processing unit (GPU)
`gpuDeviceCount`	Number of GPU devices present
`GPUDeviceManager`	Manager for GPU Devices
`gputimeit`	Time required to run function on GPU

H

help Help for toolbox functions in Command Window

I

`Inf`	Array of infinity
`isaUnderlying`	True if distributed array's underlying elements are of specified class
`iscodistributed`	True for codistributed array
`isComplete`	True if codistributor object is complete
`isdistributed`	True for distributed array
`isequal`	True if clusters have same property values
`isequal (FevalFuture)`	True if futures have same ID
`isreplicated`	True for replicated array

J

jobStartup File for user-defined options to run when job starts

L

`labBarrier`	Block execution until all workers reach this call
`labBroadcast`	Send data to all workers or receive data sent to all workers
`labindex`	Index of this worker
`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
`length`	Length of object array
`listAutoAttachedFiles`	List of files automatically attached to job, task, or parallel pool
`load`	Load workspace variables from batch job
`logout`	Log out of MJS cluster

M

`mapreduce`	Programming technique for analyzing data sets that do not fit in memory
`mapreducer`	Define parallel execution environment for mapreduce and tall arrays
`methods`	List functions of object class
`mexcuda`	Compile MEX-function for GPU computation
`mpiLibConf`	Location of MPI implementation
`mpiprofile`	Profile parallel communication and execution times
`mpiSettings`	Configure options for MPI communication
`mxGPUArray`	Type for MATLAB gpuArray
`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
`mxInitGPU`	Initialize MATLAB GPU library on currently selected device
`mxIsGPUArray`	Determine if mxArray contains GPU data

N

`NaN`	Array of Not-a-Numbers
`numlabs`	Total number of workers operating in parallel on current job
`numpartitions`	Number of datastore partitions

O

ones Array of ones

P

`pagefun`	Apply function to each page of array on GPU
`parallel.Cluster`	Access cluster properties and behaviors
`parallel.cluster.Hadoop`	Create Hadoop cluster object
`parallel.cluster.Hadoop`	Hadoop cluster for mapreducer, mapreduce and tall arrays
`parallel.clusterProfiles`	Names of all available cluster profiles
`parallel.defaultClusterProfile`	Examine or set default cluster profile
`parallel.exportProfile`	Export one or more profiles to file
`parallel.Future`	Request function execution on parallel pool workers
`parallel.gpu.CUDAKernel`	Create GPU CUDA kernel object from PTX and CU code
`parallel.importProfile`	Import cluster profiles from file
`parallel.Job`	Access job properties and behaviors
`parallel.Pool`	Access parallel pool
`parallel.pool.Constant`	Build parallel.pool.Constant from data or function handle
`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
`parallel.Task`	Access task properties and behaviors
`parallel.Worker`	Access worker that ran task
`parcluster`	Create cluster object
`parfeval`	Execute function asynchronously on parallel pool worker
`parfevalOnAll`	Execute function asynchronously on all workers in parallel pool
`parfor`	Execute for-loop iterations in parallel on workers in parallel pool
`parpool`	Create parallel pool on cluster
`partition`	Partition a datastore
`pause`	Pause MATLAB job scheduler queue
`pctconfig`	Configure settings for Parallel Computing Toolbox client session
`pctRunDeployedCleanup`	Clean up after deployed parallel applications
`pctRunOnAll`	Run command on client and all workers in parallel pool
`pload`	Load file into parallel session
`pmode`	Interactive Parallel Command Window
`poll`	Retrieve data sent from a worker
`poolStartup`	File for user-defined options to run on each worker when parallel pool starts
`promote`	Promote job in MJS cluster queue
`psave`	Save data from communicating job session

R

`rand`	Array of rand values
`randi`	Array of random integers
`randn`	Array of randn values
`recreate`	Create new job from existing job
`redistribute`	Redistribute codistributed array with another distribution scheme
`reset`	Reset GPU device and clear its memory
`resume`	Resume processing queue in MATLAB job scheduler

S

`saveAsProfile`	Save cluster properties to specified profile
`saveProfile`	Save modified cluster properties to its current profile
`send`	Send data from worker to client using a data queue
`setConstantMemory`	Set some constant memory on GPU
`setJobClusterData`	Set specific user data for job on generic cluster
`shutdown`	Shut down cloud cluster
`size`	Size of object array
`sparse`	Create sparse distributed or codistributed matrix
`spmd`	Execute code in parallel on workers of parallel pool
`start`	Start cloud cluster
`submit`	Queue job in scheduler
`subsasgn`	Subscripted assignment for Composite
`subsref`	Subscripted reference for Composite

T

`tall`	Create tall array
`taskFinish`	User-defined options to run on worker when task finishes
`taskStartup`	User-defined options to run on worker when task starts
`ticBytes`	Start counting bytes transferred within parallel pool
`tocBytes`	Read how many bytes have been transferred since calling ticBytes
`true`	Array of logical 1 (true)

U

updateAttachedFiles Update attached files or folders on parallel pool

W

`wait`	Wait for job to change state
`wait (cluster)`	Wait for cloud cluster to change state
`wait (FevalFuture)`	Wait for futures to complete
`wait (GPUDevice)`	Wait for GPU calculation to complete
`write`	Write distributed data to an output location

Z

zeros Array of zeros

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