Parallel Computing Toolbox Functions
Alphabetical List
By Category
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 |
取消(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 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
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
Queue Management and Job Information
pause |
Pause MATLAB job scheduler queue |
resume |
Resume processing queue in MATLAB job scheduler |
取消 |
Cancel job or task |
delete |
Remove job or task object from cluster and memory |
promote |
在乔丹集群队列促进工作 |
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 |
从工作运行在集群cj读取输出消息 |
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) |
等待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 |
start |
Start cloud cluster |
shutdown |
Shut down cloud cluster |
wait (cluster) |
Wait for cloud cluster to change state |
parallel.defaultClusterProfile |
Examine or set default cluster profile |
parallel.exportProfile |
出口一个或更多的资料es 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 |
Was this topic helpful?