MPI Support for Algorithms

Concept

Introduction

MPI support in Mantid is based on processing a subset of spectra on each MPI rank. Many algorithms process each spectrum independently so this is an efficient way of splitting the computational effort and data volume.

Note that the multi-dimensional workspaces MDHistoWorkspace and MDEventWorkspace handle data differently and thus cannot be dealt with in this way. MPI support for these multi-dimensional workspaces is beyond the scope of this document.

Spectrum and Detector

In Mantid the terms spectrum and detector are used interchangeably at times, leading to confusion. In particular, it is sometimes assumed that there is a 1:1 correspondence between spectra and detectors. It is important to clearly distinguish between a spectrum and a detector, especially in the context of MPI support. We may define the terms as follows:

  • A detector is a single pixel of the instrument. Data obtained from it may be used to create a list or histogram of neutron events. If the detector can move, it can contribute to more than one list or histogram of neutron events.
  • A spectrum is a list or histogram of neutron events collected in a specific region of (typically) space. The data contained in the spectrum is data obtained from on one or more detectors.

Examples may help to clarify this:

  • If the detectors are not moveable, a 1:1 mapping is the most common.
  • Some beamlines at ISIS group multiple detectors into a single spectrum, so the region of space corresponding to a spectrum is larger than a single detector pixel.
  • After running algorithms like SumSpectra or DiffractionFocussing, a spectrum will contain data from more than one detector, even if there was an initial 1:1 correspondence.
  • If the detectors are moveable, a spectrum would correspond to a region of space given by the position a specific detector had in a certain time interval.
  • There can be detectors without a corresponding spectrum, and Mantid is thus not handling data of those detectors.

For the purpose of MPI support, Mantid always stores the complete instrument including all detectors on every MPI rank. [1] Only a subset of all spectra is stored on the local MPI rank, so a detector may be locally without corresponding spectrum but have a spectrum associated to it on another MPI rank.

Storage Mode

_images/MPI-storage-modes.png

Visualization of available storage modes. In this and the following figures a vertical column is used to depict an MPI rank.

Not all data in an MPI-based data reduction can be or has to be distributed. Some data may be present on all ranks or only on a single rank. To formalize this concept a Workspace in Mantid now has an associated storage mode (Parallel::StorageMode), as visualized in the figure on the right. The storage mode of a workspace can be obtained by calling Workspace::storageMode(). There are three available storage modes

  • StorageMode::Cloned implies that the data is not split and each rank holds a complete and identical clone.
  • StorageMode::Distributed implies that each rank holds a subset of the data and that the combination of all subsets gives the full data.
  • StorageMode::MasterOnly implies that only the master rank (rank 0) has the data.

Currently only workspaces have an associated storage mode. For all other data, such as a scalar value provided as an input to an algorithm are implicitly assumed to have StorageMode::Cloned, i.e., have the same value on all MPI ranks.

Usage examples for the storage modes could include:

  • A workspace containing the neutron monitors could be used for normalization on all ranks and could use StorageMode::Cloned.
  • A workspace containing the data for all detector pixels would usually use StorageMode::Distributed.
  • A workspace containing the result of summing all spectra, such as obtained from DiffractionFocussing would usually use StorageMode::MasterOnly.

Execution Mode

_images/MPI-execution-mode-identical.png

ExecutionMode::Identical based on an input and output workspace with StorageMode::Cloned. Example: ConvertUnits, Rebin, or many other algorithm that do not load or save data.

_images/MPI-execution-mode-distributed-load.png

ExecutionMode::Distributed creating an output workspace with StorageMode::Distributed. Example: LoadEventNexus.

_images/MPI-execution-mode-distributed.png

ExecutionMode::Distributed based on an input and output workspace with StorageMode::MasterOnly. Example: ConvertUnits or Rebin.

_images/MPI-execution-mode-distributed-gather.png

ExecutionMode::Distributed based on an input workspace with StorageMode::Distributed creating an output workspace with StorageMode::MasterOnly. Example: DiffractionFocussing.

_images/MPI-execution-mode-master-only-load.png

ExecutionMode::MasterOnly creating an output workspace with StorageMode::Distributed. Example: LoadEventNexus or other load algorithms.

_images/MPI-execution-mode-master-only.png

ExecutionMode::MasterOnly based on an input and output workspace with StorageMode::MasterOnly. Example: ConvertUnits, Rebin, or many other algorithm that do not load or save data.

_images/MPI-execution-mode-master-only-store.png

ExecutionMode::MasterOnly based on an input workspace with StorageMode::MasterOnly an no output. Example: Save or any other save algorithm.

Just like the storage mode describes how data is stored, and execution mode describes how an algorithm is executed on this data. There are five execution modes (in namespace Parallel):

  • ExecutionMode::Invalid is used to indicate that execution is not possible, e.g., if the storage modes of the inputs are inconsistent.
  • ExecutionMode::Serial is used for serial execution, i.e., in non-MPI builds of Mantid or if there is only a single MPI rank. Having this mode allows for running algorithms that do not support MPI in MPI builds by running only with a single MPI rank.
  • ExecutionMode::Identical is used for running an algorithm in an identical way on all MPI ranks. This would typically be used if the input workspaces have StorageMode::Cloned.
  • ExecutionMode::Distributed is used for running an algorithm in a distributed way across all MPI ranks. This would typically be used if the input workspaces have StorageMode::Distributed.
  • ExecutionMode::MasterOnly is typically used for running an algorithm if the input workspaces have StorageMode::MasterOnly.

The use of the word ‘typically’ above is intentional and indicates that there may be other cases. In particular, an algorithm may cause a transition from one storage mode to another, or may take inputs with different storage modes. Examples are given in the series of figures on the right.

Building and Running Mantid with MPI Support

Build with MPI support

To build Mantid with MPI support as described in this document run cmake with the additional option -DMPI_EXPERIMENTAL=ON. This requires boost-mpi and a working MPI installation.

Configuration

To avoid unintentional DDOSing or potential other issues, there is no MPI support for DownloadInstrument and CheckMantidVersion. Error messages can be avoided by disabling these startup checks in the configuration. Furthermore, to avoid pollution of usage reports, usage reporting should be disabled:

UpdateInstrumentDefinitions.OnStartup = 0
CheckMantidVersion.OnStartup = 0
usagereports.enabled = 0

Writing and running Python scripts

In principle Python scripts that use only algorithms that support MPI can be run with MPI without changes. For example:

from mantid.simpleapi import *

dataX = [1,2,3,4,2,3,4,5,3,4,5,6,4,5,6,7]
dataY = [1,1,1,1,1,1,1,1,1,1,1,1]
dataE = [1,1,1,1,1,1,1,1,1,1,1,1]

# CreateWorkspace has a new property called ParallelStorageMode that allows setting the
# desired storage mode. It defaults to "Parallel::StorageMode::Cloned".
dataWS = CreateWorkspace(DataX=dataX, DataY=dataY, DataE=dataE, NSpec=4, UnitX="Wavelength", ParallelStorageMode="Parallel::StorageMode::Distributed")
ws = Rebin(dataWS, "1,1,7");

print("Histograms: " + str(ws.getNumberHistograms()))
for i in range(ws.getNumberHistograms()):
    print("(Local) workspace index: " + str(i))
    print(ws.readX(i))
    print(ws.readY(i))

Run Python with mpirun and the desired number of MPI ranks, by using the new -n flag to mantidpython:

mantidpython -n 3 test.py

Possible output:

CreateWorkspace-[Notice] CreateWorkspace started
CreateWorkspace-[Notice] CreateWorkspace successful, Duration 0.02 seconds
Rebin-[Notice] Rebin started
Rebin-[Notice] Rebin successful, Duration 0.01 seconds
Histograms: 2
(Local) workspace index: 0
[ 1.  2.  3.  4.  5.  6.  7.]
[ 1.  1.  1.  0.  0.  0.]
(Local) workspace index: 1
[ 1.  2.  3.  4.  5.  6.  7.]
[ 0.  0.  0.  1.  1.  1.]
Histograms: 1
(Local) workspace index: 0
[ 1.  2.  3.  4.  5.  6.  7.]
[ 0.  1.  1.  1.  0.  0.]
Histograms: 1
(Local) workspace index: 0
[ 1.  2.  3.  4.  5.  6.  7.]
[ 0.  0.  1.  1.  1.  0.]

Output involving the local number of histograms and local indices is obviously not useful for users and should be avoided (see also the section on workspace indices), this example is merely for illustration.

Note that currently Mantid does not support workspaces without spectra, so running above example with more than four MPI ranks fill fail since there are only four spectra. This is probably not a problem in practice.

Logging output

With many MPI ranks it is common to get spammed by logging output. Since there is not control of output order for multi-line log messages it also tends to become hard to read since output from different ranks get interleaved.

The current solution to this is a logging offset for all but the master rank. By default an offset of 1 is added, i.e., an error message from any rank but rank 0 will be displayed as a warning. The offset can be adjusted in the Mantid properties file, e.g.,

mpi.loggingOffset=3

The drawback of this approach is that information contained in error or warning messages that are specific to a spectrum, such as a missing detector ID, can be hidden or lost. If that is an issue the logging offset can simply be set to 0.

Implementing MPI Support for an Algorithm

Supported workspace types

Only MatrixWorkspace and its subclasses support StorageMode::Distributed. All other workspace types, in particular TableWorkspace and MDWorkspace are restricted to StorageMode::MasterOnly and StorageMode::Cloned.

Mechanism

By default an algorithm does not support MPI and any attempt to execute it in an MPI run will throw an exception. MPI support for an algorithm is implemented by means of a couple of virtual methods in the Algorithm base class:

class Algorithm {
  // ...
protected:
  virtual void execDistributed();
  virtual void execMasterOnly();
  virtual Parallel::ExecutionMode getParallelExecutionMode(
      const std::map<std::string, Parallel::StorageMode> &storageModes) const;
  // ...
};

In general it is not necessary to implement all of these methods. For many algorithms it can be sufficient to implement getParallelExecutionMode. This is often the case if an algorithm has only a single input and a single output and treats all spectra independently. In that case the execution mode can simply be determined from the input workspace as follows:

Parallel::ExecutionMode MyAlg::getParallelExecutionMode(
    const std::map<std::string, Parallel::StorageMode> &storageModes) const {
  // The map key is the property name. If there is only one input workspace it can usually be ignored.
  return Parallel::getCorrespondingExecutionMode(storageModes.begin()->second);
}

Here the helper Parallel::getCorrespondingExecutionMode is used to obtain the ‘natural’ execution mode from a storage mode, i.e., ExecutionMode::Identical for StorageMode::Cloned, ExecutionMode::Distributed for StorageMode::Distributed, and ExecutionMode::MasterOnly for StorageMode::MasterOnly. More complex algorithms may require more complex decision mechanism, e.g., when there is more than one input workspace.

For many algorithms a sufficient default implementation of Algorithm::getParallelExecutionMode() is provided by one of the base classes API::SerialAlgorithm, API::ParallelAlgorithm, or API::DistributedAlgorithm. MPI support can simply be enabled by inheriting from one of these instead of from Algorithm. The level of thereby enabled MPI support is as follows:

  • API::SerialAlgorithm supports only ExecutionMode::MasterOnly.
  • API::ParallelAlgorithm supports parallel execution, but not distributed execution, i.e., ExecutionMode::MasterOnly and ExecutionMode::IdenticalOnly.
  • API::DistributedAlgorithm supports distributed execution, i.e., ExecutionMode::MasterOnly, ExecutionMode::IdenticalOnly, and ExecutionMode::Distributed.

In the latter two cases more than one execution mode is supported. Thus this usually works only for algorithms with a single input (and a single output) such that the execution mode can be uniquely derived from the storage mode of the input workpace. Multiple inputs are also supported to a certain extent. For example, for API::DistributedAlgorithm the storage modes of the inputs can be mixed if they are compatible, such as StorageMode::Distributed with StorageMode::Cloned (resulting in ExecutionMode::Distributed).

If none of the other virtual methods listed above is implemented, Algorithm will run the normal exec() method on all MPI ranks. The exception are non-master ranks if the execution mode is ExecutionMode::MasterOnly – in that case creating a dummy workspace is attempted. This is discussed in more detail in the subsections below.

Identical execution

Identical execution with execution mode ExecutionMode::Identical is usually done for data with storage mode StorageMode::Cloned. Execution is handled by simply calling Algorithm::exec() on all MPI ranks.

A notable exception that has to be kept in mind are algorithms that are saving workspaces or write to other resources, since the file names will be in conflict.

Distributed execution

Distributed execution is handled by Algorithm::execDistributed(). By default this simply calls Algorithm::exec(). In many cases this may be perfectly fine and more convenient than reimplementing Algorithm::execDistributed().

The following example illustrates the difference. We can either check for the number of MPI ranks in the normal exec() method:

void MyAlg::exec() {
  //// Algorithm logics, e.g., a sum over all spectra ////
  if (communicator.size() > 1) {
    //// MPI calls, e.g., a global sum ////
  }
}

Alternatively, we can implement Algorithm::execDistributed():

void MyAlg::exec() {
  //// Algorithm logics ////
}

void MyAlg::execDistributed() {
  //// Algorithm logics but in a very different way ////
}

Many algorithms in Mantid will require very little modification for MPI support and thus the first option is likely to be the first choice.

Master-only execution

Master-only execution is handled by Algorithm::execMasterOnly(). By default this simply calls Algorithm::exec() on rank 0 and does nothing on all other ranks.

To support running existing Python scripts without significant modification, and to be able to automatically determine execution modes based on input workspaces, workspaces with storage mode StorageMode::MasterOnly also exist on the non-master ranks.

Given that no algorithm execution happens on non-master ranks, workspaces with StorageMode::MasterOnly do not exist on non-master ranks. This implies: - No methods of such a workspace can be called, except when wrapped in an algorithm that has ExecutionMode::MasterOnly. - Retrieving such a workspace from the AnalysisDataService is not possible. - Algorithms that transform a workspace into a workspace with StorageMode::MasterOnly such as DiffractionFocussing2 should delete the input workspace when using in-place operation.

Setting spectrum numbers

Setting spectrum numbers via the legacy interface MatrixWorkspace::getSpectrum(size_t)::setSpectrumNo(specnum_t) is not supported in MPI runs and will throw an exception. The reason is that spectrum numbers are used to globally identify a spectrum and thus changing a spectrum number must be done globally, i.e., on all MPI ranks. Spectrum numbers should be set by using Indexing::IndexInfo and MatrixWorkspace::setIndexInfo(), or rather by passing the IndexInfo to one of the workspace factory functions from DataObjects/WorkspaceCreation.h.

Workspace indices

If a workspace is distributed, i.e., has storage mode StorageMode::Distributed workspaces indices lose their meaning. In particular, MatrixWorkspace::getNumberHistograms() will return the local number of spectra and not the global size of the workspace. For purposes of interaction with the user interface and for internal consistency a global equivalent of the ‘workspace index’ concept has been introduced. This index is represented by Indexing::GlobalSpectrumIndex. [2]

The consequences are as follows:

  • Workspace indices should not be logged or written into output of other types such as tables. Instead spectrum numbers (Indexing::SpectrumNumber) or global spectrum indices (Indexing::GlobalSpectrumIndex) must be used.
  • The number of histograms in a workspace obtained from MatrixWorkspace::getNumberHistograms() may only be used for processing all spectra, i.e., when each MPI rank is processing all its local spectra. It should not be logged, written as output, or used for branching execution paths since it is meaningless. If the total number of spectra in a workspace is required it can be accessed via MatrixWorkspace::indexInfo()::globalSize().
  • User input providing indices or spectrum numbers in one way or another must be translated into local indices by IndexInfo. The most common cases are a workspace property that also accepts indices, see IndexProperty.
  • The distinction between local and global indices must not be exposed to the user. In particular, the ‘global’ prefix should be omitted, i.e., for the user interface we keep referring to ‘workspace index’, even though it is internally not what used to be the workspace index but rather a global index. Indices provided by a user may never be interpreted as local indices, since a local index has no fixed meaning.

Instrument and detectors

As described above, the full set of detectors is held on each MPI rank. Thus, algorithms that modify detectors must do so in an identical manner on all MPI ranks. That is, if for example detector positions would be modified in an Algorithm it is not sufficient to do so for all detectors that have a corresponding spectrum on the MPI rank. Instead such a modification must be done for all detectors.

The details of this depend on what exactly an algorithm is supposed to do and a generic recipe cannot be given here. It is however essential to think of this when providing MPI support for an algorithm.

GUI

Running the Mantid GUI with MPI support, such as a client GUI with a MPI-based backend, is currently not possible. If it cannot be avoided to add an MPI-related property to an algorithm is shall be made invisible in the GUI. This can be done by adjusting the property settings when implementing Algorithm::init():

#include "MantidKernel/InvisibleProperty.h"

void MyAlg::init() {
  // ...
  setPropertySettings("MyProperty", Kernel::make_unique<InvisibleProperty>());
}

Units Tests

For unit testing the MPI support of an algorithm a fake backend that can be run without MPI is provided. No modifications to the code under test a required. In the unit test case ParallelRunner from MantidTestHelpers is used to run the algorithm (or other code) under test as if it were part of on MPI run. A typical example could look as follows:

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#include "MantidTestHelpers/ParallelAlgorithmCreation.h"
#include "MantidTestHelpers/ParallelRunner.h"

namespace {
void run_algorithm(const Parallel::Communicator &comm,
                   const MyType1 &arbitrary, const MyType2 &arguments) {
  // Creating the algorithm with this helper function is the recommended way,
  // otherwise the communicator has to be set by hand and the name of the
  // output workspace must be set to a different value depending on comm.rank()
  // to avoid clashes, since the threading backend in ParallelRunner shares the
  // ADS for all 'ranks'.
  auto alg = ParallelTestHelpers::create<Mantid::Algorithms::MyAlg>(comm);
  alg->setProperty("InputWorkspace", boost::make_shared<WorkspaceTester>());
  alg->execute();
  Workspace_const_sptr ws = alg->getProperty("OutputWorkspace");
  TS_ASSERT_EQUALS(ws->storageMode(), Parallel::StorageMode::Distributed);
}

class MyAlgTest : public CxxTest::TestSuite {
public:
  // ...

  void test_parallel() {
    // Runs run_algorithm in multiple threads. The first argument passed to
    // run_algorithm is of type Parallel::Communicator and is guaranteed to
    // have size() > 1, i.e., more than one rank, in at least one call to
    // run_algorithm (it is in addition also called with a single 'rank').
    ParallelTestHelpers::runParallel(run_algorithm, 42, 42.0);
  }
};

Here MantidTestHelpers/ParallelAlgorithmCreation.h provides the algorithm factory method ParallelTestHelpers::create<WorkspaceType>. MantidTestHelpers/ParallelRunner.h provides ParallelTestHelpers::runParallel, which uses ParallelRunner with a reasonable default choice for the number of ranks.

Documentation

When adding MPI support for an algorithm, add it to the table at the end of this document. Potential limitations must be described in the comments.

Porting Python reduction scripts in practice

The mechanism of execution modes and storage modes allows for “guided” porting of algorithms as follows:

  1. Run Python script such as a system test with two (or more) MPI ranks.
  2. At some point an algorithm without any MPI support or inadequate MPI support may be encountered, resulting in an error message similar to this:
MyAlg-[Error] Error in execution of algorithm MyAlg:
MyAlg-[Error] Algorithm does not support execution with input workspaces of the following storage types:
MyAlg-[Error] InputWorkspace Parallel::StorageMode::Distributed
MyAlg-[Error] InputWorkspaceMonitor Parallel::StorageMode::Cloned
MyAlg-[Error] .
  1. Add the required MPI support to MyAlg with one of the mechanisms described above. In rare cases the combination of storage modes of the inputs may be unexpected, indicating an error earlier in the chain which needs to be fixed.
  2. Go to 1., until the script finishes successfully.

Supported Algorithms

Algorithm Supported modes Comments
AlignAndFocusPowder all  
AlignAndFocusPowderFromFiles Distributed  
AlignDetectors all with StorageMode::Distributed this touches only detectors that have spectra on this rank, i.e., the modified instrument is not in an identical state on all ranks
BinaryOperation all not supported if AllowDifferentNumberSpectra is enabled
CalculateChiSquared MasterOnly, Identical see IFittingAlgorithm
CalculateCostFunction MasterOnly, Identical see IFittingAlgorithm
CalculateFlatBackground MasterOnly, Identical  
CalculateTransmission MasterOnly, Identical  
CloneWorkspace all  
Comment all  
CompareWorkspace MasterOnly, Identical if one input has StorageMode::Cloned and the other has StorageMode::MasterOnly then ExecutionMode::MasterOnly is used, with ExecutionMode::MasterOnly the workspaces always compare equal on non-master ranks
CompressEvents all  
ConvertDiffCal MasterOnly, Identical  
ConvertToHistogram all  
ConvertToPointData all  
ConvertUnits all AlignBins not supported; for indirect energy mode the number of resulting bins is in general inconsistent across MPI ranks
CopyInstrumentParameters all  
CreateSingleValuedWorkspace Identical OutputWorkspace has StorageMode::Cloned, support of MasterOnly would require adding property for selecting the mode
CreateWorkspace all  
CropToComponent all  
CropWorkspace all see ExtractSpectra regarding X cropping
DeleteWorkspace all  
DetermineChunking MasterOnly, Identical  
Divide all see BinaryOperation
EstimateFitParameters MasterOnly, Identical see IFittingAlgorithm
EvaluateFunction MasterOnly, Identical see IFittingAlgorithm
ExponentialCorrection all see UnaryOperation
ExtractSingleSpectrum all in practice ExecutionMode::Distributed not supported due to current nonzero-spectrum-count limitation
ExtractSpectra2 all currently not available via algorithm factory or Python
ExtractSpectra all not supported with DetectorList, cropping in X may exhibit inconsistent behavior in case spectra have common boundaries within some ranks but not within all ranks or across ranks
FFTSmooth2 MasterOnly, Identical  
FilterBadPulses all  
FilterByLogValue all  
FilterByTime all  
FilterEventsByLogValuePreNexus Identical see IFileLoader
FindDetectorsInShape all  
FindPeakBackground MasterOnly, Identical  
FindPeaks MasterOnly, Identical  
Fit MasterOnly, Identical see IFittingAlgorithm
GeneratePythonScript MasterOnly  
GroupWorkspaces all grouping workspaces with mixed StorageMode is not supported
IFileLoader Identical implicitly adds support for many load-algorithms inheriting from this
IFittingAlgorithm MasterOnly, Identical implicitly adds support for several fit-algorithms inheriting from this
Load all actual supported mode is dictated by underlying load algorithm, which depends on file type
LoadAscii2 Identical see IFileLoader
LoadAscii Identical see IFileLoader
LoadBBY Identical see IFileLoader
LoadCalFile Identical  
LoadCanSAS1D Identical see IFileLoader
LoadDaveGrp Identical see IFileLoader
LoadDiffCal Identical  
LoadEmptyInstrument Identical see IFileLoader
LoadEventAndCompress Distributed  
LoadEventNexus Distributed storage mode of output cannot be changed via a parameter currently, min and max bin boundary are not globally the same
LoadEventPreNexus2 Identical see IFileLoader
LoadFITS Identical see IFileLoader
LoadGSS Identical see IFileLoader
LoadILLDiffraction Identical see IFileLoader
LoadILLIndirect2 Identical see IFileLoader
LoadILLReflectometry Identical see IFileLoader
LoadILLSANS Identical see IFileLoader
LoadILLTOF2 Identical see IFileLoader
LoadInstrument all  
LoadIsawPeaks Identical see IFileLoader
LoadISISNexus2 Identical see IFileLoader
LoadLLB Identical see IFileLoader
LoadMask Identical  
LoadMcStas Identical see IFileLoader
LoadMcStasNexus Identical see IFileLoader
LoadMD Identical see IFileLoader
LoadMLZ Identical see IFileLoader
LoadMuonNexus Identical see IFileLoader
LoadNexusLogs all  
LoadNexusMonitors2 Identical  
LoadNexusProcessed Identical see IFileLoader
LoadNXcanSAS Identical see IFileLoader
LoadNXSPE Identical see IFileLoader
LoadParameterFile all segfaults when used in unit tests with MPI threading backend due to #9365, normal use should be ok
LoadPDFgetNFile Identical see IFileLoader
LoadPreNexus Identical see IFileLoader
LoadQKK Identical see IFileLoader
LoadRawHelper Identical see IFileLoader
LoadRKH Identical see IFileLoader
LoadSassena Identical see IFileLoader
LoadSESANS Identical see IFileLoader
LoadSINQFocus Identical see IFileLoader
LoadSNSspec Identical see IFileLoader
LoadSPE Identical see IFileLoader
LoadSpice2D Identical see IFileLoader
LoadSQW2 Identical see IFileLoader
LoadSQW Identical see IFileLoader
LoadSwans Identical see IFileLoader
LoadTBL Identical see IFileLoader
LoadTOFRawNexus Identical see IFileLoader
Logarithm all see UnaryOperation
MaskBins all  
MaskDetectorsInShape all  
MaskSpectra all  
Minus all see BinaryOperation
MoveInstrumentComponent all  
MultipleScatteringCylinderAbsorption all  
Multiply all see BinaryOperation
NormaliseByCurrent all  
OneMinusExponentialCor all see UnaryOperation
PDDetermineCharacterizations all  
PDLoadCharacterizations Identical  
Plus all see BinaryOperation
PoissonErrors all see BinaryOperation
PolynomialCorrection all see UnaryOperation
Q1D2 all not all optional normalization inputs are supported
Power all see UnaryOperation
PowerLawCorrection all see UnaryOperation
RealFFT MasterOnly, Identical  
Rebin all  
RebinToWorkspace all WorkspaceToMatch must have StorageMode::Cloned
RemoveLowResTOF all  
RemovePromptPulse all  
RenameWorkspace all  
ReplaceSpecialValues all see UnaryOperation
RotateInstrumentComponent all  
SANSCalculateTransmission MasterOnly, Identical  
SANSConvertToQ all  
SANSConvertToWavelength all  
SANSConvertToWavelengthAndRebin all  
SANSCreateAdjustmentWorkspaces all  
SANSCreateWavelengthAndPixelAdjustment MasterOnly, Identical  
SANSCrop all  
SANSFitShiftScale MasterOnly, Identical  
SANSLoad MasterOnly, Identical child algorithms may actually be run with ExecutionMode::Distributed if that is their default
SANSMaskWorkspace all  
SANSMove all  
SANSNormalizeToMonitor MasterOnly, Identical  
SANSReductionCore all  
SANSScale all  
SANSSingleReduction all  
SANSSliceEvent all  
SANSStitch MasterOnly, Identical  
SaveFocusedXYE MasterOnly  
SaveGSS MasterOnly  
SaveNexus MasterOnly  
SaveNexusProcessed MasterOnly  
Scale all  
SetSampleMaterial all  
SetUncertainties MasterOnly, Identical  
SignalOverError all see UnaryOperation
SNSPowderReduction Distributed  
SortEvents all  
SortTableWorkspace MasterOnly, Identical  
StripPeaks MasterOnly, Identical  
StripVanadiumPeaks2 MasterOnly, Identical  
SumSpectra MasterOnly, Identical  
UnaryOperation all  
WeightedMean all see BinaryOperation

Currently none of the above algorithms works with StorageMode::Distributed in case there are zero spectra on any rank.

Footnotes

[1]The complexity and overhead of splitting the instrument, in particular given the overhead ensuing from handling all cases exemplified above, led to the decision split only the neutron data based on spectra, but not detectors.
[2]Some will argue that this should be GlobalWorkspaceIndex. However it is not an index of a workspace so the term GlobalSpectrumIndex has been chosen for clarity. On the user interface side this will still be named ‘workspace index’, dropping the ‘global’ since the distinction between global and local indices is irrelevant for users.