Improve performance of R front end of OpenMx

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neale's picture
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Joined: 07/31/2009

So I have this model and it takes 800 seconds to compile in the front end. Yes, it has some 81 submodels in it, but still... Classic Mx had finished running the script in less time whereas Open is still going after 9hrs (preliminary results only, it is possible that model is mis-specified).

This thread caught my eye: http://www.r-bloggers.com/the-new-r-compiler-package-in-r-2-13-0-some-fi...

Is it worth trying to compile some bits of the OpenMx frontend to improve performance there?

mspiegel's picture
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Joined: 07/31/2009
Err, the short answer is no.

Err, the short answer is no. The R compiler will probably not improve the performance of the front-end. Another comment: the front-end time is currently less than 3% of the total runtime of that model (that number can only go down depending on how much more than 9 hours it takes to finish). So the first priority is to improve the performance of back-end.

neale's picture
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Joined: 07/31/2009
Yes & no

Well, with all parameters fixed (a useful way of developing new complex models to ensure that they're set up correctly) it looks like this:

frontend time: 790.3566 secs
backend time: 0.02779293 secs
independent submodels time: 9.012222e-05 secs
wall clock time: 790.3844 secs
cpu time: 790.3844 secs
openmx version number: 999.0.0-1661

So users' script development production cycle time could be reduced by front-end performance boosts. Corresponding results from Classic (but with parameters freely estimated):

Task Time elapsed (DD:HH:MM:SS)
Reading script & data 0: 0: 0: 0.54
Execution 0: 0: 9:20.46
TOTAL 0: 0: 9:21.00

Yes, I know that the overhead of R is expensive, and that it does buy the user a tremendous amount in terms of programing capabilities, data handling environment, graphics etc. etc. Indeed it was a delight to develop this script in OpenMx compared to Classic, but it is still the case that many users would benefit from any performance boosts we can muster, frontend or back. For example, Tim Bates' non-independent submodel suggestion is a good one and I think should be in 2.0.

mspiegel's picture
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Joined: 07/31/2009
OK. Here are some

OK. Here are some suggestions for profiling R code to determine where are the choke points. Immediately before the call to mxRun() place a call to Rprof("filename.out") and then after the call to mxRun() place a call to Rprof(NULL). You can either run the model with no free parameters, or another alternative is to call mxRun() with the argument 'onlyFrontend=TRUE'.

Once the run has finished, then at the unix command-line, you want to run R CMD Rprof filename.out

That will identify where are the bottlenecks in the front-end. The other task is to speedup the bottlenecks, or rewrite portions of the implementations to not use those bottlenecks.

neale's picture
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Joined: 07/31/2009
Profiling

Cool! I did that and got this:

Each sample represents 0.02 seconds.
Total run time: 471.52 seconds.

Total seconds: time spent in function and callees.
Self seconds: time spent in function alone.

% total % self
total seconds self seconds name
100.0 471.46 0.0 0.00 "mxRun"
100.0 471.46 0.0 0.00 "runHelper"
96.7 455.80 0.1 0.66 "translateObjectivesHelper"
96.7 455.80 0.0 0.00 "translateObjectives"
89.7 423.08 1.6 7.48 "imxFlattenModel"
86.5 407.84 72.1 339.90 "flattenModelHelper"
68.8 324.40 0.1 0.28 "standardGeneric"
66.6 314.22 0.1 0.60 "genericObjModelConvert"
66.5 313.62 0.1 0.48 "updateObjectiveDimnames"
66.1 311.78 0.0 0.00 "eval"
66.1 311.68 0.0 0.00 "mxEval"
8.1 37.96 0.1 0.32 "@<-"
8.0 37.70 0.1 0.28 "slot<-"
6.4 30.14 6.4 30.14 ".Call"
6.2 29.00 1.1 5.02 "generateLabelsMatrix"
6.2 29.00 0.0 0.00 "generateLabelsHelper"
6.2 29.00 0.0 0.00 "imxGenerateLabels"
5.5 25.76 5.5 25.76 "append"
4.9 23.26 0.1 0.32 "[<-"
4.9 22.94 2.8 13.26 "[<-.data.frame"
3.4 16.12 0.1 0.56 "lapply"
3.4 15.86 0.9 4.10 "FUN"
3.0 14.02 1.2 5.72 ".local"
2.0 9.58 1.8 8.54 "match"
1.7 8.18 0.0 0.00 "[[<-"
1.5 7.28 0.1 0.52 "checkSlotAssignment"
1.5 6.96 0.3 1.52 "updateModelEntitiesHelper"
1.4 6.86 0.4 2.02 "namespaceSearchReplaceHelper"
1.4 6.84 0.0 0.00 "imxReplaceMethod"
1.4 6.84 0.0 0.00 "namespaceSearchReplace"
1.4 6.68 0.0 0.20 "sapply"
1.2 5.76 0.0 0.00 "initialize"
1.2 5.76 0.0 0.00 "new"
0.9 4.42 0.2 0.84 "%in%"
0.9 4.14 0.0 0.00 "updateModelMatrices"
0.8 3.64 0.1 0.34 "as"
0.7 3.36 0.0 0.00 "convertAlgebras"
0.7 3.34 0.4 1.70 "convertSingleAlgebra"
0.6 2.90 0.2 1.06 "namespaceConvertFormula"
0.6 2.88 0.0 0.20 "genericObjFunNamespace"
0.6 2.88 0.0 0.00 "namespaceConvertObjective"
0.6 2.82 0.0 0.00 "updateModelAlgebras"
0.5 2.24 0.0 0.00 "imxGenerateNamespace"
0.5 2.22 0.0 0.00 "generateLocalNamespace"
0.5 2.14 0.2 0.98 "unlist"
0.5 2.10 0.0 0.00 "omxLapply"
0.4 2.06 0.1 0.44 "imxConvertIdentifier"
0.4 1.88 0.3 1.28 "populateDefInitialValues"
0.4 1.86 0.1 0.22 "elNamed"
0.3 1.66 0.1 0.58 "unique"
0.3 1.56 0.0 0.00 "checkEvaluation"
0.3 1.48 0.3 1.32 "grep"
0.3 1.48 0.0 0.02 "namespaceGetValues"
0.3 1.38 0.0 0.04 ".classEnv"
0.3 1.34 0.0 0.02 "flatNamespaceSearchReplace"
0.3 1.34 0.0 0.00 "flatReplaceMethod"
0.3 1.32 0.2 1.20 "doFlatNamespaceSearchReplace"
0.3 1.32 0.1 0.26 "hasSquareBrackets"
0.2 1.20 0.0 0.08 "getGeneric"
0.2 1.18 0.0 0.06 ".requirePackage"
0.2 1.16 0.1 0.50 "checkAlgebraEvaluation"
0.2 1.12 0.1 0.26 ".getGeneric"
0.2 0.86 0.2 0.86 "strsplit"
0.2 0.86 0.1 0.22 "imxIsDefinitionVariable"
0.2 0.84 0.0 0.02 "convertFormulaInsertModel"
0.2 0.82 0.1 0.56 "insertNumericValue"
0.2 0.78 0.1 0.42 "anyDuplicated"
0.2 0.78 0.0 0.02 "loadedNamespaces"
0.2 0.76 0.0 0.16 "ls"
0.1 0.64 0.0 0.08 "isLocalDefinitionVariable"
0.1 0.62 0.1 0.22 "possibleExtends"
0.1 0.60 0.1 0.48 "identical"
0.1 0.54 0.0 0.20 "NROW"
0.1 0.52 0.1 0.52 "all"
0.1 0.52 0.1 0.52 "sum"
0.1 0.52 0.1 0.28 "unique.default"
0.1 0.52 0.0 0.04 "tryCatch"
0.1 0.50 0.0 0.00 "try"
0.1 0.46 0.1 0.46 "!"
0.1 0.46 0.1 0.46 "inherits"
0.1 0.44 0.0 0.02 "imxExtractNames"
0.1 0.42 0.1 0.22 "getClass"
0.1 0.42 0.0 0.18 "tryCatchList"
0.1 0.38 0.1 0.38 "any"
0.1 0.38 0.1 0.36 "exists"
0.1 0.36 0.1 0.36 "anyDuplicated.default"
0.1 0.36 0.1 0.36 "length"
0.1 0.36 0.0 0.06 "loadMethod"
0.1 0.36 0.0 0.02 "namespaceGetEntities"
0.1 0.32 0.1 0.32 "el"
0.1 0.32 0.0 0.16 ".getMethodsTable"
0.1 0.32 0.0 0.00 "namespaceGetParameters"
0.1 0.30 0.0 0.16 "getClassDef"
0.1 0.28 0.1 0.28 "is.na"
0.1 0.28 0.1 0.24 ".getClassFromCache"
0.1 0.28 0.0 0.10 ".quickCoerceSelect"
0.1 0.28 0.0 0.04 "is.data.frame"
0.1 0.28 0.0 0.02 ".cacheGeneric"
0.1 0.28 0.0 0.00 "evaluateExpression"
0.1 0.28 0.0 0.00 "evaluateSymbol"
0.1 0.26 0.1 0.24 "=="
0.1 0.26 0.0 0.12 ".cacheGenericTable"
0.1 0.26 0.0 0.00 "computeAlgebra"
0.1 0.26 0.0 0.00 "evaluateAlgebra"
0.1 0.22 0.1 0.22 ".row_names_info"
0.1 0.22 0.1 0.22 ">"
0.1 0.22 0.1 0.22 "get"
0.1 0.22 0.1 0.22 "sys.call"
0.1 0.22 0.0 0.08 "tryCatchOne"
0.0 0.20 0.0 0.00 "checkNameAlignment"
0.0 0.18 0.0 0.18 ".identC"
0.0 0.18 0.0 0.00 "evaluateMatrix"
0.0 0.16 0.0 0.00 "computeMatrix"
0.0 0.14 0.0 0.14 "attr"
0.0 0.14 0.0 0.14 "is.null"
0.0 0.14 0.0 0.08 "apply"
0.0 0.14 0.0 0.06 "doTryCatch"
0.0 0.14 0.0 0.06 "imxIdentifier"
0.0 0.14 0.0 0.00 "cycleDetection"
0.0 0.14 0.0 0.00 "intersect"
0.0 0.12 0.0 0.12 "is.character"
0.0 0.12 0.0 0.04 "getNamespace"
0.0 0.12 0.0 0.00 "checkVariablesHelper"
0.0 0.12 0.0 0.00 "imxAddDependency"
0.0 0.12 0.0 0.00 "imxCheckVariables"
0.0 0.12 0.0 0.00 "is"
0.0 0.12 0.0 0.00 "ncol"
0.0 0.10 0.0 0.10 "is.vector"
0.0 0.10 0.0 0.10 "paste"
0.0 0.10 0.0 0.04 "localNamespaceSearchReplace"
0.0 0.10 0.0 0.00 "addAlgebraDetection"
0.0 0.10 0.0 0.00 "addFormulaDetection"
0.0 0.10 0.0 0.00 "evaluateMxObject"
0.0 0.10 0.0 0.00 "nrow"
0.0 0.08 0.0 0.08 "assign"
0.0 0.08 0.0 0.08 "list"
0.0 0.08 0.0 0.08 "match.fun"
0.0 0.08 0.0 0.08 "max"
0.0 0.08 0.0 0.08 "names<-"
0.0 0.08 0.0 0.06 "addEdge"
0.0 0.08 0.0 0.00 "[["
0.0 0.06 0.0 0.06 "&"
0.0 0.06 0.0 0.06 "is.list"
0.0 0.04 0.0 0.04 "as.character"
0.0 0.04 0.0 0.04 "class"
0.0 0.04 0.0 0.04 "class<-"
0.0 0.04 0.0 0.04 "col"
0.0 0.04 0.0 0.04 "environment"
0.0 0.04 0.0 0.04 "is.array"
0.0 0.04 0.0 0.04 "nargs"
0.0 0.04 0.0 0.04 "seq_len"
0.0 0.04 0.0 0.04 "xpdrows.data.frame"
0.0 0.04 0.0 0.02 ".class1"
0.0 0.04 0.0 0.02 "flatNamespaceSearch"
0.0 0.04 0.0 0.02 "is.factor"
0.0 0.04 0.0 0.00 "addNode"
0.0 0.04 0.0 0.00 "callNextMethod"
0.0 0.04 0.0 0.00 "dimnames<-"
0.0 0.04 0.0 0.00 "flatExtractMethod"
0.0 0.04 0.0 0.00 "genericObjNewEntities"
0.0 0.04 0.0 0.00 "library"
0.0 0.04 0.0 0.00 "mxMatrix"
0.0 0.04 0.0 0.00 "packageSlot"
0.0 0.04 0.0 0.00 "union"
0.0 0.02 0.0 0.02 "!="
0.0 0.02 0.0 0.02 "+"
0.0 0.02 0.0 0.02 "<"
0.0 0.02 0.0 0.02 "<="
0.0 0.02 0.0 0.02 "Ops.data.frame"
0.0 0.02 0.0 0.02 "as.name"
0.0 0.02 0.0 0.02 "as.symbol"
0.0 0.02 0.0 0.02 "assignDimnames"
0.0 0.02 0.0 0.02 "file.exists"
0.0 0.02 0.0 0.02 "is.numeric"
0.0 0.02 0.0 0.02 "lazyLoadDBfetch"
0.0 0.02 0.0 0.02 "names"
0.0 0.02 0.0 0.02 "populateDefVarMatrix"
0.0 0.02 0.0 0.00 ".sigLabel"
0.0 0.02 0.0 0.00 "addObjectiveDetection"
0.0 0.02 0.0 0.00 "attachNamespace"
0.0 0.02 0.0 0.00 "checkConflicts"
0.0 0.02 0.0 0.00 "containsCycle"
0.0 0.02 0.0 0.00 "convertFormula"
0.0 0.02 0.0 0.00 "convertObjectives"
0.0 0.02 0.0 0.00 "cycleVisitor"
0.0 0.02 0.0 0.00 "do.call"
0.0 0.02 0.0 0.00 "generateAlgebraList"
0.0 0.02 0.0 0.00 "genericObjDependencies"
0.0 0.02 0.0 0.00 "genericObjFunConvert"
0.0 0.02 0.0 0.00 "imxExtractMethod"
0.0 0.02 0.0 0.00 "imxSameType"
0.0 0.02 0.0 0.00 "imxVerifyMatrix"
0.0 0.02 0.0 0.00 "imxVerifyName"
0.0 0.02 0.0 0.00 "isNumber"
0.0 0.02 0.0 0.00 "lookupNumericValue"
0.0 0.02 0.0 0.00 "namespaceModelSearch"
0.0 0.02 0.0 0.00 "namespaceSearch
0.0 0.02 0.0 0.00 "namespaceSearch"
0.0 0.02 0.0 0.00 "omxCheckMatrices"
0.0 0.02 0.0 0.00 "same.isFn"
0.0 0.02 0.0 0.00 "shareData"
0.0 0.02 0.0 0.00 "shareDataHelper"
0.0 0.02 0.0 0.00 "substituteOperators"

% self % total
self seconds total seconds name
72.1 339.90 86.5 407.84 "flattenModelHelper"
6.4 30.14 6.4 30.14 ".Call"
5.5 25.76 5.5 25.76 "append"
2.8 13.26 4.9 22.94 "[<-.data.frame"
1.8 8.54 2.0 9.58 "match"
1.6 7.48 89.7 423.08 "imxFlattenModel"
1.2 5.72 3.0 14.02 ".local"
1.1 5.02 6.2 29.00 "generateLabelsMatrix"
0.9 4.10 3.4 15.86 "FUN"
0.4 2.02 1.4 6.86 "namespaceSearchReplaceHelper"
0.4 1.70 0.7 3.34 "convertSingleAlgebra"
0.3 1.52 1.5 6.96 "updateModelEntitiesHelper"
0.3 1.32 0.3 1.48 "grep"
0.3 1.28 0.4 1.88 "populateDefInitialValues"
0.2 1.20 0.3 1.32 "doFlatNamespaceSearchReplace"
0.2 1.06 0.6 2.90 "namespaceConvertFormula"
0.2 0.98 0.5 2.14 "unlist"
0.2 0.86 0.2 0.86 "strsplit"
0.2 0.84 0.9 4.42 "%in%"
0.1 0.66 96.7 455.80 "translateObjectivesHelper"
0.1 0.60 66.6 314.22 "genericObjModelConvert"
0.1 0.58 0.3 1.66 "unique"
0.1 0.56 3.4 16.12 "lapply"
0.1 0.56 0.2 0.82 "insertNumericValue"
0.1 0.52 1.5 7.28 "checkSlotAssignment"
0.1 0.52 0.1 0.52 "all"
0.1 0.52 0.1 0.52 "sum"
0.1 0.50 0.2 1.16 "checkAlgebraEvaluation"
0.1 0.48 66.5 313.62 "updateObjectiveDimnames"
0.1 0.48 0.1 0.60 "identical"
0.1 0.46 0.1 0.46 "!"
0.1 0.46 0.1 0.46 "inherits"
0.1 0.44 0.4 2.06 "imxConvertIdentifier"
0.1 0.42 0.2 0.78 "anyDuplicated"
0.1 0.38 0.1 0.38 "any"
0.1 0.36 0.1 0.38 "exists"
0.1 0.36 0.1 0.36 "anyDuplicated.default"
0.1 0.36 0.1 0.36 "length"
0.1 0.34 0.8 3.64 "as"
0.1 0.32 8.1 37.96 "@<-"
0.1 0.32 4.9 23.26 "[<-"
0.1 0.32 0.1 0.32 "el"
0.1 0.28 68.8 324.40 "standardGeneric"
0.1 0.28 8.0 37.70 "slot<-"
0.1 0.28 0.1 0.52 "unique.default"
0.1 0.28 0.1 0.28 "is.na"
0.1 0.26 0.3 1.32 "hasSquareBrackets"
0.1 0.26 0.2 1.12 ".getGeneric"
0.1 0.24 0.1 0.28 ".getClassFromCache"
0.1 0.24 0.1 0.26 "=="
0.1 0.22 0.4 1.86 "elNamed"
0.1 0.22 0.2 0.86 "imxIsDefinitionVariable"
0.1 0.22 0.1 0.62 "possibleExtends"
0.1 0.22 0.1 0.42 "getClass"
0.1 0.22 0.1 0.22 ".row_names_info"
0.1 0.22 0.1 0.22 ">"
0.1 0.22 0.1 0.22 "get"
0.1 0.22 0.1 0.22 "sys.call"
0.0 0.20 1.4 6.68 "sapply"
0.0 0.20 0.6 2.88 "genericObjFunNamespace"
0.0 0.20 0.1 0.54 "NROW"
0.0 0.18 0.1 0.42 "tryCatchList"
0.0 0.18 0.0 0.18 ".identC"
0.0 0.16 0.2 0.76 "ls"
0.0 0.16 0.1 0.32 ".getMethodsTable"
0.0 0.16 0.1 0.30 "getClassDef"
0.0 0.14 0.0 0.14 "attr"
0.0 0.14 0.0 0.14 "is.null"
0.0 0.12 0.1 0.26 ".cacheGenericTable"
0.0 0.12 0.0 0.12 "is.character"
0.0 0.10 0.1 0.28 ".quickCoerceSelect"
0.0 0.10 0.0 0.10 "is.vector"
0.0 0.10 0.0 0.10 "paste"
0.0 0.08 0.2 1.20 "getGeneric"
0.0 0.08 0.1 0.64 "isLocalDefinitionVariable"
0.0 0.08 0.1 0.22 "tryCatchOne"
0.0 0.08 0.0 0.14 "apply"
0.0 0.08 0.0 0.08 "assign"
0.0 0.08 0.0 0.08 "list"
0.0 0.08 0.0 0.08 "match.fun"
0.0 0.08 0.0 0.08 "max"
0.0 0.08 0.0 0.08 "names<-"
0.0 0.06 0.2 1.18 ".requirePackage"
0.0 0.06 0.1 0.36 "loadMethod"
0.0 0.06 0.0 0.14 "doTryCatch"
0.0 0.06 0.0 0.14 "imxIdentifier"
0.0 0.06 0.0 0.08 "addEdge"
0.0 0.06 0.0 0.06 "&"
0.0 0.06 0.0 0.06 "is.list"
0.0 0.04 0.3 1.38 ".classEnv"
0.0 0.04 0.1 0.52 "tryCatch"
0.0 0.04 0.1 0.28 "is.data.frame"
0.0 0.04 0.0 0.12 "getNamespace"
0.0 0.04 0.0 0.10 "localNamespaceSearchReplace"
0.0 0.04 0.0 0.04 "as.character"
0.0 0.04 0.0 0.04 "class"
0.0 0.04 0.0 0.04 "class<-"
0.0 0.04 0.0 0.04 "col"
0.0 0.04 0.0 0.04 "environment"
0.0 0.04 0.0 0.04 "is.array"
0.0 0.04 0.0 0.04 "nargs"
0.0 0.04 0.0 0.04 "seq_len"
0.0 0.04 0.0 0.04 "xpdrows.data.frame"
0.0 0.02 0.3 1.48 "namespaceGetValues"
0.0 0.02 0.3 1.34 "flatNamespaceSearchReplace"
0.0 0.02 0.2 0.84 "convertFormulaInsertModel"
0.0 0.02 0.2 0.78 "loadedNamespaces"
0.0 0.02 0.1 0.44 "imxExtractNames"
0.0 0.02 0.1 0.36 "namespaceGetEntities"
0.0 0.02 0.1 0.28 ".cacheGeneric"
0.0 0.02 0.0 0.04 ".class1"
0.0 0.02 0.0 0.04 "flatNamespaceSearch"
0.0 0.02 0.0 0.04 "is.factor"
0.0 0.02 0.0 0.02 "!="
0.0 0.02 0.0 0.02 "+"
0.0 0.02 0.0 0.02 "<"
0.0 0.02 0.0 0.02 "<="
0.0 0.02 0.0 0.02 "Ops.data.frame"
0.0 0.02 0.0 0.02 "as.name"
0.0 0.02 0.0 0.02 "as.symbol"
0.0 0.02 0.0 0.02 "assignDimnames"
0.0 0.02 0.0 0.02 "file.exists"
0.0 0.02 0.0 0.02 "is.numeric"
0.0 0.02 0.0 0.02 "lazyLoadDBfetch"
0.0 0.02 0.0 0.02 "names"
0.0 0.02 0.0 0.02 "populateDefVarMatrix"
Warning message:
In readLines(con, n = chunksize) :
incomplete final line found on 'RegimeSwitching_MatrixRawFixed.Rprof'

I am not experienced in parsing such output, but:

> R CMD Rprof RegimeSwitching_MatrixRawFixed.Rprof | awk '{if ($4 > 200) print $4,$5}'

seems to implicate the following routines:

339.90 "flattenModelHelper"
total
seconds name
407.84 "flattenModelHelper"
423.08 "imxFlattenModel"
455.80 "translateObjectivesHelper"
314.22 "genericObjModelConvert"
313.62 "updateObjectiveDimnames"
324.40 "standardGeneric"

Would that be your take also?

mspiegel's picture
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Joined: 07/31/2009
I tend to look at the "self

I tend to look at the "self seconds" column before I look at the "total seconds" column. The former can give me an idea for what functions could be re-written to improve performance. The "total seconds" column is useful, but then I need to determine which of the transitive closure of function calls contribute to the runtime.

I've checked in a patch to the trunk that brings down the front-end time on your model from 886 seconds to 320 seconds. We have a discrepancy in our baseline performance, but hopefully you will notice some improvement after applying the patch. I was testing with R 2.11.1 on MacPro (Quad-core 3 GHz Xeon).

neale's picture
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Joined: 07/31/2009
Neat

Great - I'll give it a go.

Another decidedly odd thing about this model (when the parameters are free) - I added checkpoint=TRUE to the mxRun call, and indeed after about 800 seconds it created a checkpoint file. However, it has not updated it in the last 8 hours, although it is still hitting the CPU.

mdewey's picture
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Joined: 01/21/2011
Is it stuck at the end?

I had what may, or may not, be a similar issue which mspiegel addressed in this thread

http://openmx.psyc.virginia.edu/thread/830

Disclaimer: I have not read, much less run, your script so cannot be sure this is the issue.

mspiegel's picture
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Joined: 07/31/2009
I had forgotten that

I had forgotten that possibility. We do not checkpoint inside the standard error or explicit hessian calculations. At one point I stared at the code to see if we could add checkpointing. See they consisted mostly of some calls to BLAS, it seemed unlikely.

tbrick's picture
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Joined: 07/31/2009
We could do something that

We could do something that resembled checkpointing during the hessian/gradient calculations.

Hessian/gradient calculation in OpenMx uses an iterative process that essentially takes small steps in each direction to estimate the change to the objective function in those directions. We could save the objective values at those locations and use them, if we restored from a checkpoint, to avoid having to recalculate.

One catch here is that the checkpoint file should probably not be the primary form of user feedback. If we really want to send feedback to the user about the state of the optimization, we should provide users a switch that either sends text directly to R's standard output or to a file or socket.

I haven't really heard much of a call for either of these features (status output or checkpointing during hessian/gradient calculation). Are either of them likely to be helpful?

neale's picture
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Joined: 07/31/2009
Every Function Evaluation, Please!

We should be able to request checkpointing at EVERY function evaluation. I have used this to debug difficult problems with Classic Mx thousands of times. It is really valuable in finding why one gets NANs or infinite values for the objective function. Even the most experienced users cannot always guess what it is with a model that causes infinite value for NAN. A useful trick is to print the parameter estimates BEFORE the function is evaluated, since things can crash during function evaluation, and it's nice to know what caused it. I realize that this is not possible if checkpoint data includes the fit function, unless that can be inserted after its successful evaluation (backspace the file, ugh, and rewrite the line). Otherwise change the order in the checkpoint file so that the fit function value can be appended to the end of it (using a no new line edit descriptor).

That being said, there is clearly some problem with the model I am trying to run. It works when some of the parameters are free, but not when they all are - in which case it just hangs, and no I don't get updated checkpoints. These are resolutely 0 bytes long, even after the 5 minutes or so for front-end compiling.

Anyway, progress to date is that the script with Fixed at the end of the name has some 9 parameters free, and it runs happily since I svn up'd to include mspiegel's faster frontend (which is a great innovation). The second script causes R to hang (and burn CPU) and produces a 0 byte checkpoint file. You have to get rid of the .txt extension on the data file for the scripts to see it.

AttachmentSize
RegimeSwitching_MatrixRawFixed.R 9.41 KB
RegimeSwitching_MatrixRaw.R 9.48 KB
sel1.1.txt 21.59 KB
mspiegel's picture
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Well, I can tell you why it

Well, I can tell you why it takes a long time for the model to run. I think there's an infinite loop somewhere in omxFIMLObjective.c:193-371. If I turn on the debugging output to print the row number, at some point after a lot of calculations it just hits row 173 and then keeps on printing row 173 over and over.

neale's picture
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And I can tell you (me) how to avoid the problem

I found a work-around, which is to lbound the residuals estimates to .1

Nevertheless, we should fix this issue - it looks as though the infinite loop may have something to do with a non-positive predicted covariance matrix.

tbrick's picture
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You really shouldn't be

You really shouldn't be getting a zero-byte checkpoint file. If nothing else, there should probably be a checkpoint at the starting values. I'll take a look at that when I get a chance.

Changes to the checkpoint spec may take a bit more work. If we're writing out checkpoints at every function evaluation, we'll need to do some work to figure out which one is the last valid starting point before restoring--I think there are cases where the parameter values at minor iterations are not valid starting points. If it will be useful to users, though, we can tack it onto the feature list for a future release.

neale's picture
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checkpoint validity

Usually the between-major-iteration changes to the parameter values are very small, so whatever was the last printed set would be a suitable starting point. In the case where optimization bombed - an NAN or infinite value or something - then the penultimate set would be suitable instead.

tbrick's picture
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How many free parameters are

How many free parameters are there in this model?

If there are a lot, that might be the reason for the slow checkpointing.

OpenMx currently only checkpoints at major iterations. This is because we don't have a clear way to get speedup from knowing where the last minor iteration was; NPSOL only lets us specify a starting point and a hessian when we restart the optimization. Estimating the initial hessian values takes on the order of k^2 evaluations of the objective function, where k is the number of free parameters. For models with a very large number of free parameters and an objective that takes a long time to process, that first set of calculations can be a doozy.

We've talked before about writing to the checkpoint file at minor iterations even though there's no way to specifically restore the computation from that checkpoint. That's not a big change, and it would at least reassure the user that OpenMx is actually doing something useful and isn't just spinning its wheels. And the trail of computed values of the objective function could be useful to some users.

We're also looking at different ways to speed the computation up, including algebraic computation of gradient and/or hessian values. There's still work to be done on those, but they should start making an appearance over the next several releases.

tbates's picture
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dataframes -> lists or matrices?

Have you guys been following the discussion on dataframes in R-dev?

I wonder if converting dataframes to lists might buy a speed up in the front end?

I guess too, that mxRun might output a progress bar (just a dot for each model it compiles before kicking off the backend...)

rgore's picture
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We looked at this a couple months ago but discussed it via email

Here is the body of what was discussed by Tim Armstrong:

I was curious so I had a bit of a poke around to see what info was out there on what is actually going on with the compiler.

From what I can gather, what is doing is actually very different from most JIT compilers. The Java compiler for example goes through a few different transformations at different times:
*At compile time*: Source Code ==> Abstract Syntax Tree ==> Bytecode
*At run time*: Bytecode is interpreted, Bytecode ==> Native code (only if a function is a hot spot)

What normally happens with R is that when the script is loaded, the source code is transformed into an abstract syntax tree and then that tree is interpreted directly. So for standard R, what happens is:
*At compile time (or script load time)*: Source Code => Abstract Syntax Tree
*At run time*: abstract syntax tree is interpreted

When we turn JIT on, I gather what happens is:
*At compile time (or script load time)*: Source Code => Abstract Syntax Tree
*At run time*: Abstract Syntax Tree => bytecode (when function first called), then bytecode is interpreted

Interpreting bytecode is still very slow compared to native code, and transforming code which only runs a few times would add a lot of overhead, so perhaps it's not so surprising that there isn't a big performance boost.

Wow.. I looked at the code and it turns out that the compiler itself is entirely written in R rather that C, so perhaps its not so surprising that it adds a lot of overhead

neale's picture
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Compiled R

Oh yes, I do recall that. Bummer!