[
https://issues.apache.org/jira/browse/SYSTEMML-678?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sebastian Baunsgaard resolved SYSTEMML-678.
-------------------------------------------
Fix Version/s: SystemML 0.10
Resolution: Done
> MLContext parallelization
> -------------------------
>
> Key: SYSTEMML-678
> URL: https://issues.apache.org/jira/browse/SYSTEMML-678
> Project: SystemDS
> Issue Type: Question
> Components: Algorithms, Parser, Runtime
> Affects Versions: SystemML 0.10
> Reporter: Johannes Wilke
> Priority: Major
> Fix For: SystemML 0.10
>
>
> I try to execute script in the MLContext. It is executing, but it dont
> parallel. For smaller scripts, it works fine. But this script doesnt and it
> is not clear why. I think it is because of the 4 loop levels, but I am not
> sure.
> Is there a documentation what is parallizable and what isnt?
> If I change the main while-loop, i wish to parallize, to a parfor loop it
> works.
> Here is the script:
> X = read($Xin)
> P = read($Pin)
> #errorMatrix = matrix(0.0,rows=1,cols=1)
> j = 1
> sum = 0
> while (j <=nrow(X) & sum >= 0){ # this should be parallelized
> #parfor(j in 1: nrow(X),check=0){
> first = TRUE
> windows = matrix(0,rows=1,cols=1)
> offsetPreWindowDefinitions = 0
> sumWindowLength = 0
> mastercount = 0
> totalwindowLength = 0
> s = 0
> for(i in 1: nrow(P)){
> if((as.scalar(P[i,1])*as.scalar(P[i,2]))>totalwindowLength){
> totalwindowLength =
> (as.scalar(P[i,1])*as.scalar(P[i,2]))
> }
> s = s+1
> }
> lastWindow = matrix(0,rows=sum(P[,1]),cols=1)
>
> for(i in 1:nrow(P)){# for every Window-Definition
>
> for(k in 1: as.integer(as.scalar(P[i,1]))){# for every pnum
> column =
> matrix(0,rows=as.integer(as.scalar(P[1,4])),cols=1)
> for(l in 1: nrow(column)+1){
> offsetPreWindowDefinitions = totalwindowLength
> - (as.scalar(P[i,1])*as.scalar(P[i,2]))
> tsindex = ((k-1) * as.scalar(P[i,2])) + l-1 +
> offsetPreWindowDefinitions
> if(l==nrow(column)+1){
> lastWindow[sumWindowLength+k,1] =
> X[j,tsindex+1]
> } else {
>
> column[l,1] = X[j,tsindex+1]
> }
> mastercount = mastercount +1
> #print(mastercount)
> }
> if(first){
> first = FALSE;
> windows = column
> } else {
> windows = cbind(windows,column)
> }
> }
>
> sumWindowLength = sumWindowLength + as.scalar(P[i,1])
> }
>
>
> result = matrix(14.3,rows=as.integer(as.scalar(P[1,4])),cols=1)
> for(i in
> totalwindowLength:as.integer(as.scalar(P[1,4]))+totalwindowLength-1){
> result[i-totalwindowLength+1,1] = X[j,i+1]
> s = s+1
> }
> params = solve(windows,result)
> print(j)
> predict = matrix(0,rows=1, cols=1)
> for(i in 1:nrow(lastWindow)){
> predict[1,1] = predict[1,1] + (params[i,1] * lastWindow[i,1])
> s = s+1
> }
>
> predictscalar = as.scalar(predict[1,1])
> targetscalar = as.scalar(X[j,ncol(X)])
> sum = sum + ((targetscalar - predictscalar) * (targetscalar -
> predictscalar))
>
>
>
> j = j+1
> #write(lastWindow,
> "/media/johannes/Data/Seafile/UNI/Beleg/sysml_output/lWOut.csv",
> format="csv", header=TRUE, sep=",", sparse=TRUE);
> #write(windows,
> "/media/johannes/Data/Seafile/UNI/Beleg/sysml_output/windowsOut.csv",
> format="csv", header=TRUE, sep=",", sparse=TRUE);
> #write(result,
> "/media/johannes/Data/Seafile/UNI/Beleg/sysml_output/resultOut.csv",
> format="csv", header=TRUE, sep=",", sparse=TRUE);
> }
> print(sum/nrow(X))
> I hope that you can help me!
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
