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invoking parLapply without a cluster fails to find a previously registered cluster > library(parallel) > setDefaultCluster(makePSOCKcluster(2)) > parLapply(X=1:2, fun=function(...) {}) Error in cut.default(i, breaks) : invalid number of intervals This is because in parLapply length(cl) is determined before defaultCluster(cl) is called. By inspection, this appears to be true of other high-level functions, but iro...

...nation. >From my point of view parLapply(cl = clu, X = 1:2, fun = fun, c = 1) shouldn't give an error. This could be easily avoided by using all the argument names in the custerApply call of parLapply which means changing, parLapply <- function(cl = NULL, X, fun, ...) { cl <- defaultCluster(cl) do.call(c, clusterApply(cl, x = splitList(X, length(cl)), fun = lapply, fun, ...), quote = TRUE) } to parLapply <- function (cl = NULL, X, fun, ...) { cl <- defaultCluster(cl) do.call(c, clusterApply(cl = cl, x = splitList(X, length(cl)), fun = la...

...:2, fun = fun, c = 1) >> shouldn't give an error. >> >> This could be easily avoided by using all the argument >> names in the custerApply call of parLapply which means changing, >> >> parLapply <- function(cl = NULL, X, fun, ...) { >> cl <- defaultCluster(cl) >> do.call(c, clusterApply(cl, x = splitList(X, length(cl)), >> fun = lapply, fun, ...), quote = TRUE) >> } >> >> to >> >> parLapply <- function (cl = NULL, X, fun, ...) { >> cl <- defaultCluster(cl) >> do....

...gt; parLapply(cl = clu, X = 1:2, fun = fun, c = 1) > shouldn't give an error. > > This could be easily avoided by using all the argument > names in the custerApply call of parLapply which means changing, > > parLapply <- function(cl = NULL, X, fun, ...) { > cl <- defaultCluster(cl) > do.call(c, clusterApply(cl, x = splitList(X, length(cl)), > fun = lapply, fun, ...), quote = TRUE) > } > > to > > parLapply <- function (cl = NULL, X, fun, ...) { > cl <- defaultCluster(cl) > do.call(c, clusterApply(cl = cl, x = splitL...

...ere are fewer chunks than workers, each worker will get **at most one chunk** and there is **no** load balancing. ## parLapplyLB This is how **parLapplyLB** splits the input list (with a bit of refactoring, for readability): ```r parLapplyLB <- function(cl = NULL, X, fun, ...) { cl <- defaultCluster(cl) chunks <- splitList(X, length(cl)) do.call(c, clusterApplyLB(cl, x = chunks, fun = lapply, fun, ...), quote = TRUE) } ``` For our examples, the chunks have these sizes: ```r > sapply(parallel:::splitList(1:100, 5), length) [1] 20 20 20 20 20 ``` There...

Hi! I recognized that the argument matching of clusterApply (and therefore parLapply) goes wrong when one of the arguments of the function is called "c". In this case, the argument "c" is used as cluster and the functions give the following error message "Error in checkCluster(cl) : not a valid cluster". Of course, "c" is for many reasons an unfortunate

...e is **no** load balancing. >> >> >> ## parLapplyLB >> >> This is how **parLapplyLB** splits the input list (with a bit of refactoring, for readability): >> >> ```r >> parLapplyLB <- function(cl = NULL, X, fun, ...) >> { >> cl <- defaultCluster(cl) >> >> chunks <- splitList(X, length(cl)) >> >> do.call(c, >> clusterApplyLB(cl, x = chunks, fun = lapply, fun, ...), >> quote = TRUE) >> } >> ``` >> >> For our examples, the chunks have these sizes: &...

...l get **at most one chunk** and there is **no** load balancing. > > > ## parLapplyLB > > This is how **parLapplyLB** splits the input list (with a bit of refactoring, for readability): > > ```r > parLapplyLB <- function(cl = NULL, X, fun, ...) > { > cl <- defaultCluster(cl) > > chunks <- splitList(X, length(cl)) > > do.call(c, > clusterApplyLB(cl, x = chunks, fun = lapply, fun, ...), > quote = TRUE) > } > ``` > > For our examples, the chunks have these sizes: > > ```r >> sapply(parall...

...;> ## parLapplyLB >>>> >>>> This is how **parLapplyLB** splits the input list (with a bit of >> refactoring, for readability): >>>> ```r >>>> parLapplyLB <- function(cl = NULL, X, fun, ...) >>>> { >>>> cl <- defaultCluster(cl) >>>> >>>> chunks <- splitList(X, length(cl)) >>>> >>>> do.call(c, >>>> clusterApplyLB(cl, x = chunks, fun = lapply, fun, ...), >>>> quote = TRUE) >>>> } >>>> ``...

...;>> >>> ## parLapplyLB >>> >>> This is how **parLapplyLB** splits the input list (with a bit of >refactoring, for readability): >>> >>> ```r >>> parLapplyLB <- function(cl = NULL, X, fun, ...) >>> { >>> cl <- defaultCluster(cl) >>> >>> chunks <- splitList(X, length(cl)) >>> >>> do.call(c, >>> clusterApplyLB(cl, x = chunks, fun = lapply, fun, ...), >>> quote = TRUE) >>> } >>> ``` >>> >>> For our...

Dear All, Is the following expected behaviour? set.seed(1) library(parallel) cl = makeCluster(5) clusterSetRNGStream(cl, iseed = NULL) parSapply(cl, 1:5, function(i) sample(1:10, 1)) # 7 4 2 10 10 clusterSetRNGStream(cl, iseed = NULL) # 7 4 2 10 10 parSapply(cl, 1:5, function(i) sample(1:10, 1)) stopCluster(cl) The documentation could be read either way, e.g. * iseed: An integer to be

...;>>> >>>>> This is how **parLapplyLB** splits the input list (with a bit of >>> refactoring, for readability): >>>>> ```r >>>>> parLapplyLB <- function(cl = NULL, X, fun, ...) >>>>> { >>>>> ???? cl <- defaultCluster(cl) >>>>> >>>>> ???? chunks <- splitList(X, length(cl)) >>>>> >>>>> ???? do.call(c, >>>>> ???????????? clusterApplyLB(cl, x = chunks, fun = lapply, fun, ...), >>>>> ???????????? quote = TRUE) >>>&g...