On Mon, Jan 7, 2019 at 4:09 AM Martin Morgan <[email protected]> wrote: > > I hope for 1. to have a 'local socket' (i.e., not using ports) implementation > shortly. > > I committed a patch in 1.17.6 for the wrong-seeming behavior of 2. We now have > > > library(BiocParallel) > > set.seed(1); p = bpparam(); rnorm(1) > [1] -0.6264538 > > set.seed(1); p = bpparam(); rnorm(1) > [1] -0.6264538 > > at the expensive of using the generator when the package is loaded. > > > set.seed(1); rnorm(1) > [1] -0.6264538 > > set.seed(1); library(BiocParallel); rnorm(1) > [1] 0.1836433 > > Is that bad? It will be consistent across platforms.
Contrary to resampling methods etc., the RNG for generating random ports does _not_ have to be statistically sound. Because of this, you should be able to generate a random port while *preserving the RNG state*, i.e. undoing .GlobalEnv$.Random.seed (including removing if in a fresh R session). I decided to add this approach for future::makeClusterPSOCK() [https://github.com/HenrikBengtsson/future/blob/073e1d1/R/makeClusterPSOCK.R#L76-L82]. BTW, here's a useful development tool for detecting when the .Random.seed has been updated in an interactive session: addTaskCallback(local({ last <- .GlobalEnv$.Random.seed function(...) { curr <- .GlobalEnv$.Random.seed if (!identical(curr, last)) { warning(".Random.seed changed", call. = FALSE, immediate. = TRUE) last <<- curr } TRUE } }), name = "RNG tracker") > > This behavior > > > set.seed(1); unlist(bplapply(1:2, function(i) rnorm(1))) > [1] 0.9624337 0.8925947 > > set.seed(1); unlist(bplapply(1:2, function(i) rnorm(1))) > [1] -0.5703597 0.1102093 > > seems wrong, but is consistent with mclapply > > > set.seed(1); unlist(mclapply(1:2, function(i) rnorm(1))) > [1] -0.02704527 0.40721777 > > set.seed(1); unlist(mclapply(1:2, function(i) rnorm(1))) > [1] -0.8239765 1.2957928 FWIW, without having invested much time tracking down why the design is what it is, I'm getting concerned about how parallel::mclapply() does parallel RNG out of the box when RNGkind("L'Ecuyer-CMRG") is in use. For example: > library(parallel) > RNGkind("L'Ecuyer-CMRG") > unlist(mclapply(1:2, function(i) rnorm(1))) ## mc.set.seed = TRUE [1] -0.71727 0.56171 > unlist(mclapply(1:2, function(i) rnorm(1))) [1] -0.71727 0.56171 > unlist(mclapply(1:2, function(i) rnorm(1))) [1] -0.71727 0.56171 I wonder how many resampling/bootstrap studies are bitten by this behavior. Same if you use mc.set.seed = FALSE; > unlist(mclapply(1:2, function(i) rnorm(1), mc.set.seed = FALSE)) [1] -0.8518311 -0.8518311 > unlist(mclapply(1:2, function(i) rnorm(1), mc.set.seed = FALSE)) [1] -0.8518311 -0.8518311 > unlist(mclapply(1:2, function(i) rnorm(1), mc.set.seed = FALSE)) [1] -0.8518311 -0.8518311 Here is how I think about parallel RNG right now: 1. To achieve fully numerically reproducible RNGs in way that is *invariant to the number of workers* (amount of chunking), I think the only solution is to pregenerated RNG seeds (using parallel::nextRNGStream()) for each individual iteration (element). In other words, if a worker will process K elements, then the main R process needs to generate K RNG seeds and pass those along to the work. I use this approach for future.apply::future_lapply(..., future.seed = TRUE/<initial_seed>), which then produce identical RNG results regardless of backend and amount of chunking. In the past, I think I've seen Martin suggesting something similar as a manual approach to some users. 2. The above approach is obviously expensive, especially when there are a large number of elements to iterate over. Because of this I'm thinking providing an option to use only one RNG seed per worker (which is the common approach used elsewhere) [https://github.com/HenrikBengtsson/future.apply/issues/20]. This won't be invariant to the number of workers, but it "should" still be statistically sound. This approach will give reproducible RNG results given the same initial seed and the same amount of chunking. 3. For algorithms which do not rely on RNG, we can ignore both of the above. The problem is that it's not always known to the user/developer which methods depend on RNG or not. The above 'RNG tracker' helps to identify some, but things might also change over time. I believe there's room for automating this in one way or the other. For instance, having a way to declare a function being dependent on RNG or not could help. Static code inspection could also do it, e.g. when an R package is built and it could be part of the R CMD checks to validate. 4. Are there other approaches? /Henrik > > The documented behavior is to us the RNGseed= argument to *Param, but I think > it could be made consistent (by default, obey the global random number seed > on workers) at least on a single machine (where the default number of cores > is constant). > > I have not (yet?) changed the default behavior to SerialParam. I guess the > cost of SerialParam is from the dependent packages that need to be loaded > > > system.time(suppressPackageStartupMessages(library(DelayedArray))) > user system elapsed > 3.068 0.082 3.150 > > If fastMNN() makes several calls to bplapply(), it might make sense to start > the default cluster at the top of the function once > > if (!isup(bpparam())) { > bpstart(bpparam()) > on.exit(bpstop(bpparam())) > } > > Martin > > On 1/6/19, 11:16 PM, "Bioc-devel on behalf of Aaron Lun" > <[email protected] on behalf of > [email protected]> wrote: > > As we know, the default BiocParallel backends are currently set to > MulticoreParam (Linux/Mac) or SnowParam (Windows). I can understand this to > some extent because a new user running, say, bplapply() without additional > arguments or set-up would expect some kind of parallelization. However, from > a developer’s perspective, I would argue that it makes more sense to use > SerialParam() by default. > > 1. It avoids problems with MulticoreParam stalling (especially on Macs) > when the randomly chosen port is in already use. This used to be a major > problem, to the point that all my BiocParallel-using functions in scran > passed BPPARAM=SerialParam() by default. Setting SerialParam() as package > default would ensure BiocParallel functions run properly in the first place; > if the code stalls due to switching to MulticoreParam, then it’s obvious > where the problem lies (and how to fix it). > > 2. It avoids the alteration of the random seed when the MulticoreParam > instance is constructed for the first time. > > library(BiocParallel) # new R session > set.seed(100) > invisible(bplapply(1:5, identity)) > rnorm(1) # 0.1315312 > set.seed(100) > invisible(bplapply(1:5, identity)) > rnorm(1) # -0.5021924 > > This is because the first bplapply() call calls bpparam(), which > constructs a MulticoreParam() for the first time; this calls the PRNG to > choose a random port number. Ensuing random numbers are altered, as seen > above. To avoid this, I need to define the MulticoreParam() object prior to > set.seed(), which undermines the utility of a default-defined bpparam(). > > 3. Job dispatch via SnowParam() is quite slow, which potentially makes > Windows package builds run slower by default. A particularly bad example is > that of scran::fastMNN(), which has a few matrix multiplications that use > DelayedArray:%*%. The %*% is parallelized with the default bpparam(), > resulting in SNOW parallelization on Windows. This slowed down fastMNN()’s > examples from 4 seconds (unix) to ~100 seconds (windows). Clearly, serial > execution is the faster option here. A related problem is MulticoreParam()’s > tendency to copy the environment, which may result in problems from inflated > memory consumption. > > So, can we default to SerialParam() on all platforms? And by this I mean > the BiocParallel in-built default - I don’t want to have to instruct all my > users to put a “register(SerialParam())” at the start of their analysis > scripts. I feel that BiocParallel’s job is to provide downstream code with > the potential for parallelization. If end-users want actual parallelization, > they had better be prepared to specify an appropriate scheme via *Param() > objects. > > -A > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/bioc-devel > > _______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/bioc-devel _______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
