Dear Christian and Henrik, thank you for spotting the problem and suggestions for a fix. We'll probably add a chunk.size argument to parLapplyLB and parLapply to follow OpenMP terminology, which has already been an inspiration for the present code (parLapply already implements static scheduling via internal function staticClusterApply, yet with a fixed chunk size; parLapplyLB already implements dynamic scheduling via internal function dynamicClusterApply, but with a fixed chunk size set to an unlucky value so that it behaves like static scheduling). The default chunk size for parallelLapplyLB will be set so that there is some dynamism in the schedule even by default. I am now testing a patch with these changes. Best Tomas On 02/20/2018 11:45 AM, Christian Krause wrote:> Dear Henrik, > > The rationale is just that it is within these extremes and that it is really simple to calculate, without making any assumptions and knowing that it won't be perfect. > > The extremes A and B you are mentioning are special cases based on assumptions. Case A is based on the assumption that the function has a long runtime or varying runtime, then you are likely to get the best load balancing with really small chunks. Case B is based on the assumption that the function runtime is the same for each list element, i.e. where you don't actually need load balancing, i.e. just use `parLapply` without load balancing. > > This new default is **not the best one**. It's just a better one than we had before. There is no best one we can use as default because **we don't know the function runtime and how it varies**. The user needs to decide that because he/she knows the function. As mentioned before, I will write a patch that makes the chunk size an optional argument, so the user can decide because only he/she has all the information to choose the best chunk size, just like you did with the `future.scheduling` parameter. > > Best Regards > > On February 19, 2018 10:11:04 PM GMT+01:00, Henrik Bengtsson <henrik.bengtsson at gmail.com> wrote: >> Hi, I'm trying to understand the rationale for your proposed amount of >> splitting and more precisely why that one is THE one. >> >> If I put labels on your example numbers in one of your previous post: >> >> nbrOfElements <- 97 >> nbrOfWorkers <- 5 >> >> With these, there are two extremes in how you can split up the >> processing in chunks such that all workers are utilized: >> >> (A) Each worker, called multiple times, processes one element each >> time: >> >>> nbrOfElements <- 97 >>> nbrOfWorkers <- 5 >>> nbrOfChunks <- nbrOfElements >>> sapply(parallel:::splitList(1:nbrOfElements, nbrOfChunks), length) >> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >> [30] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >> [59] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >> [88] 1 1 1 1 1 1 1 1 1 1 >> >> >> (B) Each worker, called once, processes multiple element: >> >>> nbrOfElements <- 97 >>> nbrOfWorkers <- 5 >>> nbrOfChunks <- nbrOfWorkers >>> sapply(parallel:::splitList(1:nbrOfElements, nbrOfChunks), length) >> [1] 20 19 19 19 20 >> >> I understand that neither of these two extremes may be the best when >> it comes to orchestration overhead and load balancing. Instead, the >> best might be somewhere in-between, e.g. >> >> (C) Each worker, called multiple times, processing multiple elements: >> >>> nbrOfElements <- 97 >>> nbrOfWorkers <- 5 >>> nbrOfChunks <- nbrOfElements / nbrOfWorkers >>> sapply(parallel:::splitList(1:nbrOfElements, nbrOfChunks), length) >> [1] 5 5 5 5 4 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 >> >> However, there are multiple alternatives between the two extremes, e.g. >> >>> nbrOfChunks <- scale * nbrOfElements / nbrOfWorkers >> So, is there a reason why you argue for scale = 1.0 to be the optimal? >> >> FYI, In future.apply::future_lapply(X, FUN, ...) there is a >> 'future.scheduling' scale factor(*) argument where default >> future.scheduling = 1 corresponds to (B) and future.scheduling = +Inf >> to (A). Using future.scheduling = 4 achieves the amount of >> load-balancing you propose in (C). (*) Different definition from the >> above 'scale'. (Disclaimer: I'm the author) >> >> /Henrik >> >> On Mon, Feb 19, 2018 at 10:21 AM, Christian Krause >> <christian.krause at idiv.de> wrote: >>> Dear R-Devel List, >>> >>> I have installed R 3.4.3 with the patch applied on our cluster and >> ran a *real-world* job of one of our users to confirm that the patch >> works to my satisfaction. Here are the results. >>> The original was a series of jobs, all essentially doing the same >> stuff using bootstrapped data, so for the original there is more data >> and I show the arithmetic mean with standard deviation. The >> confirmation with the patched R was only a single instance of that >> series of jobs. >>> ## Job Efficiency >>> >>> The job efficiency is defined as (this is what the `qacct-efficiency` >> tool below does): >>> ``` >>> efficiency = cputime / cores / wallclocktime * 100% >>> ``` >>> >>> In simpler words: how well did the job utilize its CPU cores. It >> shows the percentage of time the job was actually doing stuff, as >> opposed to the difference: >>> ``` >>> wasted = 100% - efficiency >>> ``` >>> >>> ... which, essentially, tells us how much of the resources were >> wasted, i.e. CPU cores just idling, without being used by anyone. We >> care a lot about that because, for our scientific computing cluster, >> wasted resources is like burning money. >>> ### original >>> >>> This is the entire series from our job accounting database, filteres >> the successful jobs, calculates efficiency and then shows the average >> and standard deviation of the efficiency: >>> ``` >>> $ qacct -j 4433299 | qacct-success | qacct-efficiency | meansd >>> n=945 ? 61.7276 ? 7.78719 >>> ``` >>> >>> This is the entire series from our job accounting database, filteres >> the successful jobs, calculates efficiency and does sort of a >> histogram-like binning before calculation of mean and standard >> deviation (to get a more detailed impression of the distribution when >> standard deviation of the previous command is comparatively high): >>> ``` >>> $ qacct -j 4433299 | qacct-success | qacct-efficiency | meansd-bin -w >> 10 | sort -gk1 | column -t >>> 10 - 20 -> n=3 ? 19.21666666666667 ? 0.9112811494447459 >>> 20 - 30 -> n=6 ? 26.418333333333333 ? 2.665996374091058 >>> 30 - 40 -> n=12 ? 35.11583333333334 ? 2.8575783082671196 >>> 40 - 50 -> n=14 ? 45.35285714285715 ? 2.98623361591005 >>> 50 - 60 -> n=344 ? 57.114593023255814 ? 2.1922005551774415 >>> 60 - 70 -> n=453 ? 64.29536423841049 ? 2.8334788433963856 >>> 70 - 80 -> n=108 ? 72.95592592592598 ? 2.5219474143639276 >>> 80 - 90 -> n=5 ? 81.526 ? 1.2802265424525452 >>> ``` >>> >>> I have attached an example graph from our monitoring system of a >> single instance in my previous mail. There you can see that the load >> balancing does not actually work, i.e. same as `parLapply`. This >> reflects in the job efficiency. >>> ### patch applied >>> >>> This is the single instance I used to confirm that the patch works: >>> >>> ``` >>> $ qacct -j 4562202 | qacct-efficiency >>> 97.36 >>> ``` >>> >>> The graph from our monitoring system is attached. As you can see, the >> load balancing works to a satisfying degree and the efficiency is well >> above 90% which was what I had hoped for :-) >>> ## Additional Notes >>> >>> The list used in this jobs `parLapplyLB` is 5812 elements long. With >> the `splitList`-chunking from the patch, you'll get 208 lists of about >> 28 elements (208 chunks of size 28). The job ran on 28 CPU cores and >> had a wallclock time of 120351.590 seconds, i.e. 33.43 hours. Thus, the >> function we apply to our list takes about 580 seconds per list element, >> i.e. about 10 minutes. I suppose, for that runtime, we would get even >> better load balancing if we would reduce the chunk size even further, >> maybe even down to 1, thus getting our efficiency even closer to 100%. >>> Of course, for really short-running functions, a higher chunk size >> may be more efficient because of the overhead. In our case, the >> overhead is negligible and that is why the low chunk size works really >> well. In contrast, for smallish lists with short-running functions, you >> might not even need load balancing and `parLapply` suffices. It only >> becomes an issue, when the runtime of the function is high and / or >> varying. >>> In our case, the entire runtime of the entire series of jobs was: >>> >>> ``` >>> $ qacct -j 4433299 | awk '$1 == "wallclock" { sum += $2 } END { print >> sum, "seconds" }' >>> 4.72439e+09 seconds >>> ``` >>> >>> Thats about 150 years on a single core or 7.5 years on a 20 core >> server! Our user was constantly using about 500 cores, so this took >> about 110 days. If you compare this to my 97% efficiency example, the >> jobs could have been finished in 75 days instead ;-) >>> ## Upcoming Patch >>> >>> If this patch gets applied to the R code base (and I hope it will >> :-)) my colleague and I will submit another patch that adds the chunk >> size as an optional parameter to all off the load balancing functions. >> With that parameter, users of these functions *can* decide for >> themselves which chunk size they prefer for their code. As mentioned >> before, the most efficient chunk size depends on the used functions >> runtime, which is the only thing R does not know and users really >> should be allowed to specify explicitly. The default of this new >> optional parameter would be the one we used here and this would make >> that upcoming patch fully source-compatible. >>> Best Regards >>> >>> On 02/12/2018 08:08 PM, Christian Krause wrote: >>>> Dear R-Devel List, >>>> >>>> **TL;DR:** The function **parLapplyLB** of the parallel package has >> [reportedly][1] (see also attached RRD output) not >>>> been doing its job, i.e. not actually balancing the load. My >> colleague Dirk Sarpe and I found the cause of the problem >>>> and we also have a patch to fix it (attached). A similar fix has >> also been provided [here][2]. >>>> [1]: >> https://stackoverflow.com/questions/38230831/why-does-parlapplylb-not-actually-balance-load >>>> [2]: https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16792 >>>> >>>> >>>> ## The Call Chain >>>> >>>> First, we traced the relevant R function calls through the code, >> beginning with `parLapplyLB`: >>>> 1. **parLapplyLB:** clusterApply.R:177, calls **splitList**, then >> **clusterApplyLB** >>>> 2. **splitList:** clusterApply.R:157 >>>> 3. **clusterApplyLB:** clusterApply.R:87, calls >> **dynamicClusterApply** >>>> 4. **dynamicClusterApply:** clusterApply.R:39 >>>> >>>> >>>> ## splitList >>>> >>>> We used both our whiteboard and an R session to manually *run* a few >> examples. We were using lists of 100 elements and 5 >>>> workers. First, lets take a look at **splitList**: >>>> >>>> ```r >>>>> sapply(parallel:::splitList(1:100, 5), length) >>>> [1] 20 20 20 20 20 >>>> >>>>> sapply(parallel:::splitList(1:97, 5), length) >>>> [1] 20 19 19 19 20 >>>> >>>>> sapply(parallel:::splitList(1:97, 20), length) >>>> [1] 5 5 5 5 4 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 >>>> ``` >>>> >>>> As we can see in the examples, the work is distributed as equally as >> possible. >>>> >>>> ## dynamicClusterApply >>>> >>>> **dynamicClusterApply** works this way (simplified): >>>> >>>> 1. it first gives a chunk to each worker >>>> 2. once a worker comes back with the result, it is given the next >> chunk >>>> **This is the important part:** As long as there are **more** chunks >> than workers, there will be load balancing. If >>>> there 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 we have it: 5 chunks. 5 workers. With this work distribution, >> there can't possibly be any load balancing, because >>>> each worker is given a single chunk and then it stops working >> because there are no more chunks. >>>> Instead, **parLapplyLB** should look like this (patch is attached): >>>> >>>> ```r >>>> parLapplyLB <- function(cl = NULL, X, fun, ...) >>>> { >>>> cl <- defaultCluster(cl) >>>> >>>> chunkSize <- max(length(cl), ceiling(length(X) / length(cl))) >>>> >>>> chunks <- splitList(X, chunkSize) >>>> >>>> do.call(c, >>>> clusterApplyLB(cl, x = chunks, fun = lapply, fun, ...), >>>> quote = TRUE) >>>> } >>>> ``` >>>> >>>> Examples with a cluster of 5 workers: >>>> >>>> ```r >>>> # length(cl) < length(X) >>>>> sapply(parallel:::splitList(1:100, ceiling(100 / 5)), length) >>>> [1] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 >>>> >>>> # length(cl) >= length(X) >>>>> sapply(parallel:::splitList(1:4, 4), length) >>>> [1] 1 1 1 1 >>>> # one worker idles here, but we can't do better than that >>>> ``` >>>> >>>> With this patch, the number of chunks is larger than the number of >> workers, if possible at all, and then load balancing >>>> should work. >>>> >>>> Best Regards >>>> >>>> >>>> >>>> ______________________________________________ >>>> R-devel at r-project.org mailing list >>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>> >>> -- >>> Christian Krause >>> >>> Scientific Computing Administration and Support >>> >>> >> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ >>> Email: christian.krause at idiv.de >>> >>> Office: BioCity Leipzig 5e, Room 3.201.3 >>> >>> Phone: +49 341 97 33144 >>> >>> >> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ >>> German Centre for Integrative Biodiversity Research (iDiv) >> Halle-Jena-Leipzig >>> Deutscher Platz 5e >>> >>> 04103 Leipzig >>> >>> Germany >>> >>> >> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ >>> iDiv is a research centre of the DFG ? Deutsche >> Forschungsgemeinschaft >>> iDiv ist eine zentrale Einrichtung der Universit?t Leipzig im Sinne >> des ? 92 Abs. 1 S?chsHSFG und wird zusammen mit der >> Martin-Luther-Universit?t Halle-Wittenberg und der >> Friedrich-Schiller-Universit?t Jena betrieben sowie in Kooperation mit >> dem Helmholtz-Zentrum f?r Umweltforschung GmbH ? UFZ. Beteiligte >> Kooperationspartner sind die folgenden au?eruniversit?ren >> Forschungseinrichtungen: das Helmholtz-Zentrum f?r Umweltforschung GmbH >> - UFZ, das Max-Planck-Institut f?r Biogeochemie (MPI BGC), das >> Max-Planck-Institut f?r chemische ?kologie (MPI CE), das >> Max-Planck-Institut f?r evolution?re Anthropologie (MPI EVA), das >> Leibniz-Institut Deutsche Sammlung von Mikroorganismen und Zellkulturen >> (DSMZ), das Leibniz-Institut f?r Pflanzenbiochemie (IPB), das >> Leibniz-Institut f?r Pflanzengenetik und Kulturpflanzenforschung (IPK) >> und das Leibniz-Institut Senckenberg Museum f?r Naturkunde G?rlitz >> (SMNG). USt-IdNr. DE 141510383 >>> >>> ______________________________________________ >>> R-devel at r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>
Christian Krause
2018-Mar-01 08:19 UTC
[Rd] [parallel] fixes load balancing of parLapplyLB
Dear Tomas, Thanks for your commitment to fix this issue and also to add the chunk size as an argument. If you want our input, let us know ;) Best Regards On 02/26/2018 04:01 PM, Tomas Kalibera wrote:> Dear Christian and Henrik, > > thank you for spotting the problem and suggestions for a fix. We'll probably add a chunk.size argument to parLapplyLB and parLapply to follow OpenMP terminology, which has already been an inspiration for the present code (parLapply already implements static scheduling via internal function staticClusterApply, yet with a fixed chunk size; parLapplyLB already implements dynamic scheduling via internal function dynamicClusterApply, but with a fixed chunk size set to an unlucky value so that it behaves like static scheduling). The default chunk size for parallelLapplyLB will be set so that there is some dynamism in the schedule even by default. I am now testing a patch with these changes. > > Best > Tomas > > > On 02/20/2018 11:45 AM, Christian Krause wrote: >> Dear Henrik, >> >> The rationale is just that it is within these extremes and that it is really simple to calculate, without making any assumptions and knowing that it won't be perfect. >> >> The extremes A and B you are mentioning are special cases based on assumptions. Case A is based on the assumption that the function has a long runtime or varying runtime, then you are likely to get the best load balancing with really small chunks. Case B is based on the assumption that the function runtime is the same for each list element, i.e. where you don't actually need load balancing, i.e. just use `parLapply` without load balancing. >> >> This new default is **not the best one**. It's just a better one than we had before. There is no best one we can use as default because **we don't know the function runtime and how it varies**. The user needs to decide that because he/she knows the function. As mentioned before, I will write a patch that makes the chunk size an optional argument, so the user can decide because only he/she has all the information to choose the best chunk size, just like you did with the `future.scheduling` parameter. >> >> Best Regards >> >> On February 19, 2018 10:11:04 PM GMT+01:00, Henrik Bengtsson <henrik.bengtsson at gmail.com> wrote: >>> Hi, I'm trying to understand the rationale for your proposed amount of >>> splitting and more precisely why that one is THE one. >>> >>> If I put labels on your example numbers in one of your previous post: >>> >>> nbrOfElements <- 97 >>> nbrOfWorkers <- 5 >>> >>> With these, there are two extremes in how you can split up the >>> processing in chunks such that all workers are utilized: >>> >>> (A) Each worker, called multiple times, processes one element each >>> time: >>> >>>> nbrOfElements <- 97 >>>> nbrOfWorkers <- 5 >>>> nbrOfChunks <- nbrOfElements >>>> sapply(parallel:::splitList(1:nbrOfElements, nbrOfChunks), length) >>> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >>> [30] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >>> [59] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >>> [88] 1 1 1 1 1 1 1 1 1 1 >>> >>> >>> (B) Each worker, called once, processes multiple element: >>> >>>> nbrOfElements <- 97 >>>> nbrOfWorkers <- 5 >>>> nbrOfChunks <- nbrOfWorkers >>>> sapply(parallel:::splitList(1:nbrOfElements, nbrOfChunks), length) >>> [1] 20 19 19 19 20 >>> >>> I understand that neither of these two extremes may be the best when >>> it comes to orchestration overhead and load balancing. Instead, the >>> best might be somewhere in-between, e.g. >>> >>> (C) Each worker, called multiple times, processing multiple elements: >>> >>>> nbrOfElements <- 97 >>>> nbrOfWorkers <- 5 >>>> nbrOfChunks <- nbrOfElements / nbrOfWorkers >>>> sapply(parallel:::splitList(1:nbrOfElements, nbrOfChunks), length) >>> [1] 5 5 5 5 4 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 >>> >>> However, there are multiple alternatives between the two extremes, e.g. >>> >>>> nbrOfChunks <- scale * nbrOfElements / nbrOfWorkers >>> So, is there a reason why you argue for scale = 1.0 to be the optimal? >>> >>> FYI, In future.apply::future_lapply(X, FUN, ...) there is a >>> 'future.scheduling' scale factor(*) argument where default >>> future.scheduling = 1 corresponds to (B) and future.scheduling = +Inf >>> to (A).? Using future.scheduling = 4 achieves the amount of >>> load-balancing you propose in (C).?? (*) Different definition from the >>> above 'scale'. (Disclaimer: I'm the author) >>> >>> /Henrik >>> >>> On Mon, Feb 19, 2018 at 10:21 AM, Christian Krause >>> <christian.krause at idiv.de> wrote: >>>> Dear R-Devel List, >>>> >>>> I have installed R 3.4.3 with the patch applied on our cluster and >>> ran a *real-world* job of one of our users to confirm that the patch >>> works to my satisfaction. Here are the results. >>>> The original was a series of jobs, all essentially doing the same >>> stuff using bootstrapped data, so for the original there is more data >>> and I show the arithmetic mean with standard deviation. The >>> confirmation with the patched R was only a single instance of that >>> series of jobs. >>>> ## Job Efficiency >>>> >>>> The job efficiency is defined as (this is what the `qacct-efficiency` >>> tool below does): >>>> ``` >>>> efficiency = cputime / cores / wallclocktime * 100% >>>> ``` >>>> >>>> In simpler words: how well did the job utilize its CPU cores. It >>> shows the percentage of time the job was actually doing stuff, as >>> opposed to the difference: >>>> ``` >>>> wasted = 100% - efficiency >>>> ``` >>>> >>>> ... which, essentially, tells us how much of the resources were >>> wasted, i.e. CPU cores just idling, without being used by anyone. We >>> care a lot about that because, for our scientific computing cluster, >>> wasted resources is like burning money. >>>> ### original >>>> >>>> This is the entire series from our job accounting database, filteres >>> the successful jobs, calculates efficiency and then shows the average >>> and standard deviation of the efficiency: >>>> ``` >>>> $ qacct -j 4433299 | qacct-success | qacct-efficiency | meansd >>>> n=945 ? 61.7276 ? 7.78719 >>>> ``` >>>> >>>> This is the entire series from our job accounting database, filteres >>> the successful jobs, calculates efficiency and does sort of a >>> histogram-like binning before calculation of mean and standard >>> deviation (to get a more detailed impression of the distribution when >>> standard deviation of the previous command is comparatively high): >>>> ``` >>>> $ qacct -j 4433299 | qacct-success | qacct-efficiency | meansd-bin -w >>> 10 | sort -gk1 | column -t >>>> 10? -? 20? ->? n=3??? ?? 19.21666666666667?? ?? 0.9112811494447459 >>>> 20? -? 30? ->? n=6??? ?? 26.418333333333333? ?? 2.665996374091058 >>>> 30? -? 40? ->? n=12?? ?? 35.11583333333334?? ?? 2.8575783082671196 >>>> 40? -? 50? ->? n=14?? ?? 45.35285714285715?? ?? 2.98623361591005 >>>> 50? -? 60? ->? n=344? ?? 57.114593023255814? ?? 2.1922005551774415 >>>> 60? -? 70? ->? n=453? ?? 64.29536423841049?? ?? 2.8334788433963856 >>>> 70? -? 80? ->? n=108? ?? 72.95592592592598?? ?? 2.5219474143639276 >>>> 80? -? 90? ->? n=5??? ?? 81.526????????????? ?? 1.2802265424525452 >>>> ``` >>>> >>>> I have attached an example graph from our monitoring system of a >>> single instance in my previous mail. There you can see that the load >>> balancing does not actually work, i.e. same as `parLapply`. This >>> reflects in the job efficiency. >>>> ### patch applied >>>> >>>> This is the single instance I used to confirm that the patch works: >>>> >>>> ``` >>>> $ qacct -j 4562202 | qacct-efficiency >>>> 97.36 >>>> ``` >>>> >>>> The graph from our monitoring system is attached. As you can see, the >>> load balancing works to a satisfying degree and the efficiency is well >>> above 90% which was what I had hoped for :-) >>>> ## Additional Notes >>>> >>>> The list used in this jobs `parLapplyLB` is 5812 elements long. With >>> the `splitList`-chunking from the patch, you'll get 208 lists of about >>> 28 elements (208 chunks of size 28). The job ran on 28 CPU cores and >>> had a wallclock time of 120351.590 seconds, i.e. 33.43 hours. Thus, the >>> function we apply to our list takes about 580 seconds per list element, >>> i.e. about 10 minutes. I suppose, for that runtime, we would get even >>> better load balancing if we would reduce the chunk size even further, >>> maybe even down to 1, thus getting our efficiency even closer to 100%. >>>> Of course, for really short-running functions, a higher chunk size >>> may be more efficient because of the overhead. In our case, the >>> overhead is negligible and that is why the low chunk size works really >>> well. In contrast, for smallish lists with short-running functions, you >>> might not even need load balancing and `parLapply` suffices. It only >>> becomes an issue, when the runtime of the function is high and / or >>> varying. >>>> In our case, the entire runtime of the entire series of jobs was: >>>> >>>> ``` >>>> $ qacct -j 4433299 | awk '$1 == "wallclock" { sum += $2 } END { print >>> sum, "seconds" }' >>>> 4.72439e+09 seconds >>>> ``` >>>> >>>> Thats about 150 years on a single core or 7.5 years on a 20 core >>> server! Our user was constantly using about 500 cores, so this took >>> about 110 days. If you compare this to my 97% efficiency example, the >>> jobs could have been finished in 75 days instead ;-) >>>> ## Upcoming Patch >>>> >>>> If this patch gets applied to the R code base (and I hope it will >>> :-)) my colleague and I will submit another patch that adds the chunk >>> size as an optional parameter to all off the load balancing functions. >>> With that parameter, users of these functions *can* decide for >>> themselves which chunk size they prefer for their code. As mentioned >>> before, the most efficient chunk size depends on the used functions >>> runtime, which is the only thing R does not know and users really >>> should be allowed to specify explicitly. The default of this new >>> optional parameter would be the one we used here and this would make >>> that upcoming patch fully source-compatible. >>>> Best Regards >>>> >>>> On 02/12/2018 08:08 PM, Christian Krause wrote: >>>>> Dear R-Devel List, >>>>> >>>>> **TL;DR:** The function **parLapplyLB** of the parallel package has >>> [reportedly][1] (see also attached RRD output) not >>>>> been doing its job, i.e. not actually balancing the load. My >>> colleague Dirk Sarpe and I found the cause of the problem >>>>> and we also have a patch to fix it (attached). A similar fix has >>> also been provided [here][2]. >>>>> [1]: >>> https://stackoverflow.com/questions/38230831/why-does-parlapplylb-not-actually-balance-load >>>>> [2]: https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16792 >>>>> >>>>> >>>>> ## The Call Chain >>>>> >>>>> First, we traced the relevant R function calls through the code, >>> beginning with `parLapplyLB`: >>>>> 1.? **parLapplyLB:** clusterApply.R:177, calls **splitList**, then >>> **clusterApplyLB** >>>>> 2.? **splitList:** clusterApply.R:157 >>>>> 3.? **clusterApplyLB:** clusterApply.R:87, calls >>> **dynamicClusterApply** >>>>> 4.? **dynamicClusterApply:** clusterApply.R:39 >>>>> >>>>> >>>>> ## splitList >>>>> >>>>> We used both our whiteboard and an R session to manually *run* a few >>> examples. We were using lists of 100 elements and 5 >>>>> workers. First, lets take a look at **splitList**: >>>>> >>>>> ```r >>>>>> sapply(parallel:::splitList(1:100, 5), length) >>>>> [1] 20 20 20 20 20 >>>>> >>>>>> sapply(parallel:::splitList(1:97, 5), length) >>>>> [1] 20 19 19 19 20 >>>>> >>>>>> sapply(parallel:::splitList(1:97, 20), length) >>>>> ? [1] 5 5 5 5 4 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 >>>>> ``` >>>>> >>>>> As we can see in the examples, the work is distributed as equally as >>> possible. >>>>> >>>>> ## dynamicClusterApply >>>>> >>>>> **dynamicClusterApply** works this way (simplified): >>>>> >>>>> 1.? it first gives a chunk to each worker >>>>> 2.? once a worker comes back with the result, it is given the next >>> chunk >>>>> **This is the important part:** As long as there are **more** chunks >>> than workers, there will be load balancing. If >>>>> there 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 we have it: 5 chunks. 5 workers. With this work distribution, >>> there can't possibly be any load balancing, because >>>>> each worker is given a single chunk and then it stops working >>> because there are no more chunks. >>>>> Instead, **parLapplyLB** should look like this (patch is attached): >>>>> >>>>> ```r >>>>> parLapplyLB <- function(cl = NULL, X, fun, ...) >>>>> { >>>>> ???? cl <- defaultCluster(cl) >>>>> >>>>> ???? chunkSize <- max(length(cl), ceiling(length(X) / length(cl))) >>>>> >>>>> ???? chunks <- splitList(X, chunkSize) >>>>> >>>>> ???? do.call(c, >>>>> ???????????? clusterApplyLB(cl, x = chunks, fun = lapply, fun, ...), >>>>> ???????????? quote = TRUE) >>>>> } >>>>> ``` >>>>> >>>>> Examples with a cluster of 5 workers: >>>>> >>>>> ```r >>>>> # length(cl) < length(X) >>>>>> sapply(parallel:::splitList(1:100, ceiling(100 / 5)), length) >>>>> ? [1] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 >>>>> >>>>> # length(cl) >= length(X) >>>>>> sapply(parallel:::splitList(1:4, 4), length) >>>>> [1] 1 1 1 1 >>>>> # one worker idles here, but we can't do better than that >>>>> ``` >>>>> >>>>> With this patch, the number of chunks is larger than the number of >>> workers, if possible at all, and then load balancing >>>>> should work. >>>>> >>>>> Best Regards >>>>> >>>>> >>>>> >>>>> ______________________________________________ >>>>> R-devel at r-project.org mailing list >>>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>>> >>>> -- >>>> Christian Krause >>>> >>>> Scientific Computing Administration and Support >>>> >>>> >>> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ >>>> Email: christian.krause at idiv.de >>>> >>>> Office: BioCity Leipzig 5e, Room 3.201.3 >>>> >>>> Phone: +49 341 97 33144 >>>> >>>> >>> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ >>>> German Centre for Integrative Biodiversity Research (iDiv) >>> Halle-Jena-Leipzig >>>> Deutscher Platz 5e >>>> >>>> 04103 Leipzig >>>> >>>> Germany >>>> >>>> >>> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ >>>> iDiv is a research centre of the DFG ? Deutsche >>> Forschungsgemeinschaft >>>> iDiv ist eine zentrale Einrichtung der Universit?t Leipzig im Sinne >>> des ? 92 Abs. 1 S?chsHSFG und wird zusammen mit der >>> Martin-Luther-Universit?t Halle-Wittenberg und der >>> Friedrich-Schiller-Universit?t Jena betrieben sowie in Kooperation mit >>> dem Helmholtz-Zentrum f?r Umweltforschung GmbH ? UFZ. Beteiligte >>> Kooperationspartner sind die folgenden au?eruniversit?ren >>> Forschungseinrichtungen: das Helmholtz-Zentrum f?r Umweltforschung GmbH >>> - UFZ, das Max-Planck-Institut f?r Biogeochemie (MPI BGC), das >>> Max-Planck-Institut f?r chemische ?kologie (MPI CE), das >>> Max-Planck-Institut f?r evolution?re Anthropologie (MPI EVA), das >>> Leibniz-Institut Deutsche Sammlung von Mikroorganismen und Zellkulturen >>> (DSMZ), das Leibniz-Institut f?r Pflanzenbiochemie (IPB), das >>> Leibniz-Institut f?r Pflanzengenetik und Kulturpflanzenforschung (IPK) >>> und das Leibniz-Institut Senckenberg Museum f?r Naturkunde G?rlitz >>> (SMNG). USt-IdNr. DE 141510383 >>>> >>>> ______________________________________________ >>>> R-devel at r-project.org mailing list >>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>> > >-- Christian Krause Scientific Computing Administration and Support ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Email: christian.krause at idiv.de Office: BioCity Leipzig 5e, Room 3.201.3 Phone: +49 341 97 33144 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Deutscher Platz 5e 04103 Leipzig Germany ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ iDiv is a research centre of the DFG ? Deutsche Forschungsgemeinschaft iDiv ist eine zentrale Einrichtung der Universit?t Leipzig im Sinne des ? 92 Abs. 1 S?chsHSFG und wird zusammen mit der Martin-Luther-Universit?t Halle-Wittenberg und der Friedrich-Schiller-Universit?t Jena betrieben sowie in Kooperation mit dem Helmholtz-Zentrum f?r Umweltforschung GmbH ? UFZ. Beteiligte Kooperationspartner sind die folgenden au?eruniversit?ren Forschungseinrichtungen: das Helmholtz-Zentrum f?r Umweltforschung GmbH - UFZ, das Max-Planck-Institut f?r Biogeochemie (MPI BGC), das Max-Planck-Institut f?r chemische ?kologie (MPI CE), das Max-Planck-Institut f?r evolution?re Anthropologie (MPI EVA), das Leibniz-Institut Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ), das Leibniz-Institut f?r Pflanzenbiochemie (IPB), das Leibniz-Institut f?r Pflanzengenetik und Kulturpflanzenforschung (IPK) und das Leibniz-Institut Senckenberg Museum f?r Naturkunde G?rlitz (SMNG). USt-IdNr. 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Chunk size support has been added in R-devel 74353. Please let me know if you find any problem. Thanks, Tomas On 03/01/2018 09:19 AM, Christian Krause wrote:> Dear Tomas, > > Thanks for your commitment to fix this issue and also to add the chunk size as an argument. If you want our input, let us know ;) > > Best Regards > > On 02/26/2018 04:01 PM, Tomas Kalibera wrote: >> Dear Christian and Henrik, >> >> thank you for spotting the problem and suggestions for a fix. We'll probably add a chunk.size argument to parLapplyLB and parLapply to follow OpenMP terminology, which has already been an inspiration for the present code (parLapply already implements static scheduling via internal function staticClusterApply, yet with a fixed chunk size; parLapplyLB already implements dynamic scheduling via internal function dynamicClusterApply, but with a fixed chunk size set to an unlucky value so that it behaves like static scheduling). The default chunk size for parallelLapplyLB will be set so that there is some dynamism in the schedule even by default. I am now testing a patch with these changes. >> >> Best >> Tomas >> >> >> On 02/20/2018 11:45 AM, Christian Krause wrote: >>> Dear Henrik, >>> >>> The rationale is just that it is within these extremes and that it is really simple to calculate, without making any assumptions and knowing that it won't be perfect. >>> >>> The extremes A and B you are mentioning are special cases based on assumptions. Case A is based on the assumption that the function has a long runtime or varying runtime, then you are likely to get the best load balancing with really small chunks. Case B is based on the assumption that the function runtime is the same for each list element, i.e. where you don't actually need load balancing, i.e. just use `parLapply` without load balancing. >>> >>> This new default is **not the best one**. It's just a better one than we had before. There is no best one we can use as default because **we don't know the function runtime and how it varies**. The user needs to decide that because he/she knows the function. As mentioned before, I will write a patch that makes the chunk size an optional argument, so the user can decide because only he/she has all the information to choose the best chunk size, just like you did with the `future.scheduling` parameter. >>> >>> Best Regards >>> >>> On February 19, 2018 10:11:04 PM GMT+01:00, Henrik Bengtsson <henrik.bengtsson at gmail.com> wrote: >>>> Hi, I'm trying to understand the rationale for your proposed amount of >>>> splitting and more precisely why that one is THE one. >>>> >>>> If I put labels on your example numbers in one of your previous post: >>>> >>>> nbrOfElements <- 97 >>>> nbrOfWorkers <- 5 >>>> >>>> With these, there are two extremes in how you can split up the >>>> processing in chunks such that all workers are utilized: >>>> >>>> (A) Each worker, called multiple times, processes one element each >>>> time: >>>> >>>>> nbrOfElements <- 97 >>>>> nbrOfWorkers <- 5 >>>>> nbrOfChunks <- nbrOfElements >>>>> sapply(parallel:::splitList(1:nbrOfElements, nbrOfChunks), length) >>>> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >>>> [30] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >>>> [59] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >>>> [88] 1 1 1 1 1 1 1 1 1 1 >>>> >>>> >>>> (B) Each worker, called once, processes multiple element: >>>> >>>>> nbrOfElements <- 97 >>>>> nbrOfWorkers <- 5 >>>>> nbrOfChunks <- nbrOfWorkers >>>>> sapply(parallel:::splitList(1:nbrOfElements, nbrOfChunks), length) >>>> [1] 20 19 19 19 20 >>>> >>>> I understand that neither of these two extremes may be the best when >>>> it comes to orchestration overhead and load balancing. Instead, the >>>> best might be somewhere in-between, e.g. >>>> >>>> (C) Each worker, called multiple times, processing multiple elements: >>>> >>>>> nbrOfElements <- 97 >>>>> nbrOfWorkers <- 5 >>>>> nbrOfChunks <- nbrOfElements / nbrOfWorkers >>>>> sapply(parallel:::splitList(1:nbrOfElements, nbrOfChunks), length) >>>> [1] 5 5 5 5 4 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 >>>> >>>> However, there are multiple alternatives between the two extremes, e.g. >>>> >>>>> nbrOfChunks <- scale * nbrOfElements / nbrOfWorkers >>>> So, is there a reason why you argue for scale = 1.0 to be the optimal? >>>> >>>> FYI, In future.apply::future_lapply(X, FUN, ...) there is a >>>> 'future.scheduling' scale factor(*) argument where default >>>> future.scheduling = 1 corresponds to (B) and future.scheduling = +Inf >>>> to (A).? Using future.scheduling = 4 achieves the amount of >>>> load-balancing you propose in (C).?? (*) Different definition from the >>>> above 'scale'. (Disclaimer: I'm the author) >>>> >>>> /Henrik >>>> >>>> On Mon, Feb 19, 2018 at 10:21 AM, Christian Krause >>>> <christian.krause at idiv.de> wrote: >>>>> Dear R-Devel List, >>>>> >>>>> I have installed R 3.4.3 with the patch applied on our cluster and >>>> ran a *real-world* job of one of our users to confirm that the patch >>>> works to my satisfaction. Here are the results. >>>>> The original was a series of jobs, all essentially doing the same >>>> stuff using bootstrapped data, so for the original there is more data >>>> and I show the arithmetic mean with standard deviation. The >>>> confirmation with the patched R was only a single instance of that >>>> series of jobs. >>>>> ## Job Efficiency >>>>> >>>>> The job efficiency is defined as (this is what the `qacct-efficiency` >>>> tool below does): >>>>> ``` >>>>> efficiency = cputime / cores / wallclocktime * 100% >>>>> ``` >>>>> >>>>> In simpler words: how well did the job utilize its CPU cores. It >>>> shows the percentage of time the job was actually doing stuff, as >>>> opposed to the difference: >>>>> ``` >>>>> wasted = 100% - efficiency >>>>> ``` >>>>> >>>>> ... which, essentially, tells us how much of the resources were >>>> wasted, i.e. CPU cores just idling, without being used by anyone. We >>>> care a lot about that because, for our scientific computing cluster, >>>> wasted resources is like burning money. >>>>> ### original >>>>> >>>>> This is the entire series from our job accounting database, filteres >>>> the successful jobs, calculates efficiency and then shows the average >>>> and standard deviation of the efficiency: >>>>> ``` >>>>> $ qacct -j 4433299 | qacct-success | qacct-efficiency | meansd >>>>> n=945 ? 61.7276 ? 7.78719 >>>>> ``` >>>>> >>>>> This is the entire series from our job accounting database, filteres >>>> the successful jobs, calculates efficiency and does sort of a >>>> histogram-like binning before calculation of mean and standard >>>> deviation (to get a more detailed impression of the distribution when >>>> standard deviation of the previous command is comparatively high): >>>>> ``` >>>>> $ qacct -j 4433299 | qacct-success | qacct-efficiency | meansd-bin -w >>>> 10 | sort -gk1 | column -t >>>>> 10? -? 20? ->? n=3??? ?? 19.21666666666667?? ?? 0.9112811494447459 >>>>> 20? -? 30? ->? n=6??? ?? 26.418333333333333? ?? 2.665996374091058 >>>>> 30? -? 40? ->? n=12?? ?? 35.11583333333334?? ?? 2.8575783082671196 >>>>> 40? -? 50? ->? n=14?? ?? 45.35285714285715?? ?? 2.98623361591005 >>>>> 50? -? 60? ->? n=344? ?? 57.114593023255814? ?? 2.1922005551774415 >>>>> 60? -? 70? ->? n=453? ?? 64.29536423841049?? ?? 2.8334788433963856 >>>>> 70? -? 80? ->? n=108? ?? 72.95592592592598?? ?? 2.5219474143639276 >>>>> 80? -? 90? ->? n=5??? ?? 81.526????????????? ?? 1.2802265424525452 >>>>> ``` >>>>> >>>>> I have attached an example graph from our monitoring system of a >>>> single instance in my previous mail. There you can see that the load >>>> balancing does not actually work, i.e. same as `parLapply`. This >>>> reflects in the job efficiency. >>>>> ### patch applied >>>>> >>>>> This is the single instance I used to confirm that the patch works: >>>>> >>>>> ``` >>>>> $ qacct -j 4562202 | qacct-efficiency >>>>> 97.36 >>>>> ``` >>>>> >>>>> The graph from our monitoring system is attached. As you can see, the >>>> load balancing works to a satisfying degree and the efficiency is well >>>> above 90% which was what I had hoped for :-) >>>>> ## Additional Notes >>>>> >>>>> The list used in this jobs `parLapplyLB` is 5812 elements long. With >>>> the `splitList`-chunking from the patch, you'll get 208 lists of about >>>> 28 elements (208 chunks of size 28). The job ran on 28 CPU cores and >>>> had a wallclock time of 120351.590 seconds, i.e. 33.43 hours. Thus, the >>>> function we apply to our list takes about 580 seconds per list element, >>>> i.e. about 10 minutes. I suppose, for that runtime, we would get even >>>> better load balancing if we would reduce the chunk size even further, >>>> maybe even down to 1, thus getting our efficiency even closer to 100%. >>>>> Of course, for really short-running functions, a higher chunk size >>>> may be more efficient because of the overhead. In our case, the >>>> overhead is negligible and that is why the low chunk size works really >>>> well. In contrast, for smallish lists with short-running functions, you >>>> might not even need load balancing and `parLapply` suffices. It only >>>> becomes an issue, when the runtime of the function is high and / or >>>> varying. >>>>> In our case, the entire runtime of the entire series of jobs was: >>>>> >>>>> ``` >>>>> $ qacct -j 4433299 | awk '$1 == "wallclock" { sum += $2 } END { print >>>> sum, "seconds" }' >>>>> 4.72439e+09 seconds >>>>> ``` >>>>> >>>>> Thats about 150 years on a single core or 7.5 years on a 20 core >>>> server! Our user was constantly using about 500 cores, so this took >>>> about 110 days. If you compare this to my 97% efficiency example, the >>>> jobs could have been finished in 75 days instead ;-) >>>>> ## Upcoming Patch >>>>> >>>>> If this patch gets applied to the R code base (and I hope it will >>>> :-)) my colleague and I will submit another patch that adds the chunk >>>> size as an optional parameter to all off the load balancing functions. >>>> With that parameter, users of these functions *can* decide for >>>> themselves which chunk size they prefer for their code. As mentioned >>>> before, the most efficient chunk size depends on the used functions >>>> runtime, which is the only thing R does not know and users really >>>> should be allowed to specify explicitly. The default of this new >>>> optional parameter would be the one we used here and this would make >>>> that upcoming patch fully source-compatible. >>>>> Best Regards >>>>> >>>>> On 02/12/2018 08:08 PM, Christian Krause wrote: >>>>>> Dear R-Devel List, >>>>>> >>>>>> **TL;DR:** The function **parLapplyLB** of the parallel package has >>>> [reportedly][1] (see also attached RRD output) not >>>>>> been doing its job, i.e. not actually balancing the load. My >>>> colleague Dirk Sarpe and I found the cause of the problem >>>>>> and we also have a patch to fix it (attached). A similar fix has >>>> also been provided [here][2]. >>>>>> [1]: >>>> https://stackoverflow.com/questions/38230831/why-does-parlapplylb-not-actually-balance-load >>>>>> [2]: https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=16792 >>>>>> >>>>>> >>>>>> ## The Call Chain >>>>>> >>>>>> First, we traced the relevant R function calls through the code, >>>> beginning with `parLapplyLB`: >>>>>> 1.? **parLapplyLB:** clusterApply.R:177, calls **splitList**, then >>>> **clusterApplyLB** >>>>>> 2.? **splitList:** clusterApply.R:157 >>>>>> 3.? **clusterApplyLB:** clusterApply.R:87, calls >>>> **dynamicClusterApply** >>>>>> 4.? **dynamicClusterApply:** clusterApply.R:39 >>>>>> >>>>>> >>>>>> ## splitList >>>>>> >>>>>> We used both our whiteboard and an R session to manually *run* a few >>>> examples. We were using lists of 100 elements and 5 >>>>>> workers. First, lets take a look at **splitList**: >>>>>> >>>>>> ```r >>>>>>> sapply(parallel:::splitList(1:100, 5), length) >>>>>> [1] 20 20 20 20 20 >>>>>> >>>>>>> sapply(parallel:::splitList(1:97, 5), length) >>>>>> [1] 20 19 19 19 20 >>>>>> >>>>>>> sapply(parallel:::splitList(1:97, 20), length) >>>>>> ? [1] 5 5 5 5 4 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 >>>>>> ``` >>>>>> >>>>>> As we can see in the examples, the work is distributed as equally as >>>> possible. >>>>>> ## dynamicClusterApply >>>>>> >>>>>> **dynamicClusterApply** works this way (simplified): >>>>>> >>>>>> 1.? it first gives a chunk to each worker >>>>>> 2.? once a worker comes back with the result, it is given the next >>>> chunk >>>>>> **This is the important part:** As long as there are **more** chunks >>>> than workers, there will be load balancing. If >>>>>> there 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 we have it: 5 chunks. 5 workers. With this work distribution, >>>> there can't possibly be any load balancing, because >>>>>> each worker is given a single chunk and then it stops working >>>> because there are no more chunks. >>>>>> Instead, **parLapplyLB** should look like this (patch is attached): >>>>>> >>>>>> ```r >>>>>> parLapplyLB <- function(cl = NULL, X, fun, ...) >>>>>> { >>>>>> ???? cl <- defaultCluster(cl) >>>>>> >>>>>> ???? chunkSize <- max(length(cl), ceiling(length(X) / length(cl))) >>>>>> >>>>>> ???? chunks <- splitList(X, chunkSize) >>>>>> >>>>>> ???? do.call(c, >>>>>> ???????????? clusterApplyLB(cl, x = chunks, fun = lapply, fun, ...), >>>>>> ???????????? quote = TRUE) >>>>>> } >>>>>> ``` >>>>>> >>>>>> Examples with a cluster of 5 workers: >>>>>> >>>>>> ```r >>>>>> # length(cl) < length(X) >>>>>>> sapply(parallel:::splitList(1:100, ceiling(100 / 5)), length) >>>>>> ? [1] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 >>>>>> >>>>>> # length(cl) >= length(X) >>>>>>> sapply(parallel:::splitList(1:4, 4), length) >>>>>> [1] 1 1 1 1 >>>>>> # one worker idles here, but we can't do better than that >>>>>> ``` >>>>>> >>>>>> With this patch, the number of chunks is larger than the number of >>>> workers, if possible at all, and then load balancing >>>>>> should work. >>>>>> >>>>>> Best Regards >>>>>> >>>>>> >>>>>> >>>>>> ______________________________________________ >>>>>> R-devel at r-project.org mailing list >>>>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>>>> >>>>> -- >>>>> Christian Krause >>>>> >>>>> Scientific Computing Administration and Support >>>>> >>>>> >>>> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ >>>>> Email: christian.krause at idiv.de >>>>> >>>>> Office: BioCity Leipzig 5e, Room 3.201.3 >>>>> >>>>> Phone: +49 341 97 33144 >>>>> >>>>> >>>> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ >>>>> German Centre for Integrative Biodiversity Research (iDiv) >>>> Halle-Jena-Leipzig >>>>> Deutscher Platz 5e >>>>> >>>>> 04103 Leipzig >>>>> >>>>> Germany >>>>> >>>>> >>>> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ >>>>> iDiv is a research centre of the DFG ? Deutsche >>>> Forschungsgemeinschaft >>>>> iDiv ist eine zentrale Einrichtung der Universit?t Leipzig im Sinne >>>> des ? 92 Abs. 1 S?chsHSFG und wird zusammen mit der >>>> Martin-Luther-Universit?t Halle-Wittenberg und der >>>> Friedrich-Schiller-Universit?t Jena betrieben sowie in Kooperation mit >>>> dem Helmholtz-Zentrum f?r Umweltforschung GmbH ? UFZ. Beteiligte >>>> Kooperationspartner sind die folgenden au?eruniversit?ren >>>> Forschungseinrichtungen: das Helmholtz-Zentrum f?r Umweltforschung GmbH >>>> - UFZ, das Max-Planck-Institut f?r Biogeochemie (MPI BGC), das >>>> Max-Planck-Institut f?r chemische ?kologie (MPI CE), das >>>> Max-Planck-Institut f?r evolution?re Anthropologie (MPI EVA), das >>>> Leibniz-Institut Deutsche Sammlung von Mikroorganismen und Zellkulturen >>>> (DSMZ), das Leibniz-Institut f?r Pflanzenbiochemie (IPB), das >>>> Leibniz-Institut f?r Pflanzengenetik und Kulturpflanzenforschung (IPK) >>>> und das Leibniz-Institut Senckenberg Museum f?r Naturkunde G?rlitz >>>> (SMNG). USt-IdNr. DE 141510383 >>>>> ______________________________________________ >>>>> R-devel at r-project.org mailing list >>>>> https://stat.ethz.ch/mailman/listinfo/r-devel >>>>> >>