Re: [Bioc-devel] Memory usage for bplapply
From the earlier example, whether the worker sees all the data or not depends on whether it is in the environment of FUN, the object sent to the worker. I don't really know about packages and forked processes. I'd bet that the vector allocations are essentially constant, but that the S-Expressions that point to the symbols do actually get modified, e.g., when the user creates a symbol that references a package symbol (possibly incrementing the NAMED status of the S-expression) or even when the garbage collector comes along and decides that the S-expression in the package should be moved to a different generation. Be sure to understand the difference (maybe you do) between the environment in which the function is defined and the environment in which it is called. Also note that as you restrict the environment in which a function is defined, you restrict the operations that the function perform; the reason a function foo in a package can call another function bar in the same package is because bar is defined in the same environment as foo, and > local(1 + 2, envir = emptyenv()) Error in 1 + 2 : could not find function "+" Usually the bigger problem is that one serializes large data on the manager and sends it to the worker (e.g., reading chunks of a BAM file on the manager and sending each chunk to the worker) rather than arranging to do the heavy IO on the worker (e.g., sending instructions that the worker is supposed to read chromosome 1 from disk). I think if one is worrying about memory at this level, then it's time to get a bigger computer! Martin On 1/6/19, 9:48 PM, "Shian Su" wrote: Can I get a indication here about what is expected to consume memory under fork and socket models as well as patterns to mitigate excessive memory consumption? When using sockets, the model is that of multiple communicating machines running on their own memory, so it makes sense that memory usage is duplicated for loaded packages and the parent environment. But is the while data object duplicated or only the portion of the tasks assigned to a thread? i.e. 4 mb of packages, 4 mb of parent environment, 4 mb of data to run bplapply over, is each thread going to consume 12mb or 9mb of memory? It is unclear to me whether the data object operated on should be thought of as a part of the parent environment. When using forks, the model is that of multiple processes running on shared memory. This is specific to macOS and Unix variants and I believe the model is meant to share memory until a write operation causes variables to be copied. I also believe R’s internal memory management can potentially touch all the variables and cause copies, so the worse case scenario is that everything is copied. What’s unclear is whether this applies to loaded packages, are they under the supervision of a garbage collector? So as per the previous scenario, from the second thread onwards, do we expect up to (0 + 4 + 1)mb, (4 + 4 + 1)mb or (4 + 4 + 4)mb of memory usage? Maybe even the ideal scenario of (0 + 0 + 1)? With regards to patterns to efficiently use memory, is it sufficient to keep the parent environment as compact as possible? Are there clever ways to use local() for this? Kind regards, Shian On 6 Jan 2019, at 9:24 am, Martin Morgan wrote: In one R session I did library(SummarizedExperiment) and then saved search(). In another R session I loaded the packages on the search path in reverse order, recording pryr::mem_used() after each. I ended up with mem_used methods 25870312 datasets 30062016 utils 30062136 grDevices 30062256 graphics 30062376 stats 30062496 stats4 32262992 parallel 32495080 BiocGenerics 38903928 S4Vectors 59586928 IRanges 100171896 GenomeInfoDb 113791328 GenomicRanges 154729400 Biobase 163335336 matrixStats 163518520 BiocParallel 167373512 DelayedArray 280812736 SummarizedExperiment 317386656 Each of the Bioconductor dependencies of SummarizedExperiment contribute to the overall size. Two dependencies (Biobase, DelayedArray) look a little unnecessary to me (they do not provide functionality that must be used by SummarizedExperiment) but removing them only reduces the total footprint to about 300MB. Somehow it makes sense that a package like SummarizedExperiment uses the data structures defined in other packages, and that it has a complex dependency graph. It is surprising how large the final footprint is. One possible way
Re: [Bioc-devel] Memory usage for bplapply
Can I get a indication here about what is expected to consume memory under fork and socket models as well as patterns to mitigate excessive memory consumption? When using sockets, the model is that of multiple communicating machines running on their own memory, so it makes sense that memory usage is duplicated for loaded packages and the parent environment. But is the while data object duplicated or only the portion of the tasks assigned to a thread? i.e. 4 mb of packages, 4 mb of parent environment, 4 mb of data to run bplapply over, is each thread going to consume 12mb or 9mb of memory? It is unclear to me whether the data object operated on should be thought of as a part of the parent environment. When using forks, the model is that of multiple processes running on shared memory. This is specific to macOS and Unix variants and I believe the model is meant to share memory until a write operation causes variables to be copied. I also believe R’s internal memory management can potentially touch all the variables and cause copies, so the worse case scenario is that everything is copied. What’s unclear is whether this applies to loaded packages, are they under the supervision of a garbage collector? So as per the previous scenario, from the second thread onwards, do we expect up to (0 + 4 + 1)mb, (4 + 4 + 1)mb or (4 + 4 + 4)mb of memory usage? Maybe even the ideal scenario of (0 + 0 + 1)? With regards to patterns to efficiently use memory, is it sufficient to keep the parent environment as compact as possible? Are there clever ways to use local() for this? Kind regards, Shian On 6 Jan 2019, at 9:24 am, Martin Morgan mailto:mtmorgan.b...@gmail.com>> wrote: In one R session I did library(SummarizedExperiment) and then saved search(). In another R session I loaded the packages on the search path in reverse order, recording pryr::mem_used() after each. I ended up with mem_used methods 25870312 datasets 30062016 utils 30062136 grDevices 30062256 graphics 30062376 stats 30062496 stats432262992 parallel 32495080 BiocGenerics 38903928 S4Vectors 59586928 IRanges 100171896 GenomeInfoDb 113791328 GenomicRanges154729400 Biobase 163335336 matrixStats 163518520 BiocParallel 167373512 DelayedArray 280812736 SummarizedExperiment 317386656 Each of the Bioconductor dependencies of SummarizedExperiment contribute to the overall size. Two dependencies (Biobase, DelayedArray) look a little unnecessary to me (they do not provide functionality that must be used by SummarizedExperiment) but removing them only reduces the total footprint to about 300MB. Somehow it makes sense that a package like SummarizedExperiment uses the data structures defined in other packages, and that it has a complex dependency graph. It is surprising how large the final footprint is. One possible way to avoid at least some of the cost is to Import: SummarizedExperiment in the DESCRIPTION file, but not mention SummarizedExperiment in the NAMESPACE. Use SummarizedExperiment::assay() in the code. I think this has complicated side effects, e.g., adding methods to the imported methods table in your package (look for ".__T__" and ".__C__" (generic and class definitions) in ls(parent.env(getNamespace(, that indirectly increase the size of your package. I'm not exactly sure what you mean in your second paragraph, maybe a specific example (if necessary, create a small package on github) would help. It sounds like you're saying that even with doSNOW() there are additional costs to loading your package on the worker compared to in the master... Martin On 1/5/19, 2:44 PM, "Lulu Chen" mailto:luluc...@vt.edu>> wrote: Hi Martin, Thanks for your explanation which make me understand BiocParallel much better. I compare memory usage in my code before packaged (using doSNOW) and after packaged (using BiocParallel) and find the increased memory is caused by the attached packages, especially 'SummarizedExperiment'. As required to support common Bioconductor class, I used importFrom(SummarizedExperiment,assay). After deleting this, the memory for each thread save nearly 200Mb. I open a new R session and find pryr::mem_used() 38.5 MB library(SummarizedExperiment) pryr::mem_used() 314 MB (I am still using R 3.5.2, not sure any update in develop version). I think it should be a issue. A lot of packages are importing SummarizedExperiment just for a support and never know it can cause such a problem. My package still imports other packages, e.g limma, fdrtool. Checked by pryr::mem_used() as above, only 1~2 Mb increase for each. I also check my_package in a new session, which is around 5Mb. However, each thread in parallel computation still increases much larger
Re: [Bioc-devel] Memory usage for bplapply
In one R session I did library(SummarizedExperiment) and then saved search().
In another R session I loaded the packages on the search path in reverse order,
recording pryr::mem_used() after each. I ended up with
mem_used
methods 25870312
datasets 30062016
utils 30062136
grDevices 30062256
graphics 30062376
stats 30062496
stats432262992
parallel 32495080
BiocGenerics 38903928
S4Vectors 59586928
IRanges 100171896
GenomeInfoDb 113791328
GenomicRanges154729400
Biobase 163335336
matrixStats 163518520
BiocParallel 167373512
DelayedArray 280812736
SummarizedExperiment 317386656
Each of the Bioconductor dependencies of SummarizedExperiment contribute to the
overall size. Two dependencies (Biobase, DelayedArray) look a little
unnecessary to me (they do not provide functionality that must be used by
SummarizedExperiment) but removing them only reduces the total footprint to
about 300MB. Somehow it makes sense that a package like SummarizedExperiment
uses the data structures defined in other packages, and that it has a complex
dependency graph. It is surprising how large the final footprint is.
One possible way to avoid at least some of the cost is to Import:
SummarizedExperiment in the DESCRIPTION file, but not mention
SummarizedExperiment in the NAMESPACE. Use SummarizedExperiment::assay() in the
code. I think this has complicated side effects, e.g., adding methods to the
imported methods table in your package (look for ".__T__" and ".__C__" (generic
and class definitions) in ls(parent.env(getNamespace(, that
indirectly increase the size of your package.
I'm not exactly sure what you mean in your second paragraph, maybe a specific
example (if necessary, create a small package on github) would help. It sounds
like you're saying that even with doSNOW() there are additional costs to
loading your package on the worker compared to in the master...
Martin
On 1/5/19, 2:44 PM, "Lulu Chen" wrote:
Hi Martin,
Thanks for your explanation which make me understand BiocParallel much
better.
I compare memory usage in my code before packaged (using doSNOW) and after
packaged (using BiocParallel) and find the increased memory is caused by the
attached packages, especially 'SummarizedExperiment'.
As required to support common Bioconductor class, I used
importFrom(SummarizedExperiment,assay). After deleting this, the memory for
each thread save nearly 200Mb. I open a new R session and find
> pryr::mem_used()
38.5 MB
> library(SummarizedExperiment)
> pryr::mem_used()
314 MB
(I am still using R 3.5.2, not sure any update in develop version). I
think it should be a issue. A lot of packages are importing
SummarizedExperiment just for a support and never know it can cause such a
problem.
My package still imports other packages, e.g limma, fdrtool. Checked by
pryr::mem_used() as above, only 1~2 Mb increase for each. I also check
my_package in a new session, which is around 5Mb. However, each thread in
parallel computation still increases
much larger than 5 Mb. I did a simulation: In my old code with doSNOW, I
just inserted "require('my_package')" into foreach loop and keep other code as
the same. I used 20 cores and 1000 jobs. Each thread still increases 20~30 Mb.
I don't know if there are
any other thing that cause extra cost to each thread. Thanks!
Best,
Lulu
On Fri, Jan 4, 2019 at 2:38 PM Martin Morgan
wrote:
Memory use can be complicated to understand.
library(BiocParallel)
v <- replicate(100, rnorm(1), simplify=FALSE)
bplapply(v, sum)
by default, bplapply splits 100 jobs (each element of the list) equally
between the number of cores available, and sends just the necessary data to the
cores. Again by default, the jobs are sent 'en masse' to the cores, so if there
were 10 cores (and hence
10 tasks), the first core would receive the first 10 jobs and 10 x 1
elements, and so on. The memory used to store v on the workers would be
approximately the size of v, # of workers * jobs /per worker * job size = 10 *
10 * 1.
If memory were particularly tight, or if computation time for each job was
highly variable, it might be advantageous to sends jobs one at a time, by
setting the number of tasks equal to the number of jobs SnowParam(workers = 10,
tasks = length(v)). Then the
amount of memory used to store v would only be # of workers * 1 * 1;
this is generally slower, because there is much more communication between the
manager and the workers.
m <- matrix(rnorm(100 * 1), 100, 1)
Re: [Bioc-devel] Memory usage for bplapply
Hi Martin,
Thanks for your explanation which make me understand BiocParallel
much better.
I compare memory usage in my code before packaged (using doSNOW) and after
packaged (using BiocParallel) and find the increased memory is caused by
the attached packages, especially 'SummarizedExperiment'.
As required to support common Bioconductor class, I used
importFrom(SummarizedExperiment,assay). After deleting this, the memory for
each thread save nearly 200Mb. I open a new R session and find
> pryr::mem_used()
38.5 MB
> library(SummarizedExperiment)
> pryr::mem_used()
314 MB
(I am still using R 3.5.2, not sure any update in develop version). I
think it should be a issue. A lot of packages are importing
SummarizedExperiment just for a support and never know it can cause such a
problem.
My package still imports other packages, e.g limma, fdrtool. Checked by
pryr::mem_used() as above, only 1~2 Mb increase for each. I also check
my_package in a new session, which is around 5Mb. However, each thread in
parallel computation still increases much larger than 5 Mb. I did a
simulation: In my old code with doSNOW, I just inserted
"require('my_package')" into foreach loop and keep other code as the same.
I used 20 cores and 1000 jobs. Each thread still increases 20~30 Mb. I
don't know if there are any other thing that cause extra cost to each
thread. Thanks!
Best,
Lulu
On Fri, Jan 4, 2019 at 2:38 PM Martin Morgan
wrote:
> Memory use can be complicated to understand.
>
> library(BiocParallel)
>
> v <- replicate(100, rnorm(1), simplify=FALSE)
> bplapply(v, sum)
>
> by default, bplapply splits 100 jobs (each element of the list) equally
> between the number of cores available, and sends just the necessary data to
> the cores. Again by default, the jobs are sent 'en masse' to the cores, so
> if there were 10 cores (and hence 10 tasks), the first core would receive
> the first 10 jobs and 10 x 1 elements, and so on. The memory used to
> store v on the workers would be approximately the size of v, # of workers *
> jobs /per worker * job size = 10 * 10 * 1.
>
> If memory were particularly tight, or if computation time for each job was
> highly variable, it might be advantageous to sends jobs one at a time, by
> setting the number of tasks equal to the number of jobs SnowParam(workers =
> 10, tasks = length(v)). Then the amount of memory used to store v would
> only be # of workers * 1 * 1; this is generally slower, because there
> is much more communication between the manager and the workers.
>
> m <- matrix(rnorm(100 * 1), 100, 1)
> bplapply(seq_len(nrow(m)), function(i, m) sum(m[i]), m)
>
> Here bplapply doesn't know how to send just some rows to the workers, so
> each worker gets a complete copy of m. This would be expensive.
>
> f <- function(x) sum(x)
>
> g <- function() {
> v <- replicate(100, rnorm(1), simplify=FALSE)
> bplapply(v, f)
> }
>
> this has the same memory consequences as above, the function `f()` is
> defined in the .GlobalEnv, so only the function definition (small) is sent
> to the workers.
>
> h <- function() {
> f <- function(x) sum(x)
> v <- replicate(100, rnorm(1), simplify=FALSE)
> bplapply(v, f)
> }
>
> This is expensive. The function `f()` is defined in the body of the
> function `h()`. So the workers receive both the function f and the
> environment in which it defined. The environment includes v, so each worker
> receives a slice of v (for f() to operate on) AND an entire copy of v
> (because it is in the body of the environment where `f()` was defined. A
> similar cost would be paid in a package, if the package defined large data
> objects at load time.
>
> For more guidance, it might be helpful to provide a simplified example of
> what you did with doSNOW, and what you do with BiocParallel.
>
> Hope that helps,
>
> Martin
>
> On 1/3/19, 11:52 PM, "Bioc-devel on behalf of Lulu Chen" <
> bioc-devel-boun...@r-project.org on behalf of luluc...@vt.edu> wrote:
>
> Dear all,
>
> I met a memory issue for bplapply with SnowParam(). I need to calculate
> something from a large matrix many many times. But from the
> discussions in
> https://support.bioconductor.org/p/92587, I learned that bplapply
> copied
> the current and parent environment to each worker thread. Then means
> the
> large matrix in my package will be copied so many times. Do you have
> better
> suggestions in windows platform?
>
> Before I tried to package my code, I used doSNOW package with foreach
> %dopar%. It seems to consume less memory in each core (almost the size
> of
> the matrix the task needs). But bplapply seems to copy more then
> objects in
> current environment and the above one level environment. I am very
> confused.and just guess it was copying everything.
>
> Thanks for any help!
> Best,
> Lulu
>
> [[alternative HTML version
Re: [Bioc-devel] Memory usage for bplapply
Memory use can be complicated to understand.
library(BiocParallel)
v <- replicate(100, rnorm(1), simplify=FALSE)
bplapply(v, sum)
by default, bplapply splits 100 jobs (each element of the list) equally between
the number of cores available, and sends just the necessary data to the cores.
Again by default, the jobs are sent 'en masse' to the cores, so if there were
10 cores (and hence 10 tasks), the first core would receive the first 10 jobs
and 10 x 1 elements, and so on. The memory used to store v on the workers
would be approximately the size of v, # of workers * jobs /per worker * job
size = 10 * 10 * 1.
If memory were particularly tight, or if computation time for each job was
highly variable, it might be advantageous to sends jobs one at a time, by
setting the number of tasks equal to the number of jobs SnowParam(workers = 10,
tasks = length(v)). Then the amount of memory used to store v would only be #
of workers * 1 * 1; this is generally slower, because there is much more
communication between the manager and the workers.
m <- matrix(rnorm(100 * 1), 100, 1)
bplapply(seq_len(nrow(m)), function(i, m) sum(m[i]), m)
Here bplapply doesn't know how to send just some rows to the workers, so each
worker gets a complete copy of m. This would be expensive.
f <- function(x) sum(x)
g <- function() {
v <- replicate(100, rnorm(1), simplify=FALSE)
bplapply(v, f)
}
this has the same memory consequences as above, the function `f()` is defined
in the .GlobalEnv, so only the function definition (small) is sent to the
workers.
h <- function() {
f <- function(x) sum(x)
v <- replicate(100, rnorm(1), simplify=FALSE)
bplapply(v, f)
}
This is expensive. The function `f()` is defined in the body of the function
`h()`. So the workers receive both the function f and the environment in which
it defined. The environment includes v, so each worker receives a slice of v
(for f() to operate on) AND an entire copy of v (because it is in the body of
the environment where `f()` was defined. A similar cost would be paid in a
package, if the package defined large data objects at load time.
For more guidance, it might be helpful to provide a simplified example of what
you did with doSNOW, and what you do with BiocParallel.
Hope that helps,
Martin
On 1/3/19, 11:52 PM, "Bioc-devel on behalf of Lulu Chen"
wrote:
Dear all,
I met a memory issue for bplapply with SnowParam(). I need to calculate
something from a large matrix many many times. But from the discussions in
https://support.bioconductor.org/p/92587, I learned that bplapply copied
the current and parent environment to each worker thread. Then means the
large matrix in my package will be copied so many times. Do you have better
suggestions in windows platform?
Before I tried to package my code, I used doSNOW package with foreach
%dopar%. It seems to consume less memory in each core (almost the size of
the matrix the task needs). But bplapply seems to copy more then objects in
current environment and the above one level environment. I am very
confused.and just guess it was copying everything.
Thanks for any help!
Best,
Lulu
[[alternative HTML version deleted]]
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