R中的并行foreach共享内存

Question

问题描述：

我有一个大矩阵 c，已加载到 RAM 内存中。我的目标是通过并行处理对其进行只读访问。然而，当我创建连接时，我使用 doSNOW、doMPI、big.matrix 等，使用的 ram 数量急剧增加。

有没有办法正确创建共享内存，所有进程都可以从中读取，而不创建所有数据的本地副本？

示例：

libs<-function(libraries){# Installs missing libraries and then load them
  for (lib in libraries){
    if( !is.element(lib, .packages(all.available = TRUE)) ) {
      install.packages(lib)
    }
    library(lib,character.only = TRUE)
  }
}

libra<-list("foreach","parallel","doSNOW","bigmemory")
libs(libra)

#create a matrix of size 1GB aproximatelly
c<-matrix(runif(10000^2),10000,10000)
#convert it to bigmatrix
x<-as.big.matrix(c)
# get a description of the matrix
mdesc <- describe(x)
# Create the required connections    
cl <- makeCluster(detectCores ())
registerDoSNOW(cl)
out<-foreach(linID = 1:10, .combine=c) %dopar% {
  #load bigmemory
  require(bigmemory)
  # attach the matrix via shared memory??
  m <- attach.big.matrix(mdesc)
  #dummy expression to test data aquisition
  c<-m[1,1]
}
closeAllConnections()

内存：在上图中，您可能会发现内存增加很多，直到 foreach 结束并被释放。

Answer 1

我想问题的解决办法可以从foreach包here的作者Steve Weston的post那里看到。他在那里说：

The doParallel package will auto-export variables to the workers that are referenced in the foreach loop.

所以我认为问题是在您的代码中，您的大矩阵 c 在作业 c<-m[1,1] 中被引用。只需尝试 xyz <- m[1,1]，看看会发生什么。

这是一个文件支持的例子 big.matrix:

#create a matrix of size 1GB aproximatelly
n <- 10000
m <- 10000
c <- matrix(runif(n*m),n,m)
#convert it to bigmatrix
x <- as.big.matrix(x = c, type = "double", 
                 separated = FALSE, 
                 backingfile = "example.bin", 
                 descriptorfile = "example.desc")
# get a description of the matrix
mdesc <- describe(x)
# Create the required connections    
cl <- makeCluster(detectCores ())
registerDoSNOW(cl)
## 1) No referencing
out <- foreach(linID = 1:4, .combine=c) %dopar% {
  t <- attach.big.matrix("example.desc")
  for (i in seq_len(30L)) {
    for (j in seq_len(m)) {
      y <- t[i,j]
    }
  }
  return(0L)
}

## 2) Referencing
out <- foreach(linID = 1:4, .combine=c) %dopar% {
  invisible(c) ## c is referenced and thus exported to workers
  t <- attach.big.matrix("example.desc")
  for (i in seq_len(30L)) {
    for (j in seq_len(m)) {
      y <- t[i,j]
    }
  }
  return(0L)
}
closeAllConnections()

Answer 2

或者，如果您在 Linux/Mac 上并且想要 CoW 共享内存，请使用 fork。首先将所有数据加载到主线程中，然后使用 parallel 包中的通用函数 mcparallel 启动工作线程（分支）。

您可以使用 mccollect 或使用 Rdsm 库使用真正的共享内存来收集他们的结果，如下所示：

library(parallel)
library(bigmemory) #for shared variables
shared<-bigmemory::big.matrix(nrow = size, ncol = 1, type = 'double')
shared[1]<-1 #Init shared memory with some number

job<-mcparallel({shared[1]<-23}) #...change it in another forked thread
shared[1,1] #...and confirm that it gets changed
# [1] 23

您可以确认，如果延迟写入，该值确实会在后台更新：

fn<-function()
{
  Sys.sleep(1) #One second delay
  shared[1]<-11
}

job<-mcparallel(fn())
shared[1] #Execute immediately after last command
# [1] 23
aaa[1,1] #Execute after one second
# [1] 11
mccollect() #To destroy all forked processes (and possibly collect their output)

要控制并发并避免竞争条件，请使用锁：

library(synchronicity) #for locks
m<-boost.mutex() #Lets create a mutex "m"

bad.incr<-function() #This function doesn't protect the shared resource with locks:
{
  a<-shared[1]
  Sys.sleep(1)
  shared[1]<-a+1
}

good.incr<-function()
{
  lock(m)
  a<-shared[1]
  Sys.sleep(1)
  shared[1]<-a+1
  unlock(m)
}

shared[1]<-1
for (i in 1:5) job<-mcparallel(bad.incr())
shared[1] #You can verify, that the value didn't get increased 5 times due to race conditions

mccollect() #To clear all threads, not to get the values
shared[1]<-1
for (i in 1:5) job<-mcparallel(good.incr())
shared[1] #As expected, eventualy after 5 seconds of waiting you get the 6
#[1] 6 

mccollect()

编辑：

我通过将 Rdsm::mgrmakevar 交换为 bigmemory::big.matrix 来简化依赖关系。 mgrmakevar 内部调用 big.matrix，我们不需要更多。

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