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跟进一些data.table并行性(1) (2) (3)我试图弄清楚。这种语法有什么问题？

library(data.table)
set.seed(1234)
dt <- data.table(id= factor(sample(1L:10000L, size= 1e6, replace= TRUE)),
       val= rnorm(n= 1e6), key="id")

foo <- function(l) sum(l)

dt2 <- dt[, foo(.SD), by= "id"]

library(parallel)
cl <- makeCluster(detectCores())
dt3 <- clusterApply(cl, x= parallel:::splitRows(dt, detectCores()),
          fun=lapply, FUN= function(x,foo) {
            x[, foo(data.table:::".SD"), by= "id"]
          }, foo= foo)
stopCluster(cl)
# note that library(parallel) is annoying and you often have to do this type ("::", ":::") of exporting to the parallel package

checkForRemoteErrors(val) 中的错误：4 个节点产生错误；第一个错误：维数不正确

cl <- makeCluster(detectCores())
dt3 <- clusterApply(cl, x= parallel:::splitRows(dt, detectCores()),
          fun=lapply, FUN= function(x,foo) {
            x <- data.table::data.table(x)
            x[, foo(data.table:::".SD"), by= "id"]
          }, foo= foo)
stopCluster(cl)

checkForRemoteErrors(val) 中的错误：4 个节点产生错误；第一个错误：找不到对象“id”

我已经玩了很多语法。这两个似乎是我能得到的最接近的。显然有些事情仍然不对劲。

我真正的问题是类似的结构，但有更多的行，我正在使用一台具有 24 个内核/48 个逻辑处理器的机器。所以看着我的电脑使用大约 4% 的计算能力（仅使用 1 个核心）真的很烦人

4

1 回答 1

2

您可能想要评估 Rserve 解决方案的并行性。

请参阅下面的示例构建在 Rserve 上，使用 2 个本地并行 R 节点。它也可以分布在远程实例上。 

library(data.table)
set.seed(1234)
dt <- data.table(id= factor(sample(1L:10000L, size= 1e6, replace= TRUE)),
                 val= rnorm(n= 1e6), key="id")
foo <- function(l) sum(l)

library(big.data.table)
# start 2 R instances
library(Rserve)
port = 6311:6312
invisible(sapply(port, function(port) Rserve(debug = FALSE, port = port, args = c("--no-save"))))
# client side
rscl = rscl.connect(port = port, pkgs = "data.table") # connect and auto require packages
bdt = as.big.data.table(dt, rscl) # create big.data.table from local data.table and list of connections to R nodes
rscl.assign(rscl, "foo", foo) # assign `foo` function to nodes
bdt[, foo(.SD), by="id"][, foo(.SD), by="id"] # first query is run remotely, second locally
#          id         V1
#    1:     1  10.328998
#    2:     2  -8.448441
#    3:     3  21.475910
#    4:     4  -5.302411
#    5:     5 -11.929699
#   ---                 
# 9996:  9996  -4.905192
# 9997:  9997  -4.293194
# 9998:  9998  -2.387100
# 9999:  9999  16.530731
#10000: 10000 -15.390543

# optionally with special care
# bdt[, foo(.SD), by= "id", outer.aggregate = TRUE]

会话信息：

R version 3.2.3 (2015-12-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.4 LTS

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8     LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8    LC_PAPER=en_GB.UTF-8      
 [8] LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] Rserve_1.8-5         big.data.table_0.3.3 data.table_1.9.7    

loaded via a namespace (and not attached):
[1] RSclient_0.7-3 tools_3.2.3
于 2016-03-08T02:13:36.730 回答