r - 将多核与 Snow Cluster 相结合

Question

Parallel R 相当新。快速提问。我有一个计算密集型的算法。幸运的是，它可以很容易地分解成碎片以使用multicoreor snow。我想知道的是在实践multicore中与 ? 结合使用是否被认为是好的snow？

我想做的是将我的负载拆分为在集群中的多台机器上和每台机器上运行。我想利用机器上的所有内核。对于这种类型的处理，混合雪是否合理multicore？

Answer 1

我使用了 lockoff 上面建议的方法，即使用并行包将令人尴尬的并行工作负载分配到具有多个内核的多台机器上。首先，工作负载分布在所有机器上，然后每台机器的工作负载分布在它的所有内核上。这种方法的缺点是机器之间没有负载均衡（至少我不知道怎么做）。

所有加载的 r 代码应该是相同的，并且在所有机器（svn）上的相同位置。因为初始化集群需要相当长的时间，所以可以通过重用创建的集群来改进下面的代码。

foo <- function(workload, otherArgumentsForFoo) {
    source("/home/user/workspace/mycode.R")
    ...
}

distributedFooOnCores <- function(workload) {
    # Somehow assign a batch number to every record
    workload$ParBatchNumber = NA
    # Split the assigned workload into batches according to DistrParNumber
    batches = by(workload, workload$ParBatchNumber, function(x) x)

    # Create a cluster with workers on all machines 
    library("parallel")
    cluster = makeCluster(detectCores(), outfile="distributedFooOnCores.log")
    batches = parLapply(cluster, batches, foo, otherArgumentsForFoo)
    stopCluster(cluster)

    # Merge the resulting batches
    results = someEmptyDataframe
    p = 1;
    for(i in 1:length(batches)){
        results[p:(p + nrow(batches[[i]]) - 1), ] = batches[[i]]
        p = p + nrow(batches[[i]])      
    }

    # Clean up
    workload$ParBatchNumber = NULL
    return(invisible(results))
}

distributedFooOnMachines <- function(workload) {
    # Somehow assign a batch number to every record
    workload$DistrBatchNumber = NA
    # Split the assigned activity into batches according to DistrBatchNumber
    batches = by(workload, workload$DistrBatchNumber, function(x) x)

    # Create a cluster with workers on all machines 
    library("parallel")
    # If makeCluster hangs, please make sure passwordless ssh is configured on all machines
    cluster = makeCluster(c("machine1", "etc"), master="ub2", user="", outfile="distributedFooOnMachines.log")
    batches = parLapply(cluster, batches, foo, otherArgumentsForFoo)
    stopCluster(cluster)

    # Merge the resulting batches
    results = someEmptyDataframe
    p = 1;
    for(i in 1:length(batches)){
        results[p:(p + nrow(batches[[i]]) - 1), ] = batches[[i]]
        p = p + nrow(batches[[i]])      
    }

    # Clean up
    workload$DistrBatchNumber = NULL
    return(invisible(results))
}

我很感兴趣如何改进上述方法。