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LICENSE.md

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+Please note: We provide the copyright and license information in accordance to the [REUSE Specification 3.0](https://reuse.software/spec/).
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LICENSES/CC-BY-4.0.txt

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LICENSES/MIT.txt

+MIT License
+
+Copyright (c) 2023 Competence Center for High Performance Computing in Hessen (HKHLR) <sittr@hrz.uni-marburg.de.de>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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+

exercises/Rmpi/mpi.R

+#!/usr/bin/env Rscript
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+source("../codebase/glm_iris.R")
+library(Rmpi)
+
+# MPI-only variable - we need these for sending and receiving message destinations
+rank <- mpi.comm.rank(0)
+size <- mpi.comm.size(0)
+
+# All ranks need the complete data set in this case
+x <- get_data()
+
+# Only executed by the first process (setup and work distribution)
+if(rank==0) {
+        # Full vector of trial indices
+        trials <- 1:10000
+        # Doing the same as 'clusterSplit()' - partitioning the indices into <nprocs> chunks
+        trial_chunks <- split(trials, cut(seq_along(trials),size,labels=FALSE))
+        # Send each process the indices it should work on
+        for(i in 1:(size-1)) {
+                #print(paste0("Sending ",trial_chunks[[i+1]]," to rank ",i))
+                mpi.send.Robj(trial_chunks[[i+1]],i,tag=0,comm=0)
+        }
+        # Local process also does work
+        local_chunk <- trial_chunks[[1]]
+} else {
+        # Only executed by other processes - receive indices on which to work on 
+        local_chunk <- mpi.recv.Robj(0,tag=0,comm=0)
+}
+#print(paste0("rank ",rank," processing local chunk: ",local_chunk))
+# Each process executes this on its own set of indices
+local_result <- lapply(local_chunk,boot_fx,data=x)
+
+# Only executed by the first process (gather and output results)
+if(rank==0) {
+        # Receive results from worker processes, append them to the local result
+        for(i in 1:(size-1)) {
+                remote_result <- mpi.recv.Robj(i,tag=0,comm=0)
+                #print(paste0("got remote result: ",remote_result," from rank ",i))
+                local_result <- c(local_result,remote_result)
+       }
+        # Output combined results
+        local_result
+} else {
+        # Only executed by other processes - send results back to the first process
+        #print(paste0("rank ",rank," sending local result: ",local_result))
+        mpi.send.Robj(local_result,0,tag=0,comm=0)
+}
+
+# Signal to the MPI runtime that this process is done executing - has to be called by all processes
+mpi.finalize()

exercises/Rmpi/mpi_R_job.sh

+#!/bin/bash
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+#SBATCH --job-name=mpi_R_job
+#SBATCH --output=mpi_R_job.%j.out
+#SBATCH --error=mpi_R_job.%j.err
+#SBATCH --ntasks=8
+#SBATCH --cpus-per-task=1
+#SBATCH --mem-per-cpu=1G
+#SBATCH --time=00:30:00
+
+module purge
+module load gnu9 openmpi4 R
+
+export OMP_NUM_THREADS=1
+
+time mpiexec -n $SLURM_NTASKS Rscript mpi.R

exercises/Rmpi/mpi_R_job_collective.sh

+#!/bin/bash
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+#SBATCH --job-name=mpi_R_job_collective
+#SBATCH --output=mpi_R_job_collective.%j.out
+#SBATCH --error=mpi_R_job_collective.%j.err
+#SBATCH --ntasks=8
+#SBATCH --cpus-per-task=1
+#SBATCH --mem-per-cpu=1G
+#SBATCH --time=00:30:00
+
+module purge
+module load gnu9 openmpi4 R
+
+export OMP_NUM_THREADS=1
+
+time mpiexec -n $SLURM_NTASKS Rscript mpi_collective.R

exercises/Rmpi/mpi_collective.R

+#!/usr/bin/env Rscript
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+source("../codebase/glm_iris.R")
+library(Rmpi)
+
+# MPI-only variable - we need these for sending and receiving message destinations
+rank <- mpi.comm.rank(0)
+size <- mpi.comm.size(0)
+
+# All ranks need the complete data set in this case
+x <- get_data()
+
+# Rank 0 splits the indices for distribution
+if(rank==0) {
+	trials <- 1:10000
+	trial_chunks <- split(trials, cut(seq_along(trials),size,labels=FALSE))
+}
+
+# Rank 0 distributes the chunks to workers (including itself)
+local_chunk <- mpi.scatter.Robj(trial_chunks,root=0,comm=0)
+
+# Each worker calculates its local part of the data
+local_result <- lapply(local_chunk,boot_fx,data=x)
+
+# Gather results into a list on rank 0
+global_result <- mpi.gather.Robj(local_result,root=0,comm=0,simplify=FALSE)
+
+# Rank 0 does the output
+if(rank==0) {
+	# Additional unlist() because we have a list of lists 
+	# and instead want a simple list for comparison to mpi.R
+	unlist(global_result,recursive=FALSE)
+}
+
+# Signal to the MPI runtime that this process is done executing - has to be called by all processes
+mpi.finalize()

exercises/Rmpi/rmpisnow.R

+#!/usr/bin/env Rscript
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+source("../codebase/glm_iris.R")
+library(snow)
+library(foreach)
+library(doParallel)
+library(microbenchmark)
+
+# Additional call to set up cluster object
+# n_cores is determined by the snow package, according to number of started MPI processes!
+cl <- snow::makeCluster()
+# Tells foreach's %dopar% to use the cluster object for parallelization
+registerDoParallel(cl)
+
+# When using cluster calls, we'd normally use this to ensure proper parallel RNG:
+#clusterSetRNGStream(cl, NULL)
+
+# Results checking: since we hard-set RNG seed in boot_fx(), we can
+# check whether serial and parallel runs produce the same result using
+# microbenchmark's builtin check feature
+check_result <- function(values) {
+    err_thresh = 1e-12
+    max_err = max(
+                  abs(unlist(values[[1]]) - unlist(values[[2]])),
+                  abs(unlist(values[[1]]) - unlist(values[[3]])),
+                  abs(unlist(values[[2]]) - unlist(values[[3]]))
+                  )
+    max_err < err_thresh
+}
+
+x <- get_data()
+trials <- 1:10000
+
+microbenchmark("serial" = lapply(trials,boot_fx,data=x),
+               "parLapply" = snow::parLapply(cl,trials,boot_fx,data=x),
+               "foreach" = foreach(i=snow::clusterSplit(cl,trials),.inorder=TRUE) %dopar% lapply(i,boot_fx,data=x),
+               times=5L,
+               check=check_result
+               )
+
+snow::stopCluster(cl)

exercises/Rmpi/rmpisnow_R_job.sh

+#!/bin/bash
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+#SBATCH --job-name=rmpisnow_R_job
+#SBATCH --output=rmpisnow_R_job.%j.out
+#SBATCH --error=rmpisnow_R_job.%j.err
+#SBATCH --ntasks=8
+#SBATCH --cpus-per-task=1
+#SBATCH --mem-per-cpu=1G
+#SBATCH --time=00:30:00
+
+module purge
+module load gnu9 openmpi4 R
+
+export OMP_NUM_THREADS=1
+
+mpiexec -n $SLURM_NTASKS ~/R/x86_64-pc-linux-gnu-library/4.1/snow/RMPISNOW --no-echo < rmpisnow.R

exercises/cluster/cluster_1.R

+#!/usr/bin/env Rscript
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+source("../codebase/glm_iris.R")
+library(parallel)
+library(foreach)
+library(doParallel)
+library(microbenchmark)
+
+# Read n_cores from commandline argument, set 4 as default
+args <- commandArgs(trailingOnly=TRUE)
+n_cores <- 1
+if(length(args) > 0) {
+	n_cores <- as.integer(args[1])
+}
+
+# Additional call to set up cluster object
+cl <- makeCluster(n_cores)
+# Tells foreach's %dopar% to use the cluster object for parallelization
+registerDoParallel(cl)
+
+# When using cluster calls, we'd normally use this to ensure proper parallel RNG:
+#clusterSetRNGStream(cl, NULL)
+
+# Results checking: since we hard-set RNG seed in boot_fx(), we can
+# check whether serial and parallel runs produce the same result using
+# microbenchmark's builtin check feature
+check_result <- function(values) {
+    err_thresh = 1e-12
+    max_err = max(
+		  abs(unlist(values[[1]]) - unlist(values[[2]])),
+		  abs(unlist(values[[1]]) - unlist(values[[3]])),
+		  abs(unlist(values[[2]]) - unlist(values[[3]]))
+		  )
+    max_err < err_thresh
+}
+
+x <- get_data()
+trials <- 1:10000
+
+microbenchmark(
+	       "serial" = lapply(trials,boot_fx,data=x),
+           "parLapply" = parLapply(cl,trials,boot_fx,data=x),
+           "foreach" = foreach(i=trials) %dopar% boot_fx(i,x),
+	       times=5L,
+	       check=check_result
+	       )
+
+# Needed for proper finalization
+stopCluster(cl)

exercises/cluster/cluster_1_R_job.sh

+#!/bin/bash
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+#SBATCH --job-name=cluster_1_R_job
+#SBATCH --output=cluster_1_R_job.%j.out
+#SBATCH --error=cluster_1_R_job.%j.err
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem-per-cpu=1G
+#SBATCH --time=00:30:00
+
+module purge
+module load gnu9 openmpi4 R
+
+export OMP_NUM_THREADS=1
+
+Rscript cluster_1.R $SLURM_CPUS_PER_TASK

exercises/cluster/cluster_2.R

+#!/usr/bin/env Rscript
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+source("../codebase/glm_iris.R")
+library(parallel)
+library(foreach)
+library(doParallel)
+library(microbenchmark)
+
+# Read n_cores from commandline argument, set 4 as default
+args <- commandArgs(trailingOnly=TRUE)
+n_cores <- 1
+if(length(args) > 0) {
+	n_cores <- as.integer(args[1])
+}
+
+# Additional call to set up cluster object
+cl <- makeCluster(n_cores)
+# Tells foreach's %dopar% to use the cluster object for parallelization
+registerDoParallel(cl)
+
+# When using cluster calls, we'd normally use this to ensure proper parallel RNG:
+#clusterSetRNGStream(cl, NULL)
+
+# Results checking: since we hard-set RNG seed in boot_fx(), we can
+# check whether serial and parallel runs produce the same result using
+# microbenchmark's builtin check feature
+check_result <- function(values) {
+    err_thresh = 1e-12
+    max_err = max(
+		  abs(unlist(values[[1]]) - unlist(values[[2]])),
+		  abs(unlist(values[[1]]) - unlist(values[[3]])),
+          abs(unlist(values[[1]]) - unlist(values[[4]])),
+		  abs(unlist(values[[2]]) - unlist(values[[3]])),
+          abs(unlist(values[[2]]) - unlist(values[[4]])),
+          abs(unlist(values[[3]]) - unlist(values[[4]]))
+		  )
+    max_err < err_thresh
+}
+
+x <- get_data()
+trials <- 1:10000
+
+microbenchmark(
+	       "serial" = lapply(trials,boot_fx,data=x),
+           "parLapply" = parLapply(cl,trials,boot_fx,data=x),
+           "foreach" = foreach(i=trials) %dopar% boot_fx(i,x),
+           "foreach_split" = foreach(i=clusterSplit(cl,trials)) %dopar% lapply(i,boot_fx,data=x),
+	       times=5L,
+	       check=check_result
+	       )
+
+# Needed for proper finalization
+stopCluster(cl)

exercises/cluster/cluster_2_R_job.sh

+#!/bin/bash
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+#SBATCH --job-name=cluster_2_R_job
+#SBATCH --output=cluster_2_R_job.%j.out
+#SBATCH --error=cluster_2_R_job.%j.err
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem-per-cpu=1G
+#SBATCH --time=00:30:00
+
+module purge
+module load gnu9 openmpi4 R
+
+export OMP_NUM_THREADS=1
+
+Rscript cluster_2.R $SLURM_CPUS_PER_TASK

exercises/cluster/cluster_3.R

+#!/usr/bin/env Rscript
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+source("../codebase/glm_iris.R")
+library(parallel)
+library(foreach)
+library(doParallel)
+library(microbenchmark)
+
+# Read n_cores from commandline argument, set 4 as default
+args <- commandArgs(trailingOnly=TRUE)
+n_cores <- 1
+if(length(args) > 0) {
+	n_cores <- as.integer(args[1])
+}
+
+# Additional call to set up cluster object
+cl <- makeCluster(n_cores)
+# Tells foreach's %dopar% to use the cluster object for parallelization
+registerDoParallel(cl)
+
+# When using cluster calls, we'd normally use this to ensure proper parallel RNG:
+#clusterSetRNGStream(cl, NULL)
+
+# Results checking: since we hard-set RNG seed in boot_fx(), we can
+# check whether serial and parallel runs produce the same result using
+# microbenchmark's builtin check feature
+check_result <- function(values) {
+    err_thresh = 1e-12
+    max_err = max(
+		  abs(unlist(values[[1]]) - unlist(values[[2]])),
+		  abs(unlist(values[[1]]) - unlist(values[[3]])),
+          abs(unlist(values[[1]]) - unlist(values[[4]])),
+		  abs(unlist(values[[2]]) - unlist(values[[3]])),
+          abs(unlist(values[[2]]) - unlist(values[[4]])),
+          abs(unlist(values[[3]]) - unlist(values[[4]]))
+		  )
+    max_err < err_thresh
+}
+
+x <- get_data()
+trials <- 1:10000
+
+microbenchmark(
+	       "serial" = lapply(trials,boot_fx,data=x),
+           "mclapply" = mclapply(trials,boot_fx,data=x,mc.cores=n_cores),
+           "parLapply" = parLapply(cl,trials,boot_fx,data=x),
+           "foreach_split" = foreach(i=clusterSplit(cl,trials)) %dopar% lapply(i,boot_fx,data=x),
+	       times=5L,
+	       check=check_result
+	       )
+
+# Needed for proper finalization
+stopCluster(cl)

exercises/cluster/cluster_3_R_job.sh

+#!/bin/bash
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+#SBATCH --job-name=cluster_3_R_job
+#SBATCH --output=cluster_3_R_job.%j.out
+#SBATCH --error=cluster_3_R_job.%j.err
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem-per-cpu=1G
+#SBATCH --time=00:30:00
+
+module purge
+module load gnu9 openmpi4 R
+
+export OMP_NUM_THREADS=1
+
+Rscript cluster_3.R $SLURM_CPUS_PER_TASK

exercises/cluster/cluster_4.R

+#!/usr/bin/env Rscript
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+source("../codebase/glm_iris.R")
+library(parallel)
+library(foreach)
+library(doParallel)
+library(microbenchmark)
+
+# Read n_cores from commandline argument, set 4 as default
+args <- commandArgs(trailingOnly=TRUE)
+n_cores <- 1
+if(length(args) > 0) {
+	n_cores <- as.integer(args[1])
+}
+
+# Additional call to set up cluster object
+cl <- makeCluster(n_cores)
+# Tells foreach's %dopar% to use the cluster object for parallelization
+registerDoParallel(cl)
+
+# When using cluster calls, we'd normally use this to ensure proper parallel RNG:
+#clusterSetRNGStream(cl, NULL)
+
+# Results checking: since we hard-set RNG seed in boot_fx(), we can
+# check whether serial and parallel runs produce the same result using
+# microbenchmark's builtin check feature
+check_result <- function(values) {
+    err_thresh = 1e-12
+    max_err = max(
+		  abs(unlist(values[[1]]) - unlist(values[[2]])),
+		  abs(unlist(values[[1]]) - unlist(values[[3]])),
+          abs(unlist(values[[1]]) - unlist(values[[4]])),
+		  abs(unlist(values[[2]]) - unlist(values[[3]])),
+          abs(unlist(values[[2]]) - unlist(values[[4]])),
+          abs(unlist(values[[3]]) - unlist(values[[4]]))
+		  )
+    max_err < err_thresh
+}
+
+x <- get_data()
+trials <- 1:10000
+
+microbenchmark(
+	       "serial" = lapply(trials,boot_fx,data=x),
+           "mclapply" = mclapply(trials,boot_fx,data=x,mc.cores=n_cores),
+           "parLapply" = parLapply(cl,trials,boot_fx,data=x),
+           "foreach_split" = foreach(i=clusterSplit(cl,trials)) %dopar% lapply(i,boot_fx,data=x),
+           "foreach_expl" = foreach(i=clusterSplit(cl,trials)) %dopar% {
+               boot_fx_inline <- function(i,data) {
+                   set.seed(i)
+                   ind <- sample(100,100,replace=TRUE)
+                   result1 <- glm(x[ind,2]~x[ind,1],family=binomial(logit))
+                   r <- coefficients(result1)
+               }
+               lapply(i,boot_fx_inline,data=x)
+           },
+	       times=5L,
+	       check=check_result
+	       )
+
+# Needed for proper finalization
+stopCluster(cl)

exercises/cluster/cluster_4_R_job.sh

+#!/bin/bash
+
+# SPDX-FileCopyrightText: 2023 Competence Center for High Performance Computing in Hessen (HKHLR)
+# SPDX-License-Identifier: MIT
+
+#SBATCH --job-name=cluster_4_R_job
+#SBATCH --output=cluster_4_R_job.%j.out
+#SBATCH --error=cluster_4_R_job.%j.err
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem-per-cpu=1G
+#SBATCH --time=00:30:00
+
+module purge
+module load gnu9 openmpi4 R
+
+export OMP_NUM_THREADS=1
+
+Rscript cluster_4.R $SLURM_CPUS_PER_TASK