diff --git a/demos/snow_package/mpi_snow_hello.sh b/demos/snow_package/mpi_snow_hello.sh
deleted file mode 100644
index 961d259..0000000
--- a/demos/snow_package/mpi_snow_hello.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-mpiexec --allow-run-as-root -n 1 --hostfile /kube-openmpi/generated/hostfile R --vanilla -f snow-hello.R > mpi.out
diff --git a/demos/snow_package/slurm_submit_snow_hello.sh b/demos/snow_package/slurm_submit_snow_hello.sh
deleted file mode 100644
index 7be1804..0000000
--- a/demos/snow_package/slurm_submit_snow_hello.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/bash
-#SBATCH --ntasks=5
-
-cd $SLURM_SUBMIT_DIR
-
-mpiexec -n 1 R --vanilla -f snow-hello.R
diff --git a/demos/snow_package/snow-hello.R b/demos/snow_package/snow-hello.R
deleted file mode 100644
index 0a34de1..0000000
--- a/demos/snow_package/snow-hello.R
+++ /dev/null
@@ -1,95 +0,0 @@
-### Paul Johnson
-### 2017-07-20
-
-### Garrett Mills
-### 2019-05-02
-
-### Demonstration of SNOW "Simple Network of Workstations" using MPI
-### "Message Passing Interface" (OpenMPI implementation)
-
-
-library(snow)
-
-p <- rnorm(123, m = 33)
-
-## Sub script asks for 18 cores, here cluster must
-## be one smaller
-CLUSTERSIZE <- Rmpi::mpi.universe.size()-1
-
-cl <- makeCluster(CLUSTERSIZE, type = "MPI")
-
-
-## One way to send function to each system.
-## Could send identical arguments to nodes
-clusterCall(cl, function() {
-    Sys.info()[c("nodename","machine")]
-    date()
-}
-)
-
-
-timeFn <- function(){
-    y1 <- Sys.info()[c("nodename","machine")]
-    y2 <- date()
-    list(y1, y2)
-}
-
-## Send function to each node
-clusterExport(cl, c("timeFn"))
-
-## Evaluates a string on a node
-x1 <- clusterEvalQ(cl, timeFn())
-
-print(x1)
-
-
-clusterCall(cl, function() rnorm(1, 33, 1) )
-
-
-myNorms <- matrix(rnorm(100*CLUSTERSIZE), ncol = CLUSTERSIZE)
-
-## goes column by column
-mypapply <- parApply(cl, myNorms, 2,  print)
-
-attributes(mypapply)
-
-mypapply <- parApply(cl, myNorms, 2, mean )
-
-mypapply
-
-myNorms <- matrix(rnorm(100*CLUSTERSIZE), ncol = CLUSTERSIZE)
-
-mySum <- function( v ){
-    s <-Sys.info()[c("nodename")]
-    s[2] <- date()
-    ms <- sum(v)
-    list(s, ms)
-}
-
-mypcapply <- parApply(cl, myNorms, 2, mySum)
-
-mypcapply
-
-myNorms <- matrix(rnorm(2500*CLUSTERSIZE), ncol = CLUSTERSIZE)
-
-## Add the system date (includes time) before and after
-## calculations to vector s
-myMeans <- function(v){
-    s <- Sys.info()[c("nodename")]
-    s[2] <- date()
-    ms <- mean(v)
-    ## want to slow this down so you can study the cluster? uncomment this:
-    ## x <- rnorm(50000000); x <- log(50+x); sum(x); mean(x); quantile(x); gg <- cut(x, quantile(x)) 
-    s[3] <- date()
-    list(s, ms)
-}
-
-mypcapply <- parApply(cl, myNorms, 2, myMeans )
-
-mypcapply
-
-
-library(snow)
-stopCluster(cl)
-Rmpi::mpi.quit()
-