Ⓒ Exercise Timings parSapply() Running the dice game is embarrassingly parallel. These types of simulations usually (but not always) produce a good speed-up. As before, we can use microbenchmark() or system.time(). For simplicity, we'll use system.time() in this exercise. Instructions The play() function has been defined in your workspace. We'll assume that we want to play one hundred thousand games. Set no_of_games to 15. • Use system. time() to time play() being repeatedly called in serial. • Call sapply() with 1:no_of_games and the play() wrapper function. < • Assign the result to serial. • Wrap this call in system. time() to time it. Create a 4 core cluster object and export the play() function to it. • Use system.time() to time play() being repeatedly called in parallel. • Rework the code you used to create serial, but make it work in parallel. script.R 1 2 3 4 5 # Set the number of games to play no_of_games <- 1e5 # Time serial version system.time (serial <- par Sapply(1:no_of_games, function(i) play())) 6 7 8 9 10 11 # Time parallel version 12 system.time (par <- play) 13 14 # Stop cluster 15 stopCluster (cl) # Set up cluster cl <- makeCluster (4) clusterExport (cl, "play") R Console Slides Notes 5 Run Code Light Mod Submit Answer
Ⓒ Exercise Timings parSapply() Running the dice game is embarrassingly parallel. These types of simulations usually (but not always) produce a good speed-up. As before, we can use microbenchmark() or system.time(). For simplicity, we'll use system.time() in this exercise. Instructions The play() function has been defined in your workspace. We'll assume that we want to play one hundred thousand games. Set no_of_games to 15. • Use system. time() to time play() being repeatedly called in serial. • Call sapply() with 1:no_of_games and the play() wrapper function. < • Assign the result to serial. • Wrap this call in system. time() to time it. Create a 4 core cluster object and export the play() function to it. • Use system.time() to time play() being repeatedly called in parallel. • Rework the code you used to create serial, but make it work in parallel. script.R 1 2 3 4 5 # Set the number of games to play no_of_games <- 1e5 # Time serial version system.time (serial <- par Sapply(1:no_of_games, function(i) play())) 6 7 8 9 10 11 # Time parallel version 12 system.time (par <- play) 13 14 # Stop cluster 15 stopCluster (cl) # Set up cluster cl <- makeCluster (4) clusterExport (cl, "play") R Console Slides Notes 5 Run Code Light Mod Submit Answer
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Transcribed Image Text:B Exercise
Timings parSapply()
Running the dice game is embarrassingly parallel. These types of
simulations usually (but not always) produce a good speed-up. As
before, we can use microbenchmark() or system.time(). For
simplicity, we'll use system.time() in this exercise.
Instructions
The play() function has been defined in your workspace. We'll
assume that we want to play one hundred thousand games.
Set no_of_games to 1e5.
• Use system.time() to time play() being repeatedly called in
serial.
• Call sapply() with 1:no_of_games and the play() wrapper
function.
• Assign the result to serial.
• Wrap this call in system.time() to time it.
• Create a 4 core cluster object and export the play() function to it.
• Use system.time() to time play() being repeatedly called in
parallel.
• Rework the code you used to create serial, but make it work in
parallel.
<
script.R
1 # Set the number of games to play
no_of_games <- 1e5
23 J
4
5
6
7
8
9
10
982LZ
11
12
13
# Time serial version
system.time (serial <- par Sapply(1:no_of_games, function(i) play()))
14
# Set up cluster
cl <- makeCluster (4)
clusterExport(cl, "play")
# Time parallel version
system.time (par <-
play)
# Stop cluster
15 stopCluster(cl)
R Console
Slides
Notes
5 Run Code
☀
Light Mod-
Submit Answer
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