Ⓒ 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

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Computational Systems

The computation systems can be defined as the systems that are capable of solving a problem that includes calculations either mathematical or logical, and are able to produce the result as an output. For example, a simple calculator that is given a set of numbers and operators as input and produces the result as an output can be defined as a computational system. The above example is a simple illustration only, the computational systems range from simple devices to complex devices such as the systems that control the SpaceX companies, Starlink satellites which are being quite recently seen in the skies at many places globally.