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Installing parallel on mac7/9/2023 ![]() ![]() The code below shows 0.14 seconds (on my machine) are spent is spent evaluating. In other words, it takes longer to send the data to/from the nodes than it does to perform the calculation.įor the same data set, the communication costs are approximately fixed, so parallel processing is going to be more useful as the time spent evaluating the function increases. Running in parallel will be slower than running sequentially when the communication costs between the nodes is greater than the calculation time of the function. This was run on a Mac OS X 10.5.8 Core 2 Duo machine. ![]() A more complicated computation would also tilt it even further in parallel's favor, likely giving it an advantage. With a 10 times bigger data set, the penalty for parallel is smaller. Using the rbenchmark package: baseball10 <- baseballīenchmark(noparallel = ddply(baseball. The baseball data may be too small to see improvement by making the computations parallel the overhead of passing the data to the different processes may be swamping any speedup by doing the calculations in parallel.
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