Mar
7
Hyperthreading FTW? Testing parallelization performance in R.
Alright, let's test some parallelization functionalities in R.
The machine:
MacBook Air (mid-2013) with 8 GB of RAM and the i7 CPU (Intel i7 Haswell 4650U). This CPU is hyper-threaded, meaning (at least that's my understanding of it) that it has two physical cores but can run up to four threads.
The task:
Draw a number of cases from a normal distribution with a mean of 10 and a standard deviation of 30. Do this a hundred times and combine the result in one vector. The number of cases is varied from half a million to two millions. The number of cores used by R is also varied (between 1 and 4). All this is done 5 times, hence we get multiple estimates of each run's properties. Altogether, 80 runs are made: 5 times x 4 n-cores x 4 n-cases = 80 runs.
The machine:
MacBook Air (mid-2013) with 8 GB of RAM and the i7 CPU (Intel i7 Haswell 4650U). This CPU is hyper-threaded, meaning (at least that's my understanding of it) that it has two physical cores but can run up to four threads.
The task:
Draw a number of cases from a normal distribution with a mean of 10 and a standard deviation of 30. Do this a hundred times and combine the result in one vector. The number of cases is varied from half a million to two millions. The number of cores used by R is also varied (between 1 and 4). All this is done 5 times, hence we get multiple estimates of each run's properties. Altogether, 80 runs are made: 5 times x 4 n-cores x 4 n-cases = 80 runs.
