For information, in R you can run MPI jobs if you hit the 125 communication socket limit.
However, the work is spread differently, it must be taken into account. The work could be more realistic using MPI, but its performance for sharing data is another story.
MPI chart behavior:
To use MPI, in R, replace the following, make sure to add the MPI type to the cluster spawn:
makeCluster(my_threads, type = "MPI")
BIG WARNING: You should close the MPI cluster with Rmpi::mpi.exit(), otherwise it crashes (it could still crash with Rmpi::mpi.exit()). I would recommend to use that command only at the very end (don't use stopCluster(cl)), and to check afterwards if there are any zombie R processes left in the wild.
Due to how MPI and R socket spreads the work at the beginning, the timings are entirely different from 1 to 7 (except the "[Parallel"] part which is creating processes and sending data to each process). MPI spawns processes faster than R sockets, however it is faster to communicate to R socket processes than MPI processes. The huge difference in sending data might be also because we are using serialization v3 in R, which is the default now in R 3.6.
MPI:
[Parallel] 58 Process(es) Creation Time: 1.102s
[Parallel] Sending Hardware Specifications Time: 24.290s
[Parallel] Sending Data Time: 59.740s
1. Total Time (Σ=6518.746, μ=61.498, σ=9.142)
2. Model Time (Σ=5917.713, μ=55.827, σ=8.112)
3. Matrix Train Build Time (Σ=58.603, μ=0.553, σ=0.113)
4. Matrix Test Build Time (Σ=57.090, μ=0.539, σ=0.115)
5. Predict Time (Σ=357.093, μ=3.369, σ=1.331)
6. Garbage Collector Time (Σ=15.051, μ=0.142, σ=0.040)
7. Metric (Σ=77.781147, μ=0.733784, σ=0.000353)
R socket:
[Parallel] 58 Process(es) Creation Time: 8.146s
[Parallel] Sending Hardware Specifications Time: 0.007s
[Parallel] Sending Data Time: 1.428s
1. Total Time (Σ=6774.940, μ=63.915, σ=20.778)
2. Model Time (Σ=6126.667, μ=57.799, σ=18.843)
3. Matrix Train Build Time (Σ=52.500, μ=0.495, σ=0.154)
4. Matrix Test Build Time (Σ=48.795, μ=0.460, σ=0.110)
5. Predict Time (Σ=320.306, μ=3.022, σ=1.262)
6. Garbage Collector Time (Σ=14.658, μ=0.138, σ=0.048)
7. Metric (Σ=77.781523, μ=0.733788, σ=0.000356)
For information, in R you can run MPI jobs if you hit the 125 communication socket limit.
However, the work is spread differently, it must be taken into account. The work could be more realistic using MPI, but its performance for sharing data is another story.
MPI chart behavior:
To use MPI, in R, replace the following, make sure to add the MPI type to the cluster spawn:
BIG WARNING: You should close the MPI cluster with
Rmpi::mpi.exit(), otherwise it crashes (it could still crash withRmpi::mpi.exit()). I would recommend to use that command only at the very end (don't usestopCluster(cl)), and to check afterwards if there are any zombie R processes left in the wild.Due to how MPI and R socket spreads the work at the beginning, the timings are entirely different from 1 to 7 (except the "[Parallel"] part which is creating processes and sending data to each process). MPI spawns processes faster than R sockets, however it is faster to communicate to R socket processes than MPI processes. The huge difference in sending data might be also because we are using serialization v3 in R, which is the default now in R 3.6.
MPI:
R socket: