Parallel execution
It is highly recommended to run MDDF calculations in parallel, using multiple processors of a single computer. To run the computation in parallel, initialize julia with the -t N option, where N is the number of processes to be used. For example, to use 8 parallel processes, use:
julia -t 8 example.jlThe computation will use a number of parallel processes equal to N. Use -t auto to automatically pick the number of threads available in your computer.
The number of threads used for computation of the MDDF is the number of threads available to Julia. Many computers allow hyperthreading, and not necessarily this this beneficial for the execution of this package. The optimal number of threads may vary.
Independently of the number of threads initialized with the -t command-line parameter, the number of processes launched by ComplexMixtures in any given computation can be adjusted by the Options(nthreads=N) option. This won't provide any speedup if the optional number of threads is greater than the number of threads available to Julia at runtime.
If the calculations get Killed by no apparent reason, that is probably because you are running out of memory because of the many parallel computations running. One way to alleviate this problem is to force garbage collection, using
options = Options(GC=true, GC_threshold=0.5)
R = mddf(trajectory, options)
The GC_threshold=0.5 indicates that if the free memory is smaller than 50% of the total memory of the machine, a garbage-collection run will occur. The default parameters are GC=true and GC_threshold=0.3.
Read the predefinition of atom groups section if you are experiencing memory issues.