Parallel execution

An MDDF calculation can be performed in parallel, using many processors of a single computer. The speedup is almost linear, as the parallelization is performed by splitting the calculation of each frame in each processor, in an asynchronous manner. To run the computation in parallel, just define the number of threads (processors) to be used, by defining the JULIA_NUM_THREADS environment variable:

export JULIA_NUM_THREADS=4

or, which is more simple in Julia 1.5 or greater, just start julia with:

julia -t 4 

To directly run a script, use

julia -t 4 example.jl

Note

The number of threads used for computation of the MDDF is the number of defined threads minus one, because one thread is dedicated to control the execution. Since the control of the execution is not very demanding, particularly if the number of threads is small, you may want to set the number of threads as one more than you originally intended, if the total number of threads is not very large. In particular, running with only -t 2 will not parallelize the calculation at all.

Warning

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=0.1.

Unfortunately, this may slow the calculations quite a bit, and the parallelization to many processors becomes not very satisfactory. We are working to improve this.