Local parallel processing

To run an analysis in parallel, you first need to load the Distributed package and add a few worker processes. For example, you may start 5 local processes (that may utilize 5 CPUs) like this:

using Distributed
`Distributed.jl` installation

Distributed.jl usually comes pre-installed with Julia distribution, but you may still need to "enable" it by typing ] add Distributed.

You may check that the workers are really there, using workers(). In this case, it should give you a vector of worker IDs, very likely equal to [2,3,4,5,6].

Each of the processes contains a self-sufficient image of Julia that can act independently; in turn the additional processes also consume some memory. Each process with loaded COBREXA.jl and a simple solver such as GLPK may consume around 500MB of RAM, which should be taken into account when planning the analysis scale.

Packages (COBREXA and your selected solver) must be loaded at all processes, which you can ensure using the "everywhere" macro:

@everywhere using COBREXA, GLPK

Utilizing the prepared worker processes is then straightforward: You pass the list of workers to the selected analysis function using the workers keyword argument, and the parallel processing is automatically orchestrated for you.

model = load_model("e_coli_core.xml")
result = flux_variability_analysis(model, GLPK.Optimizer; workers=workers())