docs fix

docs/src/basics/distributed.md

@@ -1,7 +1,7 @@
 # Parallel simulations
 
 `HetaSimulator` supports parallel simulations on a single machine and in a distributed (cluster) environment. It can be achieved by setting up `Julia workers`.
-`Distributed` package can be used to start workers on a local machine and [ClusterManagers](https://github.com/JuliaParallel/ClusterManagers.jl) package supports a number of job queue systems (SGE, PBS, HTCondor, etc). In the following examples we will use `Distributed` package to start `workers`. `HetaSimulator` implementation of parallel simulations relies on [`SciML Ensemble Simulations`](https://diffeq.sciml.ai/stable/features/ensemble/) features and inherits [EnsembleAlgorithms](https://diffeq.sciml.ai/stable/features/ensemble/#EnsembleAlgorithms) choice. Parallelization algorithm is defined by `parallel_type` keyword argument. Currently it supports the following options:
+`Distributed` package can be used to start workers on a local machine and [ClusterManagers](https://github.com/JuliaParallel/ClusterManagers.jl) package supports a number of job queue systems (SGE, PBS, HTCondor, etc). In the following examples we will use `Distributed` package to start `workers`. `HetaSimulator` implementation of parallel simulations relies on [SciML Ensemble Simulations](https://diffeq.sciml.ai/stable/features/ensemble/) features and inherits [EnsembleAlgorithms](https://diffeq.sciml.ai/stable/features/ensemble/#EnsembleAlgorithms) choice. Parallelization algorithm is defined by `parallel_type` keyword argument. Currently it supports the following options:
 - `EnsembleSerial()` - No parallelism. The default. 
 - `EnsembleThreads()` - This uses multithreading. It's local (single computer, shared memory) parallelism only.
 - `EnsembleDistributed()` - Uses pmap internally. It will use as many processors as you have Julia processes.