From 82f7bac1a02243854bc26d004a5dd45527850451 Mon Sep 17 00:00:00 2001 From: Ivan Date: Tue, 1 Mar 2022 00:50:12 +0300 Subject: [PATCH] docs fix --- docs/src/basics/distributed.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/src/basics/distributed.md b/docs/src/basics/distributed.md index b9ca3d0..1c50a19 100644 --- a/docs/src/basics/distributed.md +++ b/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.