update internal references

README.md

@@ -58,7 +58,7 @@ be found in its corresponding research paper, [Catalyst: Fast and flexible model
 - [Conservation laws can be detected and utilized](https://docs.sciml.ai/Catalyst/stable/model_creation/network_analysis/#network_analysis_deficiency) to reduce system sizes, and to generate non-singular Jacobians (e.g. during conversion to ODEs, SDEs, and steady state equations).
 - Catalyst reaction network models can be [coupled with differential and algebraic equations](https://docs.sciml.ai/Catalyst/stable/model_creation/constraint_equations/) (which are then incorporated during conversion to ODEs, SDEs, and steady state equations).
 - Models can be [coupled with events](https://docs.sciml.ai/Catalyst/stable/model_creation/constraint_equations/#constraint_equations_events) that affect the system and its state during simulations.
-- By leveraging ModelingToolkit, users have a variety of options for generating optimized system representations to use in solvers. These include construction of [dense or sparse Jacobians](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_sparse_jacobian), [multithreading or parallelization of generated derivative functions](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_parallelisation), [automatic classification of reactions into optimized jump types for Gillespie type simulations](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#jump_types), [automatic construction of dependency graphs for jump systems](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#Jump-Aggregators-Requiring-Dependency-Graphs), and more.
+- By leveraging ModelingToolkit, users have a variety of options for generating optimized system representations to use in solvers. These include construction of [dense or sparse Jacobians](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_sparse_jacobian), [multithreading or parallelization of generated derivative functions](https://docs.sciml.ai/Catalyst/stable/model_simulation/ode_simulation_performance/#ode_simulation_performance_parallelisation), [automatic classification of reactions into optimized jump types for Gillespie type simulations](https://docs.sciml.ai/Catalyst/stable/model_simulation/jump_simulation_performance/#types_of_jumps), [automatic construction of dependency graphs for jump systems](https://docs.sciml.ai/Catalyst/stable/model_simulation/jump_simulation_performance/#jump_solver_selection), and more.
 - [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) symbolic expressions and Julia `Expr`s can be obtained for all rate laws and functions determining the deterministic and stochastic terms within resulting ODE, SDE, or jump models.
 - [Steady states](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/homotopy_continuation/) (and their [stabilities](https://docs.sciml.ai/Catalyst/stable/steady_state_functionality/steady_state_stability_computation/)) can be computed for model ODE representations.
 

docs/src/index.md

@@ -27,7 +27,7 @@ etc).
 - [Conservation laws can be detected and utilized](@ref network_analysis_deficiency) to reduce system sizes, and to generate non-singular Jacobians (e.g. during conversion to ODEs, SDEs, and steady state equations).
 - Catalyst reaction network models can be [coupled with differential and algebraic equations](@ref constraint_equations_coupling_constraints) (which are then incorporated during conversion to ODEs, SDEs, and steady state equations).
 - Models can be [coupled with events](@ref constraint_equations_events) that affect the system and its state during simulations.
-- By leveraging ModelingToolkit, users have a variety of options for generating optimized system representations to use in solvers. These include construction of [dense or sparse Jacobians](@ref ode_simulation_performance_sparse_jacobian), [multithreading or parallelization of generated derivative functions](@ref ode_simulation_performance_parallelisation), [automatic classification of reactions into optimized jump types for Gillespie type simulations](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#jump_types), [automatic construction of dependency graphs for jump systems](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#Jump-Aggregators-Requiring-Dependency-Graphs), and more.
+- By leveraging ModelingToolkit, users have a variety of options for generating optimized system representations to use in solvers. These include construction of [dense or sparse Jacobians](@ref ode_simulation_performance_sparse_jacobian), [multithreading or parallelization of generated derivative functions](@ref ode_simulation_performance_parallelisation), [automatic classification of reactions into optimized jump types for Gillespie type simulations](@ref jump_simulation_performance_jump_types), [automatic construction of dependency graphs for jump systems](@ref jump_simulation_performance_solver_selection), and more.
 - [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) symbolic expressions and Julia `Expr`s can be obtained for all rate laws and functions determining the deterministic and stochastic terms within resulting ODE, SDE, or jump models.
 - [Steady states](@ref homotopy_continuation) (and their [stabilities](@ref steady_state_stability)) can be computed for model ODE representations.
 

docs/src/model_simulation/jump_simulation_performance.md

@@ -141,7 +141,7 @@ promoter = @reaction_network begin
 end
 ```
 Let us simulate this model and consider the copy numbers of each individual component:
-```@example jump_simulation_performance_2
+```@example jump_simulation_performance_3
 using JumpProcesses, Plots # hide
 u0 = [:Pᵢ => 1, :Pₐ => 0, :M => 10000]
 tspan = (0.0, 5000.0)

docs/src/model_simulation/simulation_introduction.md

@@ -1,7 +1,7 @@
 # [Model Simulation Introduction](@id simulation_intro)
 Catalyst's core functionality is the creation of *chemical reaction network* (CRN) models that can be simulated using ODE, SDE, and jump simulations. How such simulations are carried out has already been described in [Catalyst's introduction](@ref introduction_to_catalyst). This page provides a deeper introduction, giving some additional background and introducing various simulation-related options. 
 
-Here we will focus on the basics, with other sections of the simulation documentation describing various specialised features, or giving advice on performance. Anyone who plans on using Catalyst's simulation functionality extensively is recommended to also read the documentation on [solution plotting](@ref simulation_plotting), and on how to [interact with simulation problems, integrators, and solutions](@ref simulation_structure_interfacing). Anyone with an application for which performance is critical should consider reading the corresponding page on performance advice for [ODEs](@ref ode_simulation_performance) or [SDEs](@ref sde_simulation_performance).
+Here we will focus on the basics, with other sections of the simulation documentation describing various specialised features, or giving advice on performance. Anyone who plans on using Catalyst's simulation functionality extensively is recommended to also read the documentation on [solution plotting](@ref simulation_plotting), and on how to [interact with simulation problems, integrators, and solutions](@ref simulation_structure_interfacing). Anyone with an application for which performance is critical should consider reading the corresponding page on performance advice for [ODE](@ref ode_simulation_performance), [SDE](@ref sde_simulation_performance), or [jump](@ref jump_simulation_performance) simulations.
 
 ### [Background to CRN simulations](@id simulation_intro_theory)
 This section provides some brief theory on CRN simulations. For details on how to carry out these simulations in actual code, please skip to the following sections.
@@ -290,7 +290,7 @@ While the `@default_noise_scaling` option is unavailable for [programmatically c
 
 ## [Performing jump simulations using stochastic chemical kinetics](@id simulation_intro_jumps)
 
-Catalyst uses the [JumpProcesses.jl](https://github.com/SciML/JumpProcesses.jl) package to perform jump simulations. This section provides a brief introduction, with [JumpProcesses's documentation](https://docs.sciml.ai/JumpProcesses/stable/) providing a more extensive description.
+Catalyst uses the [JumpProcesses.jl](https://github.com/SciML/JumpProcesses.jl) package to perform jump simulations. This section provides a brief introduction, with [JumpProcesses's documentation](https://docs.sciml.ai/JumpProcesses/stable/) providing a more extensive description. A dedicated section giving advice on how to optimise jump simulation performance can be found [here](@ref jump_simulation_performance).
 
 Jump simulations are performed using so-called `JumpProblem`s. Unlike ODEs and SDEs (for which the corresponding problem types can be created directly), jump simulations require first creating an intermediary `DiscreteProblem`. In this example, we first declare our two-state model and its initial conditions, time span, and parameter values.
 ```@example simulation_intro_jumps