Performance advice for Jump simulation tutorial

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/conservation_laws/) 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_guide/#types_of_jumps), [automatic construction of dependency graphs for jump systems](https://docs.sciml.ai/Catalyst/stable/model_simulation/jump_simulation_guide/#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/pages.jl

@@ -32,6 +32,7 @@ pages = Any[
         "model_simulation/ensemble_simulations.md",
         "model_simulation/ode_simulation_performance.md",
         "model_simulation/sde_simulation_performance.md",
+        "model_simulation/jump_simulation_guide.md",
         "Model simulation examples" => Any[
             "model_simulation/examples/periodic_events_simulation.md"
         ]

docs/src/index.md

@@ -27,7 +27,7 @@ etc).
 - [Conservation laws can be detected and utilized](@ref conservation_laws) 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_guide_jump_types), [automatic construction of dependency graphs for jump systems](@ref jump_simulation_guide_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.