Edits from Gail

docs/papers/joss/paper.md

@@ -41,12 +41,12 @@ Therefore, many applications will struggle to efficiently use emerging
 extreme-scale high-performance, parallel, and distributed systems.
 libEnsemble is a complete Python toolkit and workflow system for intelligently driving
 *ensembles* of experiments or simulations at massive scales.
-It enables and encourages multi-disciplinary design, decision, and inference
+It enables and encourages multidisciplinary design, decision, and inference
 studies portably running on laptops, clusters, and supercomputers.
 
 # Statement of Need
 
-While there is a growing number of packages aimed at workflows, relatively few
+While a growing number of packages are aimed at workflows, relatively few
 focus on running dynamic ensembles of calculations on clusters and supercomputers.
 Dynamic ensembles are workflows of computations that are defined and steered
 based on intermediate results.
@@ -56,19 +56,19 @@ these examples, the ensemble members are typically simulations that use differen
 parameters or data. Additional examples of applications that have used libEnsemble are
 surveyed in [Representative libEnsemble Use Cases](#Representative-libEnsemble-Use-Cases).
 
-Some key considerations for packages running dynamic ensembles include:
+Key considerations for packages running dynamic ensembles include the following:
 
-- Ease of use -- whether the software requires a complex setup.
+- Ease of use -- whether the software requires a complex setup
 
-- Portability -- running on diverse machines with different schedulers, hardware, and communication modes (e.g., MPI runners) with minimal modification to user scripts.
+- Portability -- running on diverse machines with different schedulers, hardware, and communication modes (e.g., MPI runners) with minimal modification to user scripts
 
-- Scalability -- working efficiently with large-scale and/or many concurrent simulations.
+- Scalability -- working efficiently with large-scale and/or many concurrent simulations
 
-- Interoperability -- the modularity of the package and the ability to interoperate with other packages.
+- Interoperability -- the modularity of the package and the ability to interoperate with other packages
 
-- Adaptive resource management -- the ability to adjust resources given to each simulation throughout the ensemble.
+- Adaptive resource management -- the ability to adjust resources given to each simulation throughout the ensemble
 
-- Efficient resource utilization -- including the ability to cancel simulations on the fly.
+- Efficient resource utilization -- including the ability to cancel simulations on the fly
 
 libEnsemble seeks to satisfy the above criteria using a generator--simulator--allocator
 model. libEnsemble's generators, simulators, and allocators -- commonly referred to as
@@ -85,7 +85,7 @@ and communicate via the manager.
 
 Other packages for managing workflows and ensembles include Colmena [@colmena21] and the
 RADICAL-Ensemble Toolkit [@ensembletoolkit16] as well as packages such as Parsl
-[@parsl] and Balsam [@Salim2019] that provide back-end dispatch and execution.
+[@parsl] and Balsam [@Salim2019] that provide backend dispatch and execution.
 
 libEnsemble's unique generator--simulator--allocator
 paradigm eliminates the need for users to explicitly define task dependencies.
@@ -98,17 +98,17 @@ readily choose an existing generator function and tailor a simulator function
 to their particular needs.
 
 libEnsemble takes the philosophy of minimizing required dependencies while
-supporting various back-end mechanisms when needed.
+supporting various backend mechanisms when needed.
 In contrast to other packages that cover only a
 subset of such a workflow,
 libEnsemble is a complete toolkit that includes generator-in-the-loop and
 backend mechanisms.
-For example, Colmena uses front-end components to create and
+For example, Colmena uses frontend components to create and
 coordinate tasks while using Parsl to dispatch simulations.
 
 For example, the vast majority of current use cases do not require a database or
-special run-time environment. For use cases that have such requirements, Balsam
-can be used on the back end by
+special runtime environment. For use cases that have such requirements, Balsam
+can be used on the backend by
 substituting the regular MPI executor for the Balsam executor. This approach
 simplifies the user experience and reduces the initial setup and adoption costs
 when using libEnsemble.
@@ -139,32 +139,32 @@ GPUs can also be specified for each simulation.
 The close coupling between the libEnsemble generators and simulators enables a
 generator to perform tasks such as asynchronously receiving results, updating
 models, and canceling previously initiated simulations. Simulations that are
-already running can be terminated and resources recovered. This is more
-flexible compared to other packages, where the generation of simulations is
+already running can be terminated and resources recovered. This approach is more
+flexible compared with other packages, where the generation of simulations is
 external to the dispatch of a batch of simulations.
 
-libEnsemble also supports so-called "persistent user functions" that
+libEnsemble also supports "persistent user functions" that
 maintain their state while running on workers. This prevents the need to
 store and reload data
 as is done by other ensemble packages that support only a fire-and-forget
 approach to ensemble components.
 
 # Representative libEnsemble Use Cases
 
-Examples of libEnsemble applications in science and engineering include:
+Examples of libEnsemble applications in science and engineering include the following:
 
-- Optimization of variational algorithms on quantum computers [@Liu2022layer].
-- Parallelization of the ParMOO solver for multiobjective simulation optimization problems [@ParMOODesign23].
-- Design of particle accelerators [@Neveu2023] [@PhysRevAccelBeams.26.084601] [@Pousa22].
-- Sequential Bayesian experimental design [@Surer2023] and Bayesian calibration [@MCMPSW2022].
+- Optimization of variational algorithms on quantum computers [@Liu2022layer]
+- Parallelization of the ParMOO solver for multiobjective simulation optimization problems [@ParMOODesign23]
+- Design of particle accelerators [@Neveu2023] [@PhysRevAccelBeams.26.084601] [@Pousa22]
+- Sequential Bayesian experimental design [@Surer2023] and Bayesian calibration [@MCMPSW2022]
 
 A selection of community-provided libEnsemble functions and workflows that
 users can build off is maintained in [@libEnsembleCommunityExamples].
 
 Additional details on the parallel features and scalability of libEnsemble can
 be found in [@Hudson2022] and [@libensemble-man].
 
-# Acknowledgements
+# Acknowledgments
 
 We acknowledge contributions from David Bindel.
 This article was supported in part by the PETSc/TAO activity within the U.S.