From 68bc0f85c0a2817da9038763e1681de337072a47 Mon Sep 17 00:00:00 2001 From: Erik van Sebille Date: Tue, 15 Sep 2026 08:16:32 +0200 Subject: [PATCH 1/2] Update wording for ChunkCachedArray in performance guide --- docs/user_guide/examples/explanation_performance.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/user_guide/examples/explanation_performance.md b/docs/user_guide/examples/explanation_performance.md index 873995c4e..787979f60 100644 --- a/docs/user_guide/examples/explanation_performance.md +++ b/docs/user_guide/examples/explanation_performance.md @@ -40,7 +40,7 @@ This will make Parcels use `numpy` functions in the interpolation routines, whic _Uses Parcels Backend: ChunkCachedArray_ -**Best for: large Datasets (more than a few GB) and particles distributed over the entire domain** +**Best for: large Datasets (more than a few GB) and particles in only a small region of the domain** If your Dataset is so large that it doesn't fit into memory, you can use the {py:func}`parcels.FieldSet.to_chunk_cached_arrays()`. This constructs a cache where individual (dask) chunks of data are stored. From 4d03fb5408b804e4e25c0dc9516aac3939725d7b Mon Sep 17 00:00:00 2001 From: Erik van Sebille Date: Thu, 17 Sep 2026 08:28:22 +0200 Subject: [PATCH 2/2] Fix Dask note --- docs/user_guide/examples/explanation_performance.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/user_guide/examples/explanation_performance.md b/docs/user_guide/examples/explanation_performance.md index 787979f60..ae6a62a9c 100644 --- a/docs/user_guide/examples/explanation_performance.md +++ b/docs/user_guide/examples/explanation_performance.md @@ -129,7 +129,7 @@ If your Dataset is so large that it doesn't fit into memory, and you have very f | Works out-of-the-box | Only performs well for very small ParticleSets | ```{note} -The long-term plan for Parcels development is to make this Option 4 work well for all cases. However, this will require significant work on Dask indexing. +The long-term plan for Parcels development is to make this last option (using Dask) work well for all cases. However, this will require significant work on Dask indexing. If you have ideas for how to make Parcels faster, we'd love to hear from you! Feel free to [open an issue](https://github.com/Parcels-code/Parcels/issues) or reach out to us on [Zulip](https://clam-community.github.io). ```