# Compute time series for 70,000 locations (Speed up the processing)

**URL:** https://discourse.pangeo.io/t/compute-time-series-for-70-000-locations-speed-up-the-processing/4436
**Category:** Uncategorized
**Created:** [August 21, 2024, 2:13pm UTC](https://discourse.pangeo.io/t/compute-time-series-for-70-000-locations-speed-up-the-processing/4436 "2024-08-21T14:13:12Z")
**Posts on this page:** 1
**Showing post:** 10

<div class="post-metadata">

### Author: ![maawoo](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.pangeo.io/maawoo/32/1972_2.png) [@maawoo](https://discourse.pangeo.io/u/maawoo)
#### Post date: [August 26, 2024, 3:35pm UTC](https://discourse.pangeo.io/t/compute-time-series-for-70-000-locations-speed-up-the-processing/4436/10 "2024-08-26T15:35:17Z")

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I’ve had good experiences with Dask’s P2P shuffling to reduce RAM usage. Might be worth trying by [adjusting the config](https://docs.dask.org/en/stable/configuration.html#directly-within-python):

```python
import dask

dask.config.set({"array.rechunk.method": "p2p"})
dask.config.set({"optimization.fuse.active": False})

```

Not sure if `optimization.fuse.active` needs to be False. I remember that it solved an issue some while ago and it was recommended in a GitHub issue. Might not be necessary anymore.

[Here](https://docs.coiled.io/blog/shuffling-large-data-at-constant-memory.html) is a blog post and [here](https://discourse.pangeo.io/t/rechunking-large-data-at-constant-memory-in-dask-experimental/3266) a thread in the Pangeo Discourse about it.

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