> For the complete documentation index, see [llms.txt](https://zillion.gitbook.io/iredistrict/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://zillion.gitbook.io/iredistrict/tip-how-to-reduce-workload.md).

# How to reduce workload

Redistricting Optimization is NP-Hard Computation. The more polygons (or the more nodes in the polygons), the heavier the computation. There are several ways to reduce the workload and calculation time.

## Simplify the data

Simplify the polygon can reduce the data size and therefore reduce the workloads. iRedistrict doesn't provide the function to simplify polygons. QGIS can be used to do the simplification. QGIS is free: <https://www.qgis.org/>

## Use cluster to reduce workloads

The clustering function can produce a number of clusters to replace the original units. For example, produce 1000 clusters for 10K units, can reduce the number of polygons from 10K to 1K. Usually, if the data size is larger than&#x20;

![](https://3277503274-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MID4AC0JLzhOjxVzdcB%2F-MJdqpF9pial7h04mt1X%2F-MJdrLiUxxVBQLAaTCdZ%2Fimage.png?alt=media\&token=50e05e9b-f8ba-4be2-9995-baacd04bf9ad)

## Use COI to reduce the workloads

COI groups units into one larger area. It helps to reduce the number of polygons and reduce the workloads.&#x20;

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