# Future directions for DataFrames.jl

**URL:** <https://discourse.julialang.org/t/future-directions-for-dataframes-jl/66247>\
**Category:** Data\
**Tags:** package, dataframes\
**Created:** [August 12, 2021, 6:23am UTC](https://discourse.julialang.org/t/future-directions-for-dataframes-jl/66247 "2021-08-12T06:23:11Z")\
**Posts on this page:** 1\
**Showing post:** 23

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**Author:** ![tkf](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tkf/32/17635_2.png) [@tkf](https://discourse.julialang.org/u/tkf)\
**Post date:** [August 12, 2021, 8:00pm UTC](https://discourse.julialang.org/t/future-directions-for-dataframes-jl/66247/23 "2021-08-12T20:00:48Z")

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> [@viralbshah](#):
>
> One question is, whether using abstractions like KernelAbstractions or Dagger, we can target more hardware configurations with the same codebase?

This is essentially what I discussed in [[ANN] Folds.jl: threaded, distributed, and GPU-based high-level data-parallel interface for Julia](https://discourse.julialang.org/t/ann-folds-jl-threaded-distributed-and-gpu-based-high-level-data-parallel-interface-for-julia/54701) and

[![](https://global.discourse-cdn.com/julialang/original/3X/6/2/6254dfcced4fd4d5737004d06c5e6aafc91f7e82.jpeg "JuliaFolds: Structured parallelism for Julia | Takafumi Arakaki | JuliaCon2021") ](https://www.youtube.com/watch?v=TodjM8gCylI)

KernelAbstractions and Dagger are great in that they are very flexible and generic. But having more higher-level _structured_ representation helps for lowering to target-specific programs. As always, adding more constraints can help for encoding structures of your code which then can be exploited by the underlying framework for performance and composability.

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