# What happened to XLA.jl

**URL:** <https://discourse.julialang.org/t/what-happened-to-xla-jl/88088>\
**Category:** Machine Learning\
**Created:** [October 1, 2022, 4:03pm UTC](https://discourse.julialang.org/t/what-happened-to-xla-jl/88088 "2022-10-01T16:03:31Z")\
**Posts on this page:** 1\
**Showing post:** 8

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**Author:** ![jar1](https://avatars.discourse-cdn.com/v4/letter/j/c0e974/32.png) [@jar1](https://discourse.julialang.org/u/jar1)\
**Post date:** [October 2, 2022, 10:39pm UTC](https://discourse.julialang.org/t/what-happened-to-xla-jl/88088/8 "2022-10-02T22:39:54Z")

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Related issues

> [@Julia's Broadcast vs Jax's vmap](https://discourse.julialang.org/t/julias-broadcast-vs-jaxs-vmap/38990):
>
> My (high-level) understanding of jax.vmap is that it automatically vectorizes vectorizes a function along a specified axis of its input by introducing an “abstract” axis and compiling the code as though the inputs were shaped accordingly. To see the difference let’s consider a very simple example where Julia’s broadcasting is much less performant than jax.vmap. Let’s consider how jax internally represents vector-vector dot products: import jax.numpy as np from jax.api import jit, vmap from j…

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