<story><title>Translation and accelerated solving of differential equations on GPU platforms</title><url>https://arxiv.org/abs/2304.06835</url></story><parent_chain></parent_chain><comment><author>yablak</author><text>Uhh they time the vmap of the jit on Jax, basically skipping a ton of optimizations,.esp if there is any linear algebra in there.  They also include the cost of building the vmap functional.  Not a valid comparison.&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;utkarsh530&amp;#x2F;GPUODEBenchmarks&amp;#x2F;blob&amp;#x2F;ef807198584e4634c82bfd6a82bb9241c51d56d1&amp;#x2F;GPU_ODE_JAX&amp;#x2F;bench_diffrax.py#L87&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;utkarsh530&amp;#x2F;GPUODEBenchmarks&amp;#x2F;blob&amp;#x2F;ef807198...&lt;/a&gt;</text></comment>
<story><title>Translation and accelerated solving of differential equations on GPU platforms</title><url>https://arxiv.org/abs/2304.06835</url></story><parent_chain></parent_chain><comment><author>towhom</author><text>&amp;quot;Instead of relying on high level array intrinsics that machine learning libraries use, it uses a direct kernel generation approach to greatly reduce the overhead.&amp;quot; Chris Rackauckas on LinkedIn earlier today.</text></comment>