how to parallelize a large for loop in JAX

[kchenhyd]

Dear all,

I'm working with a JAX implementation that contains a large for loop (exceeding 1 million iterations). The operations within this loop—specifically, calls to multiple JIT-compiled functions—are independent. I'm curious whether JAX provides functionality similar to Numba's 'prange' for parallelizing the loop. While I recognize that MPI4JAX could be a potential solution by distributing the workload across multiple CPUs/GPUs, my resources are limited to fewer than 100 CPUs and a single GPU. It would be ideal if I could leverage the GPU cores (e.g., the 16,384 cores available on an RTX 4090) for parallel execution of the for loop. Any insights or suggestions would be greatly appreciated.

Thank you!

[jakevdp]

If you could provide a minimal reproducible example of the type of operation you're trying to do, it would be much easier to answer this question. The best approach here depends on the details.

(I also saw what I assume is your Stackoverflow question on the same topic, but someone there already asked for a minimal reproducible example, and so I was waiting for your update).

[jakevdp]

(sorry to keep harping on this, but the details do matter, and so far I do not have enough information to be able to answer your question)

[kchenhyd]

All the calculation are done in JAX and only the input data is in xarray. Sure. I will try to prepare a minimum example using xarray as inputs and JAX for computation. Thanks.

[jakevdp]

Broadly the answer will be to use lax.map over your full computation, and tune batch_size to take advantage of available device memory given the size of your problem. If you have multiple devices or hosts, you can switch to shard_map (or possibly wrap shard_map outside lax.map) to parallelize the computation across devices.

[jakevdp]

There are some resources for getting started with sharded/parallel computation here: https://jax.readthedocs.io/en/latest/sharded-computation.html

[kchenhyd]

Thanks! I haven't tried lax.map yet. I'll test to see if it can parallelize the loop.