[nickpsecurity]

Thanks for joining us! I enjoyed studying Chapel a while back. I have a few questions about it.

Most AI in the press is done on expensive NVIDIA’s. Many papers have techniques with lower cost or higher effectiveness. Their algorithms are described in high-level form with little or limited implementation. Many in OSS and non-DL are using smaller models that can run on diverse hardware, if one has expertise to program it. It would be helpful to have a language that maps high-level techniques in papers to diverse hardware for use in training or review.

Can Chapel currently implement the concepts in papers on NN’s and LLM’s to run on multicore, clusters, and GPU’s? If so, can it implement hybrids where specific functions are GPU optimized but the overall design is split across machines? If not, what is missing for using Chapel for rapid prototyping of AI concepts?

[bradcray]

These are great questions, and ones we’re very curious about as well. I don’t believe that our current Chapel team has much experience programming NNs and LLMs, having focused on other areas. That said, I’m also not aware of any intrinsic barriers to implementing such algorithms in a portable way within Chapel, potentially calling out to vendor-optimized implementations when available and appropriate.

If you, or others, would be interested in exploring this topic, we’d be very interested in either partnering with you or supporting your efforts.

(Also see Engin's response about programming tensor cores for some thematically related thoughts: https://news.ycombinator.com/item?id=39020703 )