> Our implementation uses Highway's portable SIMD functions, so we do not have to re-implement about 3,000 lines of C++ for each platform.
Would they do the same thing today or have an LLM re-implement those 3000 lines of c++ ?
Would they do the same thing today or have an LLM re-implement those 3000 lines of c++ ?
I think software engineering in general is in a bit of a discoverability crisis. So many problems actually have solutions implemented... Somewhere. If you know about them. And are speaking the same vocabulary as the original implementer to realize the solution might be applicable to your problem. It's one of the reasons that jokes exist about microservice frameworks (https://www.youtube.com/watch?v=y8OnoxKotPQ) and how "We use Hadoop to store the output from our Kafka pipe, that's populated from our Traefik layer, all monitored with Grafana in front of Loki and Prometheus, of course" is a real sentence that has actual meaning and not a fever-dream.
LLMs are actually pretty impressive at being able to pull together disparate information from various domains into one place.
That's what I mean about "discoverability crisis." This post is how I learned about the existence of the SIMD library.
I'd absolutely still use Highway, and do. My experience is that even two separate implementations diverge over time and I'd have low confidence in bringing the same updates and improvements to all, even with LLM assistance.
Our programming model is 1) an agent+human to generate the algorithmic approach, 2) a C++ library (Highway) to translate to intrinsics, while filling in gaps + allowing customization, 3) a compiler to generate the actual code with some optimizations.
Asking the compiler to do #1 is a pipe dream: compiler friends tell me they are not going to devise new shuffles/data layouts (like what VQSort does). Conflating #2 and #3 means a custom compiler/IR which has high engineering costs (ABI boundaries, hard to debug/profile/sanitize). And doing #3 at runtime (JIT), or moving fusions into #3 (MLIR), vastly complicates the compiler. We can still get runtime adaptability thanks to Highway's multi-target support. Fusion has been much easier to implement manually for LLMs than to construct a general fusion infrastructure. Templates hide most data type differences and we see 2-5x speedup vs llama.cpp for 128k prefill+batch decode on Zen5.
Instead of requiring a compiler to do heroic transforms at runtime, and get it right every time, we can do all kinds of agentic exploration, then verify the result/approach, check in the source code, then we 'just' have a C++ compiler afterwards. And if/when something breaks, it's easy to update centrally, in code we can modify directly, rather than indirectly via updating a compiler.