On the lack of progress reports from the Mill project (2022)
millcomputing.com
millcomputing.com
This is a rather leisurely pace.
Their paper on split-stream instruction decode is kind of interesting.[1] It's a statically scheduled very long instruction word machine, like the Intel Itanium. That solves some problems while creating new ones. Most of the ones it solved relate to our of order execution, which, while complicated, works pretty well now. The ones it creates involve trying to make compilers smart enough to optimize the static scheduling. The team working on that at HP for the Itanium was beating their head against the wall in the 2000s, when they gave a talk at Stanford, but maybe it could be solved today.
Also, the niche they hoped to occupy, an alternative to Intel's architecture, is now filled by ARM in its 64-bit form, Apple's CPUs, and maybe RISC-V. They may have missed the window for this product.
This seems kind of oddly dismissive for a project people are interested in. They don’t want to be seen as dead, but weren’t giving any information to counter the narrative other than, eventually, saying something like “we’re not”.
The next paragraph lists a ton of stuff they’ve been working on. Couldn’t they just mention some of that stuff over time? Not big detailed posts (how often to people bring up an environment for a totally new way of doing things? Blog it!), but just a simple little status update here and there.
Still, good to hear they’re still working on it.
I too am very disappointed in the lack of progress. IMHO the window for the Mill ever to achieve any kind of commercial success, even in a niche, has been completely co-opted by RISC-V. That's a shame, because while RISC-V is clean, it isn't innovative. (In particular, I find our continued reliance on page-based memory protection schemes prevents the adoption of new OS paradigms for security. But of course this is for general-purpose computing which, afaik, was never the focus of the Mill project.)
There's a long history of exotic CPU architectures. The only ones that caught on were GPUs. We might see new ones for machine learning, which needs some very specific operations with huge parallelism. GPUs are gaining features for that, like tiny floating point optimized for the 0..1 range. The successful ideas in this space were driven by a specific problem. None were "build it and they will come".
I catch a whiff of con-artistry, and I find it deeply distasteful.