Oh ya, well, most programming today is akin to the macro programming that was done in the 80s in languages like Visual Basic. There is essentially no difference

Oh ya, well, most programming today is akin to the macro programming that was done in the 80s in languages like Visual Basic. There is essentially no difference between that and the imperative languages like C++, Javascript, PHP, Ruby, Rust, Swift, etc that have captured the market. VHDL and Verilog aren't really imperative languages. They're functional programming (FP) like spreadsheets, Lisp and languages built on FP like Elixir, Clojure, Julia, Haskell, etc but with ugly syntax. Another way of saying that is that a digital circuit is analogous to a spreadsheet (which is FP). So we could use referential transparency and other analyses/optimizations from FP and apply them to circuit analysis like Thevenin and Norton equivalent circuits, but at scale. Sorry the copy-on-write (COW) thing was a little obscure. What I was getting at there is that buzzwords like Map Reduce and big data are hand waving over the central concept of data locality. So for example, a CPU-RAM computer might store a database on an SSD and use a bunch of fancy algorithms to load, cache and process that data. But a multicore computer could slice up the database and store a percentage of the data locally by each core. That's what Map Reduce and Erlang/Go and the Actor model do. At that point, we can use concepts from the web like content-addressable memory and have an abstraction that frees us from the micromanagement of caching things. Basically we wouldn't have to worry about finding the fast path anymore, because with 1000 cores, anything we do is fast. We'd just have to keep the cores fed with data. IMHO this core issue is why multicore computers that connect to a single RAM have generally failed. We need multicore computers with data locality, so that each core kind of acts like it has its own OS or is a node in a cluster. That would allow us to build on supercomputer algorithms that are well-understood. Then with those basic building blocks, things like running genetic algorithms and protein folding and physics simulations become straightforward because we're able to use simple algorithms running in parallel rather than increasingly complex algorithms running serially on CPUs whose performance have stopped improving.