in the current state of technology it's impossible to determine what is derived data that can be easily recomputed and what is essential data without which the current state could not be recomputable.
Can you elaborate more on that statement? I see it as an expensive problem, but not impossible.
If you have a completely deterministic VM/instruction set, you can recompute any function output from any set of inputs. If you have non-deterministic input (network, input devices, randomness, etc) you can store those inputs to replay at a later time.
I don't know if it's always necessary to store non-derived data. Do you need to save all keystrokes? All mouse moves? All network packets? Probably not. If those network packets just result in displaying a graph on the screen, they can probably be thrown away. Maybe you can throw away the headers and just compute based on the packet data. Or maybe you can simply store the fact that a stream of data that matches a cryptographic hash was received. It depends on what function receives that non-derived data.
So I think the difficulty would be designing a system that would isolate derived data and replayable/recomputable/refetchable data, and the processes that compute that data -- and do so with reasonable efficiency. I think this could be done at the process level rather than the instruction set level (though you would have to have a restricted instruction set -- i.e. limited floating-point).
At the process level there are already plenty of programs that work this way and it's exactly the kind of thing the Clojure ecosystem is geared towards.
That's why the Purely Functional OS is essentially a thought experiment in what it would take to extend this to the entire operating system.
When I said "impossible given the current state of technology" I meant exactly what you said in your last paragraph though: impossible without designing an OS that isolates derived data.
I believe such a system will come in the form of persistent data structures coupled with reactive programming but, while I believe it's inevitable in the long run, extending that concept all the way to the OS will be quite the challenge.
Can you elaborate more on that statement? I see it as an expensive problem, but not impossible.
If you have a completely deterministic VM/instruction set, you can recompute any function output from any set of inputs. If you have non-deterministic input (network, input devices, randomness, etc) you can store those inputs to replay at a later time.
I don't know if it's always necessary to store non-derived data. Do you need to save all keystrokes? All mouse moves? All network packets? Probably not. If those network packets just result in displaying a graph on the screen, they can probably be thrown away. Maybe you can throw away the headers and just compute based on the packet data. Or maybe you can simply store the fact that a stream of data that matches a cryptographic hash was received. It depends on what function receives that non-derived data.
So I think the difficulty would be designing a system that would isolate derived data and replayable/recomputable/refetchable data, and the processes that compute that data -- and do so with reasonable efficiency. I think this could be done at the process level rather than the instruction set level (though you would have to have a restricted instruction set -- i.e. limited floating-point).