Yes! and....what if the data was mutated separately-similarly (in a fractal sense) in conjunction with use contexts (in the sense of mutable states)? Would that not provide two, separate systems hierarchies for enabling dynamically-threaded processing?
> Date: Fri, 14 Aug 2015 13:16:30 +0800 > Subject: Re: [agi] Design notes for a new parallel computing language > From: b...@goertzel.org > To: a...@listbox.com > > Thanks Andrew! > > I usually ignore the AGI list these days, but today I happened to tune > in, and happened upon this post of your regarding the relation btw > immutability and massive parallelism, from which I actually learned > something ;-) > > -- Ben G > > On Fri, Aug 14, 2015 at 4:17 AM, J. Andrew Rogers <and...@jarbox.org> wrote: > > > >> On Aug 13, 2015, at 12:04 PM, Juan Carlos Kuri Pinto <jck...@gmail.com> > >> wrote: > >> > >> Regarding massive parallelism, Haskell is naturally parallelizable because > >> it is 100% pure. That is, Haskell doesn't have mutating state. You never > >> modify memory locations. You only create new constants and let the garbage > >> collector remove them when they are out of scope. > > > > > > This is an incorrect conception of massive parallelism. The above model > > creates a vast amount of shared, mutable state that is being ignored. It is > > the reason languages like Haskell are poor for massive parallelism in > > practice. > > > > What you describe above is focused on data access parallelism. > > Unfortunately, topological parallelism dominates scalability in massively > > parallel systems. Computing models that rely on immutable memory are > > generally gaining that data access parallelism by reducing the efficiency > > of topological parallelism. > > > > Memory locations are not the only mutable state in real computing systems. > > The wires that move data *between* memory locations are also a dynamic > > shared writable resource, and a relatively scarce one at that. Computing > > models that effect mutability by copying state greatly increase the > > contention for this resource and exhibit strong sublinearity as the > > parallelism increases. > > > > > > The most efficiently parallelizable computing models use non-shared mutable > > state. You basically move functions (which are naturally immutable and > > compact) to the process that owns the state you want to mutate. This allows > > very efficient topological parallelism, and since the state is almost never > > shared between processes, most of the problems immutable memory is designed > > to solve do not apply. It is actually an elegant if somewhat unintuitive > > model in implementation. > > > > Even on single machines, this model typically offers an integer factor > > throughput improvement over lock-free multithreading models of parallelism > > and concurrency (or functional models). > > > > Cheers, > > Andrew > > > > ------------------------------------------- > > AGI > > Archives: https://www.listbox.com/member/archive/303/=now > > RSS Feed: https://www.listbox.com/member/archive/rss/303/212726-deec6279 > > Modify Your Subscription: https://www.listbox.com/member/?&; > > Powered by Listbox: http://www.listbox.com > > > > -- > Ben Goertzel, PhD > http://goertzel.org > > "The reasonable man adapts himself to the world: the unreasonable one > persists in trying to adapt the world to himself. Therefore all > progress depends on the unreasonable man." -- George Bernard Shaw > > > ------------------------------------------- > AGI > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/26941503-0abb15dc > Modify Your Subscription: https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
