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The Sandcastle of Paul Taraus Interactors (Re: A Case for Impurity: The Applied Pi Calculus)

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Hi,

While Paul Taraus Interactors were somewhere
between stackfull and stackless coroutines,
they were still a castle built on sand.

What was the comms? An interactor was a child
of a parent, and had only two comms, input "next"
and output "the". And there was not much

asynchronizity. But look at the new
library(misc/intercom) that provides ADA
Rendez Vous in Dogelog Player for Java:

:- ensure_loaded(library(misc/intercom)).

producer(C) :-
    between(1,10,X),
    Y is X*X,
    send(C,[Y]),
    fail.
producer(_).

consumer(C) :-
    between(1,10,_),
    recv(C,[Y]),
    write(Y), nl,
    fail.
consumer(_).

As the show case shows, it even works with
cooperating coroutines. I have looked at it
for 5 hours straight, its beautiful:

?- cpu_chan_new(C), create_task(producer(C)),
    create_task(consumer(C)), sleep(1000).
1
4
9
16
25
36
49
64
81
100
C = 0rReference.

Bye

Mild Shock schrieb:
> Hi,
> 
> How it started:
> 
> The Applied Pi Calculus: Mobile Values,
> New Names, and Secure Communication
> Martín Abadi et al. - Google Brain
> https://arxiv.org/abs/1609.03003
> 
> How its going:
> 
> Dynamic Control Flow in
> Large-Scale Machine Learning
> Martín Abadi et al. - Google Brain
> https://research.google/pubs/dynamic-control-flow-in-large-scale-machine-learning/ 
> 
> 
> Have Fun!
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> "Mobile HBM" refers to a new, under-development
>> form of High Bandwidth Memory (HBM) optimized
>> specifically for energy-efficient mobile devices
>> like smartphones. Manufacturers such as Samsung
>> and SK Hynix are developing this technology to
>> make the massive data bandwidth required for complex,
>> on-device AI applications available within the strict
>> power and space constraints of mobile phones.
>>
>> Unlike classic HBM solutions, which rely on
>> extra-wide silicon interposers for massive data
>> throughput in data centers and graphics cards,
>> Mobile HBM utilizes stacked LPDDR architectures.
>> The goal is to drastically reduce power consumption
>> and heat generation so that large AI models can
>> run directly on the device (on-device) without
>> placing too heavy a load on the battery. The
>> technology is expected to hit the market starting
>> in 2026/2027, with Apple among those showing great
>> interest in integrating it into future iPhone generations."
>>
>> https://www.remio.ai/post/mobile-hbm-the-ai-memory-tech-that-could-reshape-the-iphone 
>>
>>
>> Just think of a Mac Neo, which has now 8 GB,
>> but the memory going 3D in 12 layers, thats a
>> massive of 96 GB very fast memory.
>>
>> Economy of scale comes later, when the same CPU/
>> GPU/RAM combo is used for phones, tablets, desktop,
>> or with other humidity and temperature parameters
>>
>> for cars, robots, drones, etc...
>>
>> Bye

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