#discrete-event #systems #real-time #cyberphysical

asynchronix

A high performance asychronous compute framework for system simulation

2 unstable releases

0.2.0 Aug 15, 2023
0.1.0 Jan 16, 2023

#19 in Simulation

45 downloads per month

MIT/Apache

410KB
6.5K SLoC

Asynchronix

Asynchronix is a developer-friendly, highly optimized discrete-event simulation framework written in Rust. It is meant to scale from small, simple simulations to very large simulation benches with complex time-driven state machines.

Cargo Documentation License

Overview

Asynchronix is a simulator that leverages asynchronous programming to transparently and efficiently auto-parallelize simulations by means of a custom multi-threaded executor.

It promotes a component-oriented architecture that is familiar to system engineers and closely resembles flow-based programming: a model is essentially an isolated entity with a fixed set of typed inputs and outputs, communicating with other models through message passing via connections defined during bench assembly.

Although the main impetus for its development was the need for simulators able to handle large cyberphysical systems, Asynchronix is a general-purpose discrete-event simulator expected to be suitable for a wide range of simulation activities. It draws from experience on spacecraft real-time simulators but differs from existing tools in the space industry in a number of respects, including:

  1. performance: by taking advantage of Rust's excellent support for multithreading and asynchronous programming, simulation models can run efficiently in parallel with all required synchronization being transparently handled by the simulator,
  2. developer-friendliness: an ergonomic API and Rust's support for algebraic types make it ideal for the "cyber" part in cyberphysical, i.e. for modelling digital devices with even very complex state machines,
  3. open-source: last but not least, Asynchronix is distributed under the very permissive MIT and Apache 2 licenses, with the explicit intent to foster an ecosystem where models can be easily exchanged without reliance on proprietary APIs.

Documentation

The API documentation is relatively exhaustive and includes a practical overview which should provide all necessary information to get started.

More fleshed out examples can also be found in the dedicated directory.

Usage

Add this to your Cargo.toml:

[dependencies]
asynchronix = "0.2.0"

Example

// A system made of 2 identical models.
// Each model is a 2× multiplier with an output delayed by 1s.
//
//              ┌──────────────┐      ┌──────────────┐
//              │              │      │              │
// Input ●─────▶│ multiplier 1 ├─────▶│ multiplier 2 ├─────▶ Output
//              │              │      │              │
//              └──────────────┘      └──────────────┘
use asynchronix::model::{Model, Output};
use asynchronix::simulation::{Mailbox, SimInit};
use asynchronix::time::{MonotonicTime, Scheduler};
use std::time::Duration;

// A model that doubles its input and forwards it with a 1s delay.
#[derive(Default)]
pub struct DelayedMultiplier {
    pub output: Output<f64>,
}
impl DelayedMultiplier {
    pub fn input(&mut self, value: f64, scheduler: &Scheduler<Self>) {
        scheduler
            .schedule_event(Duration::from_secs(1), Self::send, 2.0 * value)
            .unwrap();
    }
    async fn send(&mut self, value: f64) {
        self.output.send(value).await;
    }
}
impl Model for DelayedMultiplier {}

// Instantiate models and their mailboxes.
let mut multiplier1 = DelayedMultiplier::default();
let mut multiplier2 = DelayedMultiplier::default();
let multiplier1_mbox = Mailbox::new();
let multiplier2_mbox = Mailbox::new();

// Connect the output of `multiplier1` to the input of `multiplier2`.
multiplier1
    .output
    .connect(DelayedMultiplier::input, &multiplier2_mbox);

// Keep handles to the main input and output.
let mut output_slot = multiplier2.output.connect_slot().0;
let input_address = multiplier1_mbox.address();

// Instantiate the simulator
let t0 = MonotonicTime::EPOCH; // arbitrary start time
let mut simu = SimInit::new()
    .add_model(multiplier1, multiplier1_mbox)
    .add_model(multiplier2, multiplier2_mbox)
    .init(t0);

// Send a value to the first multiplier.
simu.send_event(DelayedMultiplier::input, 3.5, &input_address);

// Advance time to the next event.
simu.step();
assert_eq!(simu.time(), t0 + Duration::from_secs(1));
assert_eq!(output_slot.take(), None);

// Advance time to the next event.
simu.step();
assert_eq!(simu.time(), t0 + Duration::from_secs(2));
assert_eq!(output_slot.take(), Some(14.0));

Implementation notes

Under the hood, Asynchronix is based on an asynchronous implementation of the actor model, where each simulation model is an actor. The messages actually exchanged between models are async closures which capture the event's or request's value and take the model as &mut self argument. The mailbox associated to a model and to which closures are forwarded is the receiver of an async, bounded MPSC channel.

Computations proceed at discrete times. When executed, models can request the scheduler to send an event (or rather, a closure capturing such event) at a certain simulation time. Whenever computations for the current time complete, the scheduler selects the nearest future time at which one or several events are scheduled (next event increment), thus triggering another set of computations.

This computational process makes it difficult to use general-purposes asynchronous runtimes such as Tokio, because the end of a set of computations is technically a deadlock: the computation completes when all model have nothing left to do and are blocked on an empty mailbox. Also, instead of managing a conventional reactor, the runtime manages a priority queue containing the posted events. For these reasons, Asynchronix relies on a fully custom runtime.

Even though the runtime was largely influenced by Tokio, it features additional optimizations that make its faster than any other multi-threaded Rust executor on the typically message-passing-heavy workloads seen in discrete-event simulation (see benchmark). Asynchronix also improves over the state of the art with a very fast custom MPSC channel, which performance has been demonstrated through Tachyonix, a general-purpose offshoot of this channel.

License

This software is licensed under the Apache License, Version 2.0 or the MIT license, at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

Dependencies

~0.5–31MB
~418K SLoC