DAR's Concurrent and Parallel Computing Desgin

Does DART support M:N threading model?

No, Dart does not support M:N threading model.

Dart is designed to use a 1:1 threading model, which means that each isolate in a Dart program is associated with a single native operating system thread. When a Dart program creates a new isolate, the Dart runtime creates a new thread to handle that isolate's execution.

This design is intended to simplify the programming model for concurrency and parallelism in Dart, as it provides a consistent and predictable threading model that is similar to other programming languages like Java and C#. However, it does have some limitations in terms of scalability and performance, particularly in high-concurrency scenarios.

While Dart does not support an M:N threading model, it does provide a number of other tools for working with concurrency and parallelism, such as asynchronous programming using futures and streams, and the async and await keywords, which make it easy to write non-blocking code. Additionally, Dart provides support for isolates, which can be used to execute code concurrently and in parallel across multiple threads or even across multiple machines, using message passing to communicate between isolates.

In summary, while Dart does not support an M:N threading model, it provides a range of other tools and techniques for working with concurrency and parallelism that are well-suited for the language's design and goals.

What is difference between DART's isolate and OS's thread?

Dart's isolates and an operating system's threads are both mechanisms for achieving concurrency and parallelism, but they have some key differences in their design and behavior.

Dart's isolates are independent workers that run in their own memory space, and each isolate runs on a separate thread. When you create a new isolate in Dart, the runtime creates a new thread to handle that isolate's execution. Isolates provide a way to execute code concurrently and in parallel, while also isolating the state of the program to prevent data races and other synchronization issues. Communication between isolates is typically done via message passing, using the SendPort and ReceivePort classes.

Operating system threads, on the other hand, are lightweight processes that are managed by the operating system. Threads share the same memory space and can access the same data structures, which can lead to synchronization issues such as race conditions and deadlocks. Communication between threads is typically done using shared memory or synchronization primitives such as semaphores and mutexes.

One of the main advantages of Dart's isolates is that they provide a more lightweight and efficient way to achieve concurrency and parallelism compared to operating system threads. This is because isolates run in their own memory space and do not have the same overhead as a full operating system thread. Additionally, isolates provide a way to distribute work across multiple cores or even multiple machines, which can help to scale the performance of a program.

Another advantage of isolates is that they provide a simpler programming model for concurrency and parallelism compared to operating system threads. By isolating the state of the program to prevent data races and synchronization issues, isolates make it easier to reason about the behavior of a program and avoid bugs related to concurrency.

In summary, while Dart's isolates and operating system threads are both mechanisms for achieving concurrency and parallelism, they have different design philosophies and trade-offs. Dart's isolates provide a more lightweight and efficient way to achieve concurrency, while also providing a simpler programming model and enabling easier distribution of work across multiple cores or machines.

Yes, Dart's isolate model has similarities to the actor model.

The actor model is a mathematical model for concurrent computation that was introduced in the 1970s. In the actor model, concurrent units of computation called actors communicate with each other by sending and receiving messages. Actors can create other actors and can perform computations in response to received messages.

Similarly, in Dart's isolate model, isolates communicate with each other by sending and receiving messages, and isolates can create other isolates. Each isolate has its own memory space, and messages are passed between isolates via message passing, which is similar to the way messages are passed between actors in the actor model.

Dart's isolates also share some of the characteristics of the actor model in terms of isolation and immutability. Each isolate runs in its own memory space, which helps to prevent shared mutable state and reduce synchronization issues. Additionally, the message passing system used by isolates makes it easy to send and receive immutable messages between isolates, which helps to ensure that each isolate's state remains isolated and prevents data races.

While Dart's isolate model is not an exact implementation of the actor model, it shares some key principles and design patterns. By providing a simple and efficient way to achieve concurrency and parallelism, Dart's isolate model can help developers to write high-performance and scalable applications that can take advantage of multiple cores and distributed computing environments.

What does DART compute do?

Dart's compute() function provides a way to execute computationally intensive code on a background isolate, without blocking the main thread of a Dart program.

When you call the compute() function, the Dart runtime creates a new isolate to execute the specified function, passing the arguments to the function via message passing. The function is executed on the new isolate, and the result is returned via message passing to the calling isolate. The calling isolate can continue to execute code on the main thread while the function is being executed on the background isolate, which helps to prevent blocking and freezing of the user interface.

The compute() function is part of the dart:async library and is used in conjunction with the Future and Completer classes to perform asynchronous computation. The compute() function is typically used when you have a computationally intensive function that you want to execute on a background thread or isolate, while still being able to interact with the user interface on the main thread.

For example, suppose you have a function that performs a complex calculation and takes a long time to execute. You could use the compute() function to execute that function on a background isolate, like this:

import 'dart:async';

int complexCalculation(int value) {
  // perform a complex calculation on the input value
  // and return the result
}

Future<int> calculateInBackground(int value) async {
  // use the compute() function to execute the complexCalculation()
  // function on a background isolate
  return await compute(complexCalculation, value);
}

In this example, the calculateInBackground() function uses the compute() function to execute the complexCalculation() function on a background isolate, passing in the input value via message passing. The result of the calculation is returned as a Future<int>, which can be awaited to retrieve the result.

By using the compute() function to execute computationally intensive code on a background isolate, you can create high-performance, responsive, and scalable Dart applications that can take advantage of multiple cores and distributed computing environments.

Can you explain in details how DART implement the compute API?

Sure! Here is a more detailed explanation of how Dart's compute() function works.

When you call the compute() function, Dart creates a new isolate to execute the specified function. The new isolate runs the Isolate.spawnUri() method, which loads and executes the Dart code in a new isolate. The code is loaded from a separate isolate entry point file, which must contain the function you want to execute, along with any required dependencies.

Once the new isolate is created, Dart passes the function and its arguments to the new isolate via message passing. The arguments are encoded as JSON and sent to the new isolate using the SendPort.send() method. The new isolate then decodes the JSON data and executes the function with the specified arguments.

When the function completes, the new isolate sends the result back to the main isolate using the SendPort.send() method. The result is again encoded as JSON and sent back to the main isolate via message passing. The compute() function then returns the result as a Future, which can be awaited or processed asynchronously.

Dart uses a message passing system to communicate between isolates, which helps to prevent shared mutable state and reduce synchronization issues. When you use the compute() function, Dart automatically handles the details of creating and managing the new isolate, passing the function and arguments to the new isolate, and returning the result to the main isolate.

The compute() function can be used with any function that takes arguments and returns a result, making it a versatile tool for executing computationally intensive code on a background isolate. By running code on a background isolate, you can keep the main isolate free to handle user input and other tasks, which helps to keep your Dart application responsive and performant.

In summary, Dart's compute() function provides a simple and efficient way to execute computationally intensive code on a background isolate, using a message passing system to communicate between isolates and return results to the main isolate. This makes it easy to write high-performance, responsive, and scalable Dart applications that can take advantage of multiple cores and distributed computing environments.

How can I pass a large chunk of data to compute more efficiently?

When passing large chunks of data to the compute() function, it's important to keep in mind that the data is encoded as JSON and sent over the message passing system to the new isolate, which can be inefficient for large data sets. Here are some techniques you can use to pass large chunks of data to compute() more efficiently:

Use the ByteData class: Instead of encoding the data as JSON, you can use the ByteData class to represent binary data, and pass this data directly to the new isolate using the SendPort.send() method. This can be more efficient than encoding the data as JSON, especially for large data sets.
Use the Shared classes: The dart:typed_data library provides a set of "Shared" classes that allow multiple isolates to share the same memory space. You can use the SendPort.send() method to pass a reference to a shared memory region to the new isolate, and both the main isolate and the new isolate can read and write to the same memory space. This can be a very efficient way to share large data sets between isolates.
Use streams: Instead of passing a large chunk of data as a single object, you can break the data up into smaller pieces and pass it as a stream of data. You can use the Stream and StreamController classes to create a stream of data, and pass this stream to the new isolate using the SendPort.send() method. The new isolate can then process the data as it arrives, instead of waiting for the entire data set to be passed.
Use file I/O: If the data is too large to fit in memory, you can use file I/O to read and write the data to disk, and pass a reference to the file to the new isolate. The new isolate can then read the data from the file as needed. This can be a very efficient way to work with very large data sets.

In summary, when passing large chunks of data to the compute() function, you can use techniques such as the ByteData class, the Shared classes, streams, and file I/O to pass the data more efficiently. By choosing the right technique for your specific use case, you can ensure that your Dart application remains responsive and performant, even when working with very large data sets.

Does DART support priorities when scheduling the isolate?

No, Dart does not natively support priorities when scheduling isolates.

Dart isolates are scheduled using a cooperative multitasking model, where each isolate is responsible for yielding control back to the event loop to allow other isolates to execute. Dart does not provide a mechanism for setting priorities on individual isolates, since the scheduling is handled at a more granular level within each isolate.

However, there are some techniques you can use to achieve similar effects to priority scheduling in Dart, such as:

Use asynchronous programming: By breaking up your work into smaller tasks and using asynchronous programming techniques, you can allow other isolates to execute while waiting for I/O or other operations to complete. This can help to keep your application responsive and performant, even when dealing with long-running tasks.
Use the compute() function: The compute() function automatically schedules work on a background isolate, which can help to keep the main isolate free to handle user input and other tasks. By running computationally intensive tasks on a background isolate, you can achieve some of the benefits of priority scheduling, since the background isolate will not block the main isolate while the task is running.
Use the Timer class: By using the Timer class to delay the execution of a task, you can give other isolates a chance to execute in the meantime. This can help to prevent long-running tasks from blocking other isolates and keep your application responsive.

In summary, while Dart does not natively support priorities when scheduling isolates, there are some techniques you can use to achieve similar effects and keep your application responsive and performant. By using asynchronous programming, the compute() function, and the Timer class, you can help to prevent long-running tasks from blocking other isolates and ensure that your Dart application remains responsive and efficient.

what is DART's microtask

In Dart, a microtask is a small unit of work that is scheduled to run on the event loop after the current task has completed. Microtasks are typically used to perform small, fast operations that need to execute as soon as possible, such as updating the user interface or handling input events.

When you enqueue a microtask, it is added to the microtask queue, which is a separate queue from the main event queue. When the main event queue has completed all of its tasks, the event loop will begin processing the microtask queue, running each microtask in turn until the queue is empty. This ensures that microtasks are executed as soon as possible, without blocking other tasks in the event queue.

In Dart, you can use the scheduleMicrotask() function to enqueue a microtask. This function takes a callback function as its argument, and schedules the function to run as a microtask on the event loop. Here's an example:

void main() {
  print('Start');

  scheduleMicrotask(() {
    print('Run microtask');
  });

  print('End');
}

In this example, the scheduleMicrotask() function is used to enqueue a microtask that will print "Run microtask" to the console. When the program is run, the output will be:

Start
End
Run microtask

Note that the microtask runs after the "End" message has been printed, since it was added to the microtask queue before the end of the main task. This demonstrates the asynchronous and non-blocking nature of microtasks in Dart.

In summary, microtasks are a small unit of work that can be scheduled to run on the event loop after the current task has completed. They are useful for performing small, fast operations that need to execute as soon as possible, without blocking other tasks in the event queue. The scheduleMicrotask() function is used to enqueue a microtask in Dart.

Is MicroTask a closure?

In Dart, a microtask is not a closure, but it often involves using a closure as a callback function.

A closure is a function object that has access to variables in its lexical scope, even after the enclosing function has returned. Closures are often used in Dart to define callbacks for event handlers and other asynchronous operations, since they allow you to capture variables from the enclosing scope and use them in the callback function.

When you use the scheduleMicrotask() function to enqueue a microtask, you typically pass a closure as the callback function. The closure is then executed as a microtask on the event loop. Here's an example:

void main() {
  print('Start');

  String message = 'Hello, world!';

  scheduleMicrotask(() {
    print(message);
  });

  print('End');
}

In this example, a closure is used as the callback function for the microtask, and it captures the message variable from the enclosing scope. When the program is run, the output will be:

Start
End
Hello, world!

In summary, a microtask in Dart is not a closure, but it often involves using a closure as a callback function to perform small, fast operations that need to execute as soon as possible, without blocking other tasks in the event queue. The scheduleMicrotask() function is typically used to enqueue a closure as a microtask on the event loop.

How many different queues does DART use?

In Dart, there are several different queues used to manage asynchronous tasks and events. Here are some of the main queues used in Dart:

Event queue: The event queue is the main queue used to manage events and tasks in Dart. This queue is managed by the event loop, and is used to schedule tasks and events in the order they are received. The event queue is used for handling user input events, network requests, and other asynchronous tasks.
Microtask queue: The microtask queue is a separate queue used to manage microtasks in Dart. Microtasks are small, fast operations that need to execute as soon as possible, such as updating the user interface or handling input events. The microtask queue is processed after the main event queue has completed all of its tasks, and ensures that microtasks are executed as soon as possible, without blocking other tasks in the event queue.
Timer queue: The timer queue is used to manage timers in Dart. Timers are used to schedule tasks to run at a specific time in the future, or to run repeatedly at a fixed interval. The timer queue is used to manage the timing of these tasks, and is processed by the event loop.
Future queue: The future queue is used to manage futures in Dart. A future is a value that may not be available yet, but will be available at some point in the future. The future queue is used to manage the completion of these futures, and is processed by the event loop when the future becomes available.
Isolate queue: Each isolate in Dart has its own event queue, which is used to manage events and tasks specific to that isolate. Isolates communicate with each other via message passing, and the event queues for each isolate are used to manage the processing of these messages.

In summary, Dart uses several different queues to manage asynchronous tasks and events, including the main event queue, the microtask queue, the timer queue, the future queue, and isolate-specific event queues. These queues are managed by the event loop, which ensures that tasks and events are executed in the order they are received, and that the application remains responsive and performant.

Are those queues having different priorities?

In Dart, each of the queues used to manage asynchronous tasks and events has a different priority, based on the order in which tasks are scheduled and processed.

The event queue, which is used to manage user input events and network requests, has the highest priority in Dart. Tasks and events in the event queue are processed in the order they are received, and are generally processed as soon as possible, to ensure that the application remains responsive to user input and other external events.
The microtask queue, which is used to manage small, fast operations that need to execute as soon as possible, has the second-highest priority in Dart. Microtasks are processed immediately after the current task has completed, before any other tasks in the event queue are processed. This ensures that microtasks are executed as soon as possible, without blocking other tasks in the event queue.
The timer queue, which is used to manage timers in Dart, has a lower priority than the event and microtask queues. Timers are processed by the event loop when their scheduled time has elapsed, but may be delayed if other tasks are running at the same time.
The future queue, which is used to manage futures in Dart, has a lower priority than the timer queue. Futures are processed by the event loop when they become available, but may be delayed if other tasks are running at the same time.
Each isolate in Dart has its own event queue, which is used to manage events and tasks specific to that isolate. Isolate-specific event queues have a lower priority than the main event queue, and are processed by the event loop when their tasks and events have completed.

In summary, each of the queues used to manage asynchronous tasks and events in Dart has a different priority, based on the order in which tasks are scheduled and processed. The event queue has the highest priority, followed by the microtask queue, the timer queue, the future queue, and isolate-specific event queues. Understanding the priorities of these queues can help you write efficient and responsive Dart applications.

Does DART implement the work steal scheduling?

No, Dart does not implement work-stealing scheduling for isolates.

Work-stealing is a technique used in some concurrent programming models to help balance the workload between threads or processes. In work-stealing, a thread or process that has finished its own work steals tasks from other threads or processes that still have work to do. This can help to balance the workload and improve performance in some situations.

However, Dart isolates are scheduled using a cooperative multitasking model, which does not use work-stealing. In Dart, each isolate is responsible for yielding control back to the event loop to allow other isolates to execute. The scheduling of isolates is handled at a more granular level within each isolate, so work-stealing is not necessary.

That being said, Dart does provide some built-in tools for managing concurrent workloads, such as the Future and Stream APIs, the Isolate class, and the compute() function. These tools allow you to create concurrent tasks and distribute them across multiple isolates, without the need for work-stealing.

In summary, Dart does not implement work-stealing scheduling for isolates, since isolates are scheduled using a cooperative multitasking model. However, Dart provides some built-in tools for managing concurrent workloads, which allow you to distribute tasks across multiple isolates and achieve better performance in some situations.

what if the isolate does not yield?

If an isolate in Dart does not yield, it can cause the event loop to become blocked, which can make the entire application unresponsive.

In Dart, each isolate is responsible for yielding control back to the event loop to allow other isolates to execute. If an isolate runs for a long time without yielding, it can block other isolates and prevent them from executing. This can make the entire application unresponsive and lead to performance problems.

To prevent this, it's important to design your isolates and tasks in a way that allows them to yield control back to the event loop as needed. This can be achieved by breaking up long-running tasks into smaller pieces, using asynchronous programming techniques, and using tools like the compute() function and isolates to distribute work across multiple threads.

If you encounter a situation where an isolate is not yielding and is causing the event loop to become blocked, you may need to use tools like the Timer class to force the isolate to yield periodically, or you may need to refactor your code to make it more responsive and better able to handle multiple concurrent tasks.

In summary, if an isolate in Dart does not yield, it can cause the event loop to become blocked and make the entire application unresponsive. To prevent this, it's important to design your isolates and tasks in a way that allows them to yield control back to the event loop as needed, and to use tools like the Timer class and isolates to distribute work across multiple threads.

Is isolate something like coroutine?

Yes, in some ways, an isolate in Dart can be thought of as similar to a coroutine.

A coroutine is a type of concurrent programming construct that allows multiple tasks to execute cooperatively within a single thread. Each task is defined as a separate function or procedure, and can yield control back to the main program at specific points, allowing other tasks to execute.

Similarly, an isolate in Dart is a concurrent programming construct that allows multiple tasks to execute cooperatively within a single thread of execution. Each isolate is a separate execution context with its own memory space, and can run concurrently with other isolates. Like a coroutine, an isolate can yield control back to the event loop at specific points, allowing other isolates to execute.

However, there are some important differences between isolates and coroutines. Unlike a coroutine, an isolate in Dart has its own memory space, which can help to prevent data race conditions and other concurrency issues. Additionally, isolates communicate with each other using message passing, rather than shared memory or other synchronization mechanisms, which can simplify concurrent programming.

In summary, while there are some similarities between isolates in Dart and coroutines in other programming languages, isolates have some unique features that make them well-suited to concurrent programming in Dart.

how do you compare dart isolate with goroutine

Dart isolates and Go's goroutines are both concurrent programming constructs that allow multiple tasks to execute cooperatively within a single thread of execution. However, there are some important differences between the two.

Here are some of the key differences between Dart isolates and Go's goroutines:

Memory isolation: In Dart, each isolate has its own memory space, which is completely isolated from other isolates. This can help to prevent data race conditions and other concurrency issues. In Go, goroutines share the same memory space, which can make it more challenging to write correct concurrent programs.

Message passing: In Dart, isolates communicate with each other using message passing. Messages are sent and received using the SendPort and ReceivePort classes, which can simplify concurrent programming by removing the need for locks and other synchronization mechanisms. In Go, goroutines communicate using channels, which can be used to send and receive data between goroutines.

Threading model: Dart isolates are scheduled using a cooperative multitasking model, which means that each isolate is responsible for yielding control back to the event loop to allow other isolates to execute. In contrast, Go's goroutines are scheduled using a preemptive multitasking model, which means that the Go runtime schedules goroutines to run based on various factors, such as the number of available threads and the amount of CPU time each goroutine has consumed.

Asynchronous programming: Dart has a rich set of asynchronous programming tools, such as the Future and Stream APIs, which can be used to write non-blocking and responsive code. Go also has some built-in tools for asynchronous programming, such as goroutines and channels, but it is generally considered to be more difficult to write asynchronous code in Go than in Dart.

In summary, while both Dart isolates and Go's goroutines are useful tools for concurrent programming, they have some important differences in terms of memory isolation, message passing, threading model, and asynchronous programming. Choosing the right tool for concurrent programming will depend on the specific needs of your application and the programming language you are working with.

how do you compare rust async/await with Dart's async/await?

Rust and Dart are two programming languages that have their own unique approaches to implementing asynchronous programming using the async/await pattern.

Rust's async/await feature is built on top of its "futures" system, which is a way of representing a value that may not be available yet. Rust's async/await allows you to write asynchronous code that looks and feels like synchronous code, making it easier to reason about complex, concurrent systems. One key feature of Rust's async/await is its "pinning" mechanism, which allows Rust to safely store asynchronous state on the stack.

Dart's async/await is built on top of its "Futures" system, which is similar to Rust's "futures". Dart's async/await allows you to write asynchronous code that looks and feels like synchronous code, similar to Rust. One advantage of Dart's async/await is its use of "async*" and "yield" keywords, which allow you to write code that can produce multiple values over time, which is useful in certain types of applications.

Overall, both Rust and Dart offer powerful and flexible ways to write asynchronous code using async/await, with some differences in implementation details. Rust's focus on safety and performance and Dart's focus on ease of use and flexibility makes them both excellent choices for different types of projects. The best choice of programming language for a particular project will depend on a variety of factors such as the specific requirements of the project, the skills and preferences of the development team, and the ecosystem and libraries available for the language.

Kevin Boos

2023/03/21 23:32:22

No, Dart does not support M:N threading model. Dart is designed to use a 1:1 threading model, which means that each isolate in a Dart program is associated with a single native operating system thread.

whoa, this is actually quite surprising! this was the same approach we used as a first attempt in Dreadnought, but i never figured a production system would choose that design point. (Edited)

2023/03/21 23:34:55

This is exactly why a message passing system is typically way more inefficient (and if not, more complex) than a shared-most model. I like the idea, but if we went this route, we'd really need to place lots of emphasis on message-passing abstractions that compile down into shared-memory accesses when possible. David & I discussed this within the context of his new message passing system, and in fact we also brought up how Binder works on android as a similar point of comparison. (Edited)

2023/03/21 23:36:43

One advantage of Dart's async/await is its use of "async*" and "yield" keywords, which allow you to write code that can produce multiple values over time, which is useful in certain types of applications.

i guess Rust doesn't yet have this on stable, but generators effectively provide the same thing, IIUC. (Edited)