Callback based approaches

Choosing the right reference frame – Concurrency and Asynchronous Programming: a Detailed Overview

Chelsie Smith Callback based approaches, Down the rabbit hole, Each stack has a fixed space, Exams of IT, Hyper-threading, IT Certification Exams 0 Comment

When you write code that is perfectly synchronous from your perspective, stop for a second and consider how that looks from the operating system perspective.

The operating system might not run your code from start to end at all. It might stop and resume your process many times. The CPU might get interrupted and handle some inputs while you think it’s only focused on your task.

So, synchronous execution is only an illusion. But from the perspective of you as a programmer, it’s not, and that is the important takeaway:

When we talk about concurrency without providing any other context, we are using you as a programmer and your code (your process) as the reference frame. If you start pondering concurrency without keeping this in the back of your head, it will get confusing very fast.

The reason I’m spending so much time on this is that once you realize the importance of having the same definitions and the same reference frame, you’ll start to see that some of the things you hear and learn that might seem contradictory really are not. You’ll just have to consider the reference frame first.

Asynchronous versus concurrent

So, you might wonder why we’re spending all this time talking about multitasking, concurrency, and parallelism, when the book is about asynchronous programming.

The main reason for this is that all these concepts are closely related to each other, and can even have the same (or overlapping) meanings, depending on the context they’re used in.

In an effort to make the definitions as distinct as possible, we’ll define these terms more narrowly than you’d normally see. However, just be aware that we can’t please everyone and we do this for our own sake of making the subject easier to understand. On the other hand, if you fancy heated internet debates, this is a good place to start. Just claim someone else’s definition of concurrent is 100 % wrong or that yours is 100 % correct, and off you go.

For the sake of this book, we’ll stick to this definition: asynchronous programming is the way a programming language or library abstracts over concurrent operations, and how we as users of a language or library use that abstraction to execute tasks concurrently.

The operating system already has an existing abstraction that covers this, called threads. Using OS threads to handle asynchrony is often referred to as multithreaded programming. To avoid confusion, we’ll not refer to using OS threads directly as asynchronous programming, even though it solves the same problem.

Given that asynchronous programming is now scoped to be about abstractions over concurrent or parallel operations in a language or library, it’s also easier to understand that it’s just as relevant on embedded systems without an operating system as it is for programs that target a complex system with an advanced operating system. The definition itself does not imply any specific implementation even though we’ll look at a few popular ones throughout this book.

If this still sounds complicated, I understand. Just sitting and reflecting on concurrency is difficult, but if we try to keep these thoughts in the back of our heads when we work with async code I promise it will get less and less confusing.

Concurrency versus parallelism – Concurrency and Asynchronous Programming: a Detailed Overview

Chelsie Smith A simplified overview, Callback based approaches, Hyper-threading, IT Certification Exams, PRE-EMPTION POINTS 0 Comment

Note

*However, modern CPUs can also do a lot of things in parallel. Most CPUs are pipelined, meaning that the next instruction is loaded while the current one is executing. It might have a branch predictor that tries to figure out what instructions to load next.

The processor can also reorder instructions by using out-of-order execution if it believes it makes things faster this way without ‘asking’ or ‘telling’ the programmer or the OS, so you might not have any guarantee that A happens before B.

The CPU offloads some work to separate ‘coprocessors’ such as the FPU for floating-point calculations, leaving the main CPU ready to do other tasks et cetera.

As a high-level overview, it’s OK to model the CPU as operating in a synchronous manner, but for now, let’s just make a mental note that this is a model with some caveats that become especially important when talking about parallelism, synchronization primitives (such as mutexes and atomics), and the security of computers and operating systems.

Concurrency versus parallelism

Right off the bat, we’ll dive into this subject by defining what concurrency is. Since it is quite easy to confuse concurrent with parallel, we will try to make a clear distinction between the two from the get-go.

Important

Concurrency is about dealing with a lot of things at the same time.

Parallelism is about doing a lot of things at the same time.

We call the concept of progressing multiple tasks at the same time multitasking. There are two ways to multitask. One is by progressing tasks concurrently, but not at the same time. Another is to progress tasks at the exact same time in parallel. Figure 1.1 depicts the difference between the two scenarios:

Figure 1.1 – Multitasking two tasks

First, we need to agree on some definitions:

• Resource: This is something we need to be able to progress a task. Our resources are limited. This could be CPU time or memory.
• Task: This is a set of operations that requires some kind of resource to progress. A task must consist of several sub-operations.
• Parallel: This is something happening independently at the exact same time.
• Concurrent: These are tasks that are in progress at the same time, but not necessarily progressing simultaneously.

This is an important distinction. If two tasks are running concurrently, but are not running in parallel, they must be able to stop and resume their progress. We say that a task is interruptible if it allows for this kind of concurrency.

Hyper-threading – Concurrency and Asynchronous Programming: a Detailed Overview

Chelsie Smith A simplified overview, Callback based approaches, Down the rabbit hole, IT Certification Exams, PRE-EMPTION POINTS 0 Comment

As CPUs evolved and added more functionality such as several arithmetic logic units (ALUs) and additional logic units, the CPU manufacturers realized that the entire CPU wasn’t fully utilized. For example, when an operation only required some parts of the CPU, an instruction could be run on the ALU simultaneously. This became the start of hyper-threading.

Your computer today, for example, may have 6 cores and 12 logical cores.. This is exactly where hyper-threading comes in. It “simulates” two cores on the same core by using unused parts of the CPU to drive progress on thread 2 and simultaneously running the code on thread 1. It does this by using a number of smart tricks (such as the one with the ALU).

Now, using hyper-threading, we could actually offload some work on one thread while keeping the UI interactive by responding to events in the second thread even though we only had one CPU core, thereby utilizing our hardware better.

You might wonder about the performance of hyper-threading

It turns out that hyper-threading has been continuously improved since the 90s. Since you’re not actually running two CPUs, there will be some operations that need to wait for each other to finish. The performance gain of hyper-threading compared to multitasking in a single core seems to be somewhere close to 30% but it largely depends on the workload.

Multicore processors

As most know, the clock frequency of processors has been flat for a long time. Processors get faster by improving caches, branch prediction, and speculative execution, and by working on the processing pipelines of the processors, but the gains seem to be diminishing.

On the other hand, new processors are so small that they allow us to have many on the same chip. Now, most CPUs have many cores and most often, each core will also have the ability to perform hyper-threading.

Do you really write synchronous code?

Like many things, this depends on your perspective. From the perspective of your process and the code you write, everything will normally happen in the order you write it.

From the operating system’s perspective, it might or might not interrupt your code, pause it, and run some other code in the meantime before resuming your process.

From the perspective of the CPU, it will mostly execute instructions one at a time.* It doesn’t care who wrote the code, though, so when a hardware interrupt happens, it will immediately stop and give control to an interrupt handler. This is how the CPU handles concurrency.

An evolutionary journey of multitasking – Concurrency and Asynchronous Programming: a Detailed Overview

Chelsie Smith A simplified overview, Callback based approaches, Down the rabbit hole, Each stack has a fixed space, IT Certification Exams, PRE-EMPTION POINTS 0 Comment

In the beginning, computers had one CPU that executed a set of instructions written by a programmer one by one. No operating system (OS), no scheduling, no threads, no multitasking. This was how computers worked for a long time. We’re talking back when a program was assembled in a deck of punched cards, and you got in big trouble if you were so unfortunate that you dropped the deck onto the floor.

There were operating systems being researched very early and when personal computing started to grow in the 80s, operating systems such as DOS were the standard on most consumer PCs.

These operating systems usually yielded control of the entire CPU to the program currently executing, and it was up to the programmer to make things work and implement any kind of multitasking for their program. This worked fine, but as interactive UIs using a mouse and windowed operating systems became the norm, this model simply couldn’t work anymore.

Non-preemptive multitasking

Non-preemptive multitasking was the first method used to be able to keep a UI interactive (and running background processes).

This kind of multitasking put the responsibility of letting the OS run other tasks, such as responding to input from the mouse or running a background task, in the hands of the programmer.

Typically, the programmer yielded control to the OS.

Besides offloading a huge responsibility to every programmer writing a program for your platform, this method was naturally error-prone. A small mistake in a program’s code could halt or crash the entire system.

Note

Another popular term for what we call non-preemptive multitasking is cooperative multitasking. Windows 3.1 used cooperative multitasking and required programmers to yield control to the OS by using specific system calls. One badly-behaving application could thereby halt the entire system.

Preemptive multitasking

While non-preemptive multitasking sounded like a good idea, it turned out to create serious problems as well. Letting every program and programmer out there be responsible for having a responsive UI in an operating system can ultimately lead to a bad user experience, since every bug out there could halt the entire system.

The solution was to place the responsibility of scheduling the CPU resources between the programs that requested it (including the OS itself) in the hands of the OS. The OS can stop the execution of a process, do something else, and switch back.

On such a system, if you write and run a program with a graphical user interface on a single-core machine, the OS will stop your program to update the mouse position before it switches back to your program to continue. This happens so frequently that we don’t usually observe any difference whether the CPU has a lot of work or is idle.

The OS is responsible for scheduling tasks and does this by switching contexts on the CPU. This process can happen many times each second, not only to keep the UI responsive but also to give some time to other background tasks and IO events.

This is now the prevailing way to design an operating system.

Note

Later in this book, we’ll write our own green threads and cover a lot of basic knowledge about context switching, threads, stacks, and scheduling that will give you more insight into this topic, so stay tuned.

Technical requirements – Concurrency and Asynchronous Programming: a Detailed Overview

Chelsie Smith A simplified overview, Callback based approaches, Down the rabbit hole, Each stack has a fixed space, IT Certification Exams 0 Comment

Asynchronous programming is one of those topics many programmers find confusing. You come to the point when you think you’ve got it, only to later realize that the rabbit hole is much deeper than you thought. If you participate in discussions, listen to enough talks, and read about the topic on the internet, you’ll probably also come across statements that seem to contradict each other. At least, this describes how I felt when I first was introduced to the subject.

The cause of this confusion is often a lack of context, or authors assuming a specific context without explicitly stating so, combined with terms surrounding concurrency and asynchronous programming that are rather poorly defined.

In this chapter, we’ll be covering a lot of ground, and we’ll divide the content into the following main topics:

  • Async history
  • Concurrency and parallelism
  • The operating system and the CPU
  • Interrupts, firmware, and I/O

This chapter is general in nature. It doesn’t specifically focus on Rust, or any specific programming language for that matter, but it’s the kind of background information we need to go through so we know that everyone is on the same page going forward. The upside is that this will be useful no matter what programming language you use. In my eyes, that fact also makes this one of the most interesting chapters in this book.

There’s not a lot of code in this chapter, so we’re off to a soft start. It’s a good time to make a cup of tea, relax, and get comfortable, as we’re about start this journey together.

Technical requirements

All examples will be written in Rust, and you have two alternatives for running the examples:

  • Write and run the examples we’ll write on the Rust playground
  • Install Rust on your machine and run the examples locally (recommended)

The ideal way to read this chapter is to clone the accompanying repository (https://github.com/PacktPublishing/Asynchronous-Programming-in-Rust/tree/main/ch01/a-assembly-dereference) and open the ch01 folder and keep it open while you read the book. There, you’ll find all the examples we write in this chapter and even some extra information that you might find interesting as well. You can of course also go back to the repository later if you don’t have that accessible right now.