How does multitasking really work?

The first PC on which I was running a copy of Linux back in the nineties did of course only have one CPU, so it was clear to me that it could physically only execute one instruction at a time – but still, it magically created the impression to run several programs in parallel without visible delay. How could that ever work? Almost twenty years later, I finally found the answer.

In fact, trying to understand how multitasking and multithreading actually work was one of my main motivations when I developed the ctOS kernel a couple of years ago. In this post, I will try to explain the basic mechanism behind the magic.

The basic idea sounds simple enough. Suppose a task has been running for some time and the operating system decides that it is time to switch to a different task. Then the operating system kernel will interrupt the current execution and save the current state of the CPU somewhere in memory. It then locates a different task that is ready to be run – a process which is called scheduling– and retrieves the saved CPU state representing this task from memory. Then, it will let the CPU continue execution with that saved state. As this state is exactly the state of the CPU that was in place when the newly scheduled task was stopped, the CPU will continue to process this task as if it had never been interrupted.

Unfortunately, this simple explanation leaves a number of questions open. First, if a task is executing, it means that the CPU is busy running this task. How, then, can the operating system “do” something? Second, what is the “state of the CPU”? How can we store it and how can we put the CPU back in a saved state? And then, how do we decide that “it is time to switch to a different task”? It turns out that the answer to these questions have to do with interrupts.

Task switching, interrupts and the stack

To explain this, let us for a moment assume a vastly simplified world where all tasks currently active do not require I/O operations. Suppose task A is currently being processed, i.e. the CPU is executing the stream of instructions (the “program”) that make up this task. At some point, we will see an interrupt firing. If there is no I/O, this is usually the timer interrupt that fires periodically, say one thousand times per second.

If this happens, the CPU will stop the execution of task A and start to execute an interrupt service handler. Now the interrupt service handler has been set up by the operating system and points at code within the operating system kernel – so at this point, control is given to the operating system! At the same time, we have learned that the CPU will store the value of some registers like EIP on the stack, and a smart interrupt handler will store the value of additional registers on the stack as well in order to be able to use and reconstruct their value later, and, right at its end, contain code to pop the register values off the stack again. So we could as well set up our ISR in such a way that it does in fact store the value of all registers on the stack. This already provides two ingredients for the task switching mechanism – a way to make sure that the operating system gains control of the CPU at least once a millisecond and a saved CPU state on the stack!

Now the operating system will usually maintain a list of tasks and scan that list according to certain criteria to find a task which should be executed next – we will look at this process in greater detail further below. Suppose that it determines a task which we call B which should be run next. For the sake of simplicity, let us also assume that this task is not entirely new, but has already been running for some time (setting up a new task is a bit more complicated). Thus, when task B was interrupted for the last time, its state was also saved on the stack somewhere, and our operating system has hopefully stored a pointer to this stack.

What we now want to do is to exchange the stored state of task A with that of B. The easiest way to do this is not to copy around the stacks, but to manipulate the stack pointer such that the execution resumes using the stack of task B instead of task A! When we do this and reach the end of the interrupt service handler, the part of the interrupt service handler that is responsible for popping all registers off the stack again as well as the final IRET will use the stack of task B. In other words, the CPU will pick up the state of task B instead the state of task A. It will therefore continue execution with the state of the registers and the instruction pointer that task B had when it was interrupted – in other words, task B will continue to execute instead of task A, and we have switched from task A to task B.

TaskSwitch

In reality, there are a few subtle points that need to be worked out in more detail when you really want to build this. You will have to think about what the stack actually is – does every task have its own stack, how do you isolate them etc. In addition, a task switch might invoke a switch to a different process and thus to a different virtual address space, so at some point, addresses become invalid and cannot be accessed any more. The exact process that ctOS uses is described in the section on task switching in the interrupt handling documentation of ctOS.

We have explained the process of task switching using the timer interrupt as an example. However, this is not the only possible trigger for a task switch. There are of course other hardware interrupts that might trigger a task switch, but more importantly, many task switches do in fact occur during a system call! Assume for instance that a task wants to read data from the hard drive. It will then invoke a system call to do this, i.e. it will hand over control to the kernel voluntarily. The kernel will ask the hard drive to go and get the data, but of course this takes some time. Thus the kernel will in general not return to this task, but schedule a different task and execute a task switch. If (as ctOS does it) a system call is itself implemented as a software interrupt, the mechanism is exactly the same as if a task switch had been done during the processing of a hardware interrupt (if the faster SYSENTER/SYSEXIT mechanism is used, the processing is a bit different but the idea is the same).

Scheduling

What we have not explained so far is how the operating system does actually select a new task to be executed. The exact details how to do this are different for each operating system, but it is probably fair to say that there are in general two main ingredients – the time the task has been running until the last task switch and the state of the task.

First, there is the CPU time that a task has consumed since the last task switch, commonly measured by the quantum. The quantum is a number that is set to some initial value when the task gets control of the CPU. During each processing of the timer interrupt, the quantum of the currently active task is decreased by one. As the timer interrupt fires periodically, the quantum thus measures the time that the task has already consumed – the lower the remaining quantum, the longer the task did already run. If a task has reached quantum zero, it might be a good idea to schedule a different task.

Second, there is the state of a task. Not every task is ready to be executed at any given point in time. A task might for instance be waiting for some event (user input, I/O, ..) or might otherwise be blocked. Our a task could be in the status new, indicating that it is currently still being set up and not yet ready to run. Or it might have been stopped by a signal and be waiting for the signal to continue. And finally, a task can either be active i.e. currently executing, or ready, i.e. waiting to be scheduled.

TaskStatus

During scheduling, only tasks in status ready will be considered as candidates for execution. One way to implement scheduling is to put all ready tasks onto a queue, called the ready queue. Scheduling than basically proceeds as follows.

  • Check whether the currently active task has used up its quantum
  • If no, decrease the quantum by one and return – no task switch will be done. Otherwise proceed with the next step
  • Set the state of the currently active task to “ready”, set its quantum back to its initial value and queue the task at the end
  • Select the first task in the ready queue. Update its status to active and mark it as target of the next task switch

When a task that was not ready but, for instance, blocked, becomes ready, it is added at the end of the ready queue with an initial quantum.

Scheduler.png

There is a more advanced version of this simple algorithm called round-robin scheduling with dynamic priorities. In this model, there is more than one ready queue, in fact there is a list of priorities, say 0 – 15, and there is one queue for each priority. If a new task to be scheduled needs to be determined, these queues are scanned for candidates, starting with the highest priority queue. The priority can be adjusted at run-time based on the current behaviour of the process – see the documentation of the ctOS scheduler for more details. This approach is still comparatively simple, and designing a highly optimized scheduler is an art that can make the difference between a good and a lousy performance. Currently, Linux uses a different scheduling mechanism called CFS, but scheduling remains a tricky tasks, especially in an SMP environment, and we will probably see additional work going into this in the future (see for instance this article for a more in-depth discussion).

Now multi-tasking is nice, but to really execute several programs at the same time has an additional dimension – memory. If you want to run different programs on one physical machine that might even belong to different users, you need to make sure that a program cannot access memory reserved for a different program, i.e. you need some level of memory space isolation. Fortunately, modern CPUs offer a mechanism called virtual memory that we can use for this purpose and that we will explain in the next post.

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