Kernel Korner - The New Work Queue Interface in the 2.6 Kernel
For most UNIX systems, Linux included, device drivers typically divide the work of processing interrupts into two parts or halves. The first part, the top half, is the familiar interrupt handler, which the kernel invokes in response to an interrupt signal from the hardware device. Unfortunately, the interrupt handler is true to its name: it interrupts whatever code is executing when the hardware device issues the interrupt. That is, interrupt handlers (top halves) run asynchronously with respect to the currently executing code. Because interrupt handlers interrupt already executing code (whether it is other kernel code, a user-space process or even another interrupt handler), it is important that they run as quickly as possible.
Worse, some interrupt handlers (known in Linux as fast interrupt handlers) run with all interrupts on the local processor disabled. This is done to ensure that the interrupt handler runs without interruption, as quickly as possible. More so, all interrupt handlers run with their current interrupt line disabled on all processors. This ensures that two interrupt handlers for the same interrupt line do not run concurrently. It also prevents device driver writers from having to handle recursive interrupts, which complicate programming. If interrupts are disabled, however, other interrupt handlers cannot run. Interrupt latency (how long it takes the kernel to respond to a hardware device's interrupt request) is a key factor in the performance of the system. Again, the speed of interrupt handlers is crucial.
To facilitate small and fast interrupt handlers, the second part or bottom half of interrupt handling is used to defer as much of the work as possible away from the top half and until a later time. The bottom half runs with all interrupts enabled. Therefore, a running bottom half does not prevent other interrupts from running and does not contribute adversely to interrupt latency.
Nearly every device driver employs bottom halves in one form or another. The device driver uses the top half (the interrupt handler) to respond to the hardware and perform any requisite time-sensitive operations, such as resetting a device register or copying data from the device into main memory. The interrupt handler then marks the bottom half, instructing the kernel to run it as soon as possible, and exits.
In most cases, then, the real work takes place in the bottom half. At a later point—often as soon as the interrupt handler returns—the kernel executes the bottom half. Then the bottom half runs, performing all of the remaining work not carried out by the interrupt handler. The actual division of work between the top and bottom halves is a decision made by the device driver's author. Generally, device driver authors attempt to defer as much work as possible to the bottom half.
Confusingly, Linux offers many mechanisms for implementing bottom halves. Currently, the 2.6 kernel provides softirqs, tasklets and work queues as available types of bottom halves. In previous kernels, other forms of bottom halves were available; they included BHs and task queues. This article deals with the new work queue interface only, which was introduced during the 2.5 development series to replace the ailing keventd part of the task queue interface.
Work queues are interesting for two main reasons. First, they are the simplest to use of all the bottom-half mechanisms. Second, they are the only bottom-half mechanism that runs in process context; thus, work queues often are the only option device driver writers have when their bottom half must sleep. In addition, the work queue mechanism is brand new, and new is cool.
Let's discuss the fact that work queues run in process context. This is in contrast to the other bottom-half mechanisms, which all run in interrupt context. Code running in interrupt context is unable to sleep, or block, because interrupt context does not have a backing process with which to reschedule. Therefore, because interrupt handlers are not associated with a process, there is nothing for the scheduler to put to sleep and, more importantly, nothing for the scheduler to wake up. Consequently, interrupt context cannot perform certain actions that can result in the kernel putting the current context to sleep, such as downing a semaphore, copying to or from user-space memory or non-atomically allocating memory. Because work queues run in process context (they are executed by kernel threads, as we shall see), they are fully capable of sleeping. The kernel schedules bottom halves running in work queues, in fact, the same as any other process on the system. As with any other kernel thread, work queues can sleep, invoke the scheduler and so on.
Normally, a default set of kernel threads handles work queues. One of these default kernel threads runs per processor, and these threads are named events/n where n is the processor number to which the thread is bound. For example, a uniprocessor machine would have only an events/0 kernel thread, whereas a dual-processor machine would have an events/1 thread as well.
It is possible, however, to run your work queues in your own kernel thread. Whenever your bottom half is activated, your unique kernel thread, instead of one of the default threads, wakes up and handles it. Having a unique work queue thread is useful only in certain performance-critical situations. For most bottom halves, using the default thread is the preferred solution. Nonetheless, we look at how to create new work queue threads later on.
The work queue threads execute your bottom half as a specific function, called a work queue handler. The work queue handler is where you implement your bottom half. Using the work queue interface is easy; the only hard part is writing the bottom half (that is, the work queue handler).
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
Join Linux Journal's Mike Diehl and Pat Cameron of Help Systems.
Free to Linux Journal readers.Register Now!
- SUSE LLC's SUSE Manager
- My +1 Sword of Productivity
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- Managing Linux Using Puppet
- Non-Linux FOSS: Caffeine!
- Tech Tip: Really Simple HTTP Server with Python
- Doing for User Space What We Did for Kernel Space
- Parsing an RSS News Feed with a Bash Script
- Rogue Wave Software's Zend Server
- SuperTuxKart 0.9.2 Released
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide