Introducing the 2.6 Kernel
During 2.5, VM finally came into its own. The VM subsystem is the component of the kernel responsible for managing the virtual address space of each process. This includes the memory management scheme, the page eviction strategy (what to swap out when memory is low) and the page-in strategy (when to swap things back in). The VM often has been a rough issue for Linux. Good VM performance on a specific workload often implies poor performance elsewhere. A fair, simple, well-tuned VM always seemed unobtainable—until now.
The new VM is the result of three major changes:
reverse-mapping (rmap) VM
redesigned, smarter, simpler algorithms
tighter integration with the VFS layer
The net result is superior performance in the common case without the VM miserably falling to pieces in the corner cases. Let's briefly look at each of these three changes.
Any virtual memory system has both physical addresses (the address of actual pages on your physical RAM chips) and virtual addresses (the logical address presented to the application). Architectures with a memory management unit (MMU) allow convenient lookup of a physical address from a virtual address. This is desirable because programs are accessing virtual addresses constantly, and the hardware needs to convert this to a physical address. Moving in the reverse direction, however, is not so easy. In order to resolve from a physical to a virtual address, the kernel needs to scan each page table entry and look for the desired address, which is time consuming. A reverse-mapping VM provides a reverse map from virtual to physical addresses. Consequently, instead of:
for (each page table entry) if (this physical address matches) we found a corresponding virtual address
the rmap VM simply can look up the virtual address by following a pointer. This method is much faster, especially during intensive VM pressure. Figure 4 is a diagram of the reverse mapping.
Next, the VM hackers redesigned and improved many of the VM algorithms with simplification, great average-case performance and acceptable corner-case performance in mind. The resulting VM is simplified yet more robust.
Finally, integration between the VM and VFS was greatly improved. This is essential, as the two subsystems are intimately related. File and page write-back, read-ahead and buffer management was simplified. The pdflush pool of kernel threads replaced the bdflush kernel thread. The new threads are capable of providing much-improved disk saturation; one developer noted the code could keep sixty disk spindles concurrently saturated.
Thread support in Linux always has seemed like an afterthought. A threading model does not fit perfectly into the typical UNIX process model, and consequently, the Linux kernel did little to make threads feel welcome. The user-space pthread library (called LinuxThreads) that is part of glibc (the GNU C library) did not receive much help from the kernel. The result has been less than stellar thread performance. There was a lot of room for improvement, but only if the kernel and glibc hackers worked together.
Rejoice, because they did. The result is greatly improved kernel support for threads and a new user-space pthread library, called Native POSIX Threading Library (NPTL), which replaces LinuxThreads. NPTL, like LinuxThreads, is a 1:1 threading model. This means one kernel thread exists for every user-space thread. That developers achieved excellent performance without resorting to an M:N model (where the number of kernel threads may be dynamically less than the number of user-space threads) is quite impressive.
The combination of the kernel changes and NPTL results in improved performance and standards compliance. Some of the new changes include:
thread local storage support
O(1) exit() system call
improved PID allocator
clone() system call threading enhancements
thread-aware code dump support
threaded signal enhancements
a new fast user-space locking primitive (called futexes)
The results speak for themselves. On a given machine, with the 2.5 kernel and NPTL, the simultaneous creation and destruction of 100,000 threads takes less than two seconds. On the same machine, without the kernel changes and NPTL, the same test takes approximately 15 minutes.
Table 4 shows the results of a test of thread creation and exit performance between NPTL, NGPT (IBM's M:N pthread library, Next Generation POSIX Threads) and LinuxThreads. This test also creates 100,000 threads but in much smaller parallel increments. If you are not impressed yet, you are one tough sell.
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!
- Paranoid Penguin - Building a Secure Squid Web Proxy, Part IV
- SUSE LLC's SUSE Manager
- Google's SwiftShader Released
- Managing Linux Using Puppet
- My +1 Sword of Productivity
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- Non-Linux FOSS: Caffeine!
- SourceClear Open
- SuperTuxKart 0.9.2 Released
- Parsing an RSS News Feed with a Bash Script
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