Kernel Tuning Gives 40% Gains
The CMOVxx conditional move instructions on the 21264 have been implemented in the hardware by decomposing them into two separate instructions inside the processor. The result of the latency of a CMOVxx instruction is a minimum of two cycles, and can take up to five cycles, depending upon the number of CMOVs in a given fetch block. In some situations, replacing CMOV instructions with highly predictable conditional branches can result in a performance gain on the 21264. Overall, a good rule of thumb is to try to minimize the number of CMOV instructions if possible.
The data was collected from an API NetWorks' CS20 server, which has dual 833MHz processors with 4MB DDR cache, 1GB of SDRAM and Ultra-160 SCSI disks. Two load-generation tests were run: five builds of the 2.2.18 kernel and five builds of gcc-2.95.3. The average system time (as reported by /usr/bin/time -p) was recorded, using various levels of parallelism with make (see Tables 2 and 3).
A similar version of the experiment was run using the 2.4.2 kernel in default mode (all of the performance patches exist). The results were compared to an unpatched 2.4.2 kernel with most (but not all) of the performance changes reverted.
This experiment was initially performed on an API NetWorks' UP1000 motherboard system, which has a 700MHz processor with 4MB cache, 128MB SDRAM and IDE disks. Again, five builds of the kernel and gcc were run, and the average times were recorded. The kernel used was 2.4.0-test6, with and without the patches.
On a modestly configured 21664 system (the UP1000), the performance increase is significant in terms of reducing the amount of time spent in the kernel, with improvements in the 40% range for some activities (kernel builds). On a more generously configured CS20, we consistently attained speed increases of 14-15% for the measured loads.
We attribute the differences between the UP1000 and CS20 systems to be related to their memory: the UP1000 has an 800MB/sec, 64-bit bus, while the CS20 has a 2.65GB/sec, 256-bit bus.
All of the rewritten routines have appeared in one form or another (some have undergone subsequent rewriting) as part of the 2.4.2 kernel. Additionally, we have put together a patch for 2.2.17 of the kernel and made it available on our corporate web site, http://www.api-networks.com/products/downloads/developer_support/ under “Performance”. Through additional efforts, these improvements have also migrated into glibc and will eventually help improve application performance of user-mode code.
Practical Task Scheduling Deployment
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.
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- Stunnel Security for Oracle
- The Firebird Project's Firebird Relational Database
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- SUSE LLC's SUSE Manager
- Managing Linux Using Puppet
- My +1 Sword of Productivity
- Non-Linux FOSS: Caffeine!
- Google's SwiftShader Released
- Doing for User Space What We Did for Kernel Space
- 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