Wednesday, 3 October 2018

Static Analysis Trends on Linux Next

I've been running static analysis using CoverityScan on linux-next for 2 years with the aim to find bugs (and try to fix some) before they are merged into Linux.  I have also been gathering the defect count data and tracking the defect trends:
As one can see from above, CoverityScan has found a considerable amount of defects and these are being steadily fixed by the Linux developer community.  The encouraging fact is that the outstanding issues are reducing over time. Some of the spikes in the data are because of changes in the analysis that I'm running (e.g. getting more coverage), but even so, one can see a definite trend downwards in the total defects in the Kernel.

With static analysis, some of these reported defects are false positives or corner cases that are in fact impossible to occur in real life and I am slowly working through these and annotating them so they don't get reported in the defect count.

It must be also noted that over these two years the kernel has grown from around 14.6 million to 17.1 million lines of code so the defect count has dropped from 1 defect in every ~2100 lines to 1 defect in every ~3000 lines over the past 2 years.  All in all, it is a remarkable improvement for such a large and complex codebase that is growing in size at such rate.

Monday, 16 July 2018

Comparing Latencies and Power consumption with various CPU schedulers

The low-latency kernel offering with Ubuntu provides a kernel tuned for low-latency environments using low-latency kernel configuration options.  The x86 kernels by default run with the Intel-Pstate CPU scheduler set to run with the powersave scaling governor biased towards power efficiency.

While power efficiency is fine for most use-cases, it can introduce latencies due to the fact that the CPU can be running at a low frequency to save power and also switching from a deep C state when idle to a higher C state when servicing an event can also increase on latencies.

In a somewhat contrived experiment, I rigged up an i7-3770 to collect latency timings of clock_nanosleep() wake-ups with timer event coalescing disabled (timer_slack set to zero) over 60 seconds across a range of CPU scheduler and governor settings on a 4.15 low-latency kernel.  This can be achieved using stress-ng, for example:

 sudo stress-ng --cyclic 1 --cyclic-dist 100 –cyclic-sleep=10000 --cpu 1 -l 0 -v \
--cyclic-policy rr --cyclic-method clock_ns --cpu 0 -t 60 --timer-slack 0  

..the above runs a cyclic measurement collecting latency counts in 100ns buckets with a clock_nanosecond wakeup interval of 10,000 nanoseconds with zero % load CPU stressor and timer slack set to 0 nanoseconds.  This dumps latency distribution stats that can be plotted to see where the modal latency points occur and the latency characteristics of the CPU scheduler.

I also used powerstat to measure the power consumed by the CPU package over a 60 second interval.  Measurements for the Intel-Pstate CPU scheduler [performance, powersave] and the ACPI CPU scheduler (intel_pstate=disabled) [performance, powersave, conservative and ondemand] were taken for 1,000,000 down to 10,000 nanosecond timer delays.

1,000,000 nanosecond timer delays (1 millisecond)

Strangely the powersave Intel-Pstate is using the most power (not what I expected).

The ACPI CPU scheduler in performance mode has the best latency distribution followed by the Intel-Pstate CPU scheduler also in performance mode.

100,000 nanosecond timer delays (100 microseconds)

Note that Intel-Pstate performance consumes the most power...
...and also has the most responsive low-latency distribution.

10,000 nanosecond timer delays (10 microseconds)

In this scenario, the ACPI CPU scheduler in performance mode was consuming the most power and had the best latency distribution.

It is clear that the best latency responses occur when a CPU scheduler is running in performance mode and this consumes a little more power than other CPU scheduler modes.  However, it is not clear which CPU scheduler (Intel-Pstate or ACPI) is best in specific use-cases.

The conclusion is rather obvious;  but needs to be stated.  For best low-latency response, set the CPU governor to the performance mode at the cost of higher power consumption.  Depending on the use-case, the extra power cost is probably worth the improved latency response.

As mentioned earlier, this is a somewhat contrived experiment, only one CPU was being exercised with a predictable timer wakeup.  A more interesting test would be with data handling, such as incoming packet handling over ethernet at different rates; I will probably experiment with that if and when I get more time.  Since this was a synthetic test using stress-ng, it does not represent real world low-latency scenarios, however, it may be worth exploring CPU scheduler settings to tune a low-latency configuration rather than relying on the default CPU scheduler setting.

Friday, 23 March 2018

Kernel Commits with "Fixes" tag

Over the past 5 years there has been a steady increase in the number of kernel bug fix commits that use the "Fixes" tag.  Kernel developers use this annotation on a commit to reference an older commit that originally introduced the bug, which is obviously very useful for bug tracking purposes. What is interesting is that there has been a steady take-up of developers using this annotation:

With the 4.15 release, 1859 of the 16223 commits (11.5%) were tagged as "Fixes", so that's a fair amount of work going into bug fixing.  I suspect there are more commits that are bug fixes, but aren't using the "Fixes" tag, so it's hard to tell for certain how many commits are fixes without doing deeper analysis.  Probably over time this tag will be widely adopted for all bug fixes and the trend line will level out and we will have a better idea of the proportion of commits per release that are just devoted to fixing issues.  Let's see how this looks in another 5 years time,  I'll keep you posted!

Wednesday, 21 February 2018

Linux Kernel Module Growth

The Linux kernel grows at an amazing pace, each kernel release adds more functionality, more drivers and hence more kernel modules.  I recently wondered what the trend was for kernel module growth per release, so I performed module builds on kernels v2.6.24 through to v4.16-rc2 for x86-64 to get a better idea of growth rates: one can see, the rate of growth is relatively linear with about 89 modules being added to each kernel release, which is not surprising as the size of the kernel is growing at a fairly linear rate too.  It is interesting to see that the number of modules has easily more than tripled in the 10 years between v2.6.24 and v4.16-rc2,  with a rate of about 470 new modules per year. At this rate, Linux will see the 10,000th module land in around the year 2025.