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Posts tagged with 'tools'

Colin Ian King

The stress-ng logo
The latest release of stress-ng contains a mechanism to measure latencies via a cyclic latency test.  Essentially this is just a loop that cycles around performing high precisions sleeps and measures the (extra overhead) latency taken to perform the sleep compared to expected time.  This loop runs with either one of the Round-Robin (rr) or First-In-First-Out real time scheduling polices.

The cyclic test can be configured to specify the sleep time (in nanoseconds), the scheduling type (rr or fifo),  the scheduling priority (1 to 100) and also the sleep method (explained later).

The first 10,000 latency measurements are used to compute various latency statistics:
  • mean latency (aka the 'average')
  • modal latency (the most 'popular' latency)
  • minimum latency
  • maximum latency
  • standard deviation
  • latency percentiles (25%, 50%, 75%, 90%, 95.40%, 99.0%, 99.5%, 99.9% and 99.99%
  • latency distribution (enabled with the --cyclic-dist option)
The latency percentiles indicate the latency at which a percentage of the samples fall into.  For example, the 99% percentile for the 10,000 samples is the latency at which 9,900 samples are equal to or below.

The latency distribution is shown when the --cyclic-dist option is used; one has to specify the distribution interval in nanoseconds and up to the first 100 values in the distribution are output.

For an idle machine, one can invoke just the cyclic measurements with stress-ng as follows:

 sudo stress-ng --cyclic 1 --cyclic-policy fifo \
--cyclic-prio 100 --cyclic-method --clock_ns \
--cyclic-sleep 20000 --cyclic-dist 1000 -t 5
stress-ng: info: [27594] dispatching hogs: 1 cyclic
stress-ng: info: [27595] stress-ng-cyclic: sched SCHED_FIFO: 20000 ns delay, 10000 samples
stress-ng: info: [27595] stress-ng-cyclic: mean: 5242.86 ns, mode: 4880 ns
stress-ng: info: [27595] stress-ng-cyclic: min: 3050 ns, max: 44818 ns, 1142.92
stress-ng: info: [27595] stress-ng-cyclic: latency percentiles:
stress-ng: info: [27595] stress-ng-cyclic: 25.00%: 4881 us
stress-ng: info: [27595] stress-ng-cyclic: 50.00%: 5191 us
stress-ng: info: [27595] stress-ng-cyclic: 75.00%: 5261 us
stress-ng: info: [27595] stress-ng-cyclic: 90.00%: 5368 us
stress-ng: info: [27595] stress-ng-cyclic: 95.40%: 6857 us
stress-ng: info: [27595] stress-ng-cyclic: 99.00%: 8942 us
stress-ng: info: [27595] stress-ng-cyclic: 99.50%: 9821 us
stress-ng: info: [27595] stress-ng-cyclic: 99.90%: 22210 us
stress-ng: info: [27595] stress-ng-cyclic: 99.99%: 36074 us
stress-ng: info: [27595] stress-ng-cyclic: latency distribution (1000 us intervals):
stress-ng: info: [27595] stress-ng-cyclic: latency (us) frequency
stress-ng: info: [27595] stress-ng-cyclic: 0 0
stress-ng: info: [27595] stress-ng-cyclic: 1000 0
stress-ng: info: [27595] stress-ng-cyclic: 2000 0
stress-ng: info: [27595] stress-ng-cyclic: 3000 82
stress-ng: info: [27595] stress-ng-cyclic: 4000 3342
stress-ng: info: [27595] stress-ng-cyclic: 5000 5974
stress-ng: info: [27595] stress-ng-cyclic: 6000 197
stress-ng: info: [27595] stress-ng-cyclic: 7000 209
stress-ng: info: [27595] stress-ng-cyclic: 8000 100
stress-ng: info: [27595] stress-ng-cyclic: 9000 50
stress-ng: info: [27595] stress-ng-cyclic: 10000 10
stress-ng: info: [27595] stress-ng-cyclic: 11000 9
stress-ng: info: [27595] stress-ng-cyclic: 12000 2
stress-ng: info: [27595] stress-ng-cyclic: 13000 2
stress-ng: info: [27595] stress-ng-cyclic: 14000 1
stress-ng: info: [27595] stress-ng-cyclic: 15000 9
stress-ng: info: [27595] stress-ng-cyclic: 16000 1
stress-ng: info: [27595] stress-ng-cyclic: 17000 1
stress-ng: info: [27595] stress-ng-cyclic: 18000 0
stress-ng: info: [27595] stress-ng-cyclic: 19000 0
stress-ng: info: [27595] stress-ng-cyclic: 20000 0
stress-ng: info: [27595] stress-ng-cyclic: 21000 1
stress-ng: info: [27595] stress-ng-cyclic: 22000 1
stress-ng: info: [27595] stress-ng-cyclic: 23000 0
stress-ng: info: [27595] stress-ng-cyclic: 24000 1
stress-ng: info: [27595] stress-ng-cyclic: 25000 2
stress-ng: info: [27595] stress-ng-cyclic: 26000 0
stress-ng: info: [27595] stress-ng-cyclic: 27000 1
stress-ng: info: [27595] stress-ng-cyclic: 28000 1
stress-ng: info: [27595] stress-ng-cyclic: 29000 2
stress-ng: info: [27595] stress-ng-cyclic: 30000 0
stress-ng: info: [27595] stress-ng-cyclic: 31000 0
stress-ng: info: [27595] stress-ng-cyclic: 32000 0
stress-ng: info: [27595] stress-ng-cyclic: 33000 0
stress-ng: info: [27595] stress-ng-cyclic: 34000 0
stress-ng: info: [27595] stress-ng-cyclic: 35000 0
stress-ng: info: [27595] stress-ng-cyclic: 36000 1
stress-ng: info: [27595] stress-ng-cyclic: 37000 0
stress-ng: info: [27595] stress-ng-cyclic: 38000 0
stress-ng: info: [27595] stress-ng-cyclic: 39000 0
stress-ng: info: [27595] stress-ng-cyclic: 40000 0
stress-ng: info: [27595] stress-ng-cyclic: 41000 0
stress-ng: info: [27595] stress-ng-cyclic: 42000 0
stress-ng: info: [27595] stress-ng-cyclic: 43000 0
stress-ng: info: [27595] stress-ng-cyclic: 44000 1
stress-ng: info: [27594] successful run completed in 5.00s

Note that stress-ng needs to be invoked using sudo to enable the Real Time FIFO scheduling for the cyclic measurements.

The above example uses the following options:

  • --cyclic 1
    • starts one instance of the cyclic measurements (1 is always recommended)
  • --cyclic-policy fifo 
    • use the real time First-In-First-Out scheduling for the cyclic measurements
  • --cyclic-prio 100 
    • use the maximum scheduling priority  
  • --cyclic-method clock_ns
    • use the clock_nanoseconds(2) system call to perform the high precision duration sleep
  • --cyclic-sleep 20000 
    • sleep for 20000 nanoseconds per cyclic iteration
  • --cyclic-dist 1000 
    • enable latency distribution statistics with an interval of 1000 nanoseconds between each data point.
  • -t 5
    • run for just 5 seconds
From the run above, we can see that 99.5% of latencies were less than 9821 nanoseconds and most clustered around the 4880 nanosecond model point. The distribution data shows that there is some clustering around the 5000 nanosecond point and the samples tail off with a bit of a long tail.

Now for the interesting part. Since stress-ng is packed with many different stressors we can run these while performing the cyclic measurements, for example, we can tell stress-ng to run *all* the virtual memory related stress tests and see how this affects the latency distribution using the following:

 sudo stress-ng --cyclic 1 --cyclic-policy fifo \  
--cyclic-prio 100 --cyclic-method clock_ns \
--cyclic-sleep 20000 --cyclic-dist 1000 \
--class vm --all 1 -t 60s

..the above invokes all the vm class of stressors to run all at the same time (with just one instance of each stressor) for 60 seconds.

The --cyclic-method specifies the delay used on each of the 10,000 cyclic iterations used.  The default (and recommended method) is clock_ns, using the high precision delay.  The available cyclic delay methods are:
  • clock_ns (use the clock_nanosecond() sleep)
  • posix_ns (use the POSIX nanosecond() sleep)
  • itimer (use a high precision clock timer and pause to wait for a signal to measure latency)
  • poll (busy spin-wait on clock_gettime() to eat cycles for a delay.
All the delay mechanisms use the CLOCK_REALTIME system clock for timing.

I hope this is plenty of cyclic measurement functionality to get some useful latency benchmarks against various kernel components when using some or a mix of the stress-ng stressors.  Let me know if I am missing some other cyclic measurement options and I can see if I can add them in.

Keep stressing and measuring those systems!

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Colin Ian King

The Firmware Test Suite (FWTS) has an easy to use text based front-end that is primarily used by the FWTS Live-CD image but it can also be used in the Ubuntu terminal.

To install and run the front-end use:

 sudo apt-get install fwts-frontend  
sudo fwts-frontend-text

..and one should see a menu of options:

In this demonstration, the "All Batch Tests" option has been selected:

Tests will be run one by one and a progress bar shows the progress of each test. Some tests run very quickly, others can take several minutes depending on the hardware configuration (such as number of processors).

Once the tests are all complete, the following dialogue box is displayed:

The test has saved several files into the directory /fwts/15052017/1748/ and selecting Yes one can view the results log in a scroll-box:

Exiting this, the FWTS frontend dialog is displayed:

Press enter to exit (note that the Poweroff option is just for the fwts Live-CD image version of fwts-frontend).

The tool dumps various logs, for example, the above run generated:

 ls -alt /fwts/15052017/1748/  
total 1388
drwxr-xr-x 5 root root 4096 May 15 18:09 ..
drwxr-xr-x 2 root root 4096 May 15 17:49 .
-rw-r--r-- 1 root root 358666 May 15 17:49 acpidump.log
-rw-r--r-- 1 root root 3808 May 15 17:49 cpuinfo.log
-rw-r--r-- 1 root root 22238 May 15 17:49 lspci.log
-rw-r--r-- 1 root root 19136 May 15 17:49 dmidecode.log
-rw-r--r-- 1 root root 79323 May 15 17:49 dmesg.log
-rw-r--r-- 1 root root 311 May 15 17:49 README.txt
-rw-r--r-- 1 root root 631370 May 15 17:49 results.html
-rw-r--r-- 1 root root 281371 May 15 17:49 results.log

acpidump.log is a dump of the ACPI tables in format compatible with the ACPICA acpidump tool.  The results.log file is a copy of the results generated by FWTS and results.html is a HTML formatted version of the log.

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Colin Ian King

The BPF Compiler Collection (BCC) is a toolkit for building kernel tracing tools that leverage the functionality provided by the Linux extended Berkeley Packet Filters (BPF).

BCC allows one to write BPF programs with front-ends in Python or Lua with kernel instrumentation written in C.  The instrumentation code is built into sandboxed eBPF byte code and is executed in the kernel.

The BCC github project README file provides an excellent overview and description of BCC and the various available BCC tools.  Building BCC from scratch can be a bit time consuming, however,  the good news is that the BCC tools are now available as a snap and so BCC can be quickly and easily installed just using:

 sudo snap install --devmode bcc  

There are currently over 50 BCC tools in the snap, so let's have a quick look at a few:

cachetop allows one to view the top page cache hit/miss statistics. To run this use:

 sudo bcc.cachetop  

The funccount tool allows one to count the number of times specific functions get called.  For example, to see how many kernel functions with the name starting with "do_" get called per second one can use:

 sudo bcc.funccount "do_*" -i 1  

To see how to use all the options in this tool, use the -h option:

 sudo bcc.funccount -h  

I've found the funccount tool to be especially useful to check on kernel activity by checking on hits on specific function names.

The slabratetop tool is useful to see the active kernel SLAB/SLUB memory allocation rates:

 sudo bcc.slabratetop  

If you want to see which process is opening specific files, one can snoop on open system calls use the opensnoop tool:

 sudo bcc.opensnoop -T

Hopefully this will give you a taste of the useful tools that are available in BCC (I have barely scratched the surface in this article).  I recommend installing the snap and giving it a try.

As it stands,BCC provides a useful mechanism to develop BPF tracing tools and I look forward to regularly updating the BCC snap as more tools are added to BCC. Kudos to Brendan Gregg for BCC!

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Colin Ian King

Recently I've been adding a few more features into stress-ng to get improve kernel code coverage.   I'm currently using a kernel built with gcov enabled and using the most excellent lcov tool to collate the coverage data and produce some rather useful coverage charts.

With a gcov enabled kernel, gathering coverage stats is a trivial process with lcov:

 sudo apt-get install lcov  
sudo lcov --zerocounters
stress-ng --seq 0 -t 60
sudo lcov -c -o
sudo genhtml -o html

..and the html output appears in the html directory.

In the latest 0.06.00 release of stress-ng, the following new features have been introduced:

  • af-alg stressor, added skciphers and rngs
  • new Translation Lookaside Buffer (TLB) shootdown stressor
  • new /dev/full stressor
  • hdd stressor now works through all the different hdd options if --maximize is used
  • wider procfs stressing
  • added more keyctl commands to the key stressor
  • new msync stressor, exercise msync of mmap'd memory back to file and from file back to memory.
  • Real Time Clock (RTC) stressor (via /dev/rtc and /proc/driver/rtc)
  • taskset option, allowing one to run stressors on specific CPUs (affinity setting)
  • inotify stressor now also exercises the FIONREAD ioctl()
  • and some bug fixes found when testing stress-ng on various architectures.
The --taskset option allows one to keep stress-ng stressors bound to specific CPUs, for example, to run 5 CPU stressors tied to CPUs 1, 3, 5, 6 and 7:

 stress-ng --taskset 1,3,5-7 --cpu 5  

..thanks to Jim Rowan (Intel) for the CPU affinity ideas.

stress-ng 0.06.00 will be landing in Ubunty Yakkety soon, and also in my power utilities PPA ppa:colin-king/white

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Colin Ian King

New "top" mode in eventstat

I wrote eventstat a few years ago to track wakeup events that keep a machine from being fully idle.  For Ubuntu Xenial Xerus 16.04 I've added a 'top' like mode (enabled using the -T option).

By widening the terminal one can see more of the Task, Init Function and Callback text, which is useful as these details can be rather lengthy.

Anyhow, just a minor feature change, but hopefully a useful one.

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Colin Ian King

One issue when running parallel processes is contention of shared resources such as the Last Level Cache (aka LLC or L3 Cache).  For example, a server may be running a set of Virtual Machines with processes that are memory and cache intensive hence producing a large amount of cache activity. This can impact on the other VMs and is known as the "Noisy Neighbour" problem.

Fortunately the next generation Intel processors allow one to monitor and also fine tune cache allocation using Intel Cache Monitoring Technology (CMT) and Cache Allocation Technology (CAT).

Intel kindly loaned me a 12 thread development machine with CMT and CAT support to experiment with this technology using the Intel pqos tool.   For my experiment, I installed Ubuntu Xenial Server on the machine. I then installed KVM and an VM instance of Ubuntu Xenial Server.   I then loaded the instance using stress-ng running a memory bandwidth stressor:

 stress-ng --stream 1 -v --stream-l3-size 16M  
..which allocates 16MB in 4 buffers and performs various read/compute and writes to these, hence causing a "noisy neighbour".

Using pqos,  one can monitor and see the cache/memory activity:
sudo apt-get install intel-cmt-cat
sudo modprobe msr
sudo pqos -r
TIME 2016-02-04 10:25:06
0 0.59 168259k 9144.0 12195.0 0.0
1 1.33 107k 0.0 3.3 0.0
2 0.20 2k 0.0 0.0 0.0
3 0.70 104k 0.0 2.0 0.0
4 0.86 23k 0.0 0.7 0.0
5 0.38 42k 24.0 1.5 0.0
6 0.12 2k 0.0 0.0 0.0
7 0.24 48k 0.0 3.0 0.0
8 0.61 26k 0.0 1.6 0.0
9 0.37 11k 144.0 0.9 0.0
10 0.48 1k 0.0 0.0 0.0
11 0.45 2k 0.0 0.0 0.0
Now to run a stress-ng stream stressor on the host and see the performance while the noisy neighbour is also running:
stress-ng --stream 4 --stream-l3-size 2M --perf --metrics-brief -t 60
stress-ng: info: [2195] dispatching hogs: 4 stream
stress-ng: info: [2196] stress-ng-stream: stressor loosely based on a variant of the STREAM benchmark code
stress-ng: info: [2196] stress-ng-stream: do NOT submit any of these results to the STREAM benchmark results
stress-ng: info: [2196] stress-ng-stream: Using L3 CPU cache size of 2048K
stress-ng: info: [2196] stress-ng-stream: memory rate: 1842.22 MB/sec, 736.89 Mflop/sec (instance 0)
stress-ng: info: [2198] stress-ng-stream: memory rate: 1847.88 MB/sec, 739.15 Mflop/sec (instance 2)
stress-ng: info: [2199] stress-ng-stream: memory rate: 1833.89 MB/sec, 733.56 Mflop/sec (instance 3)
stress-ng: info: [2197] stress-ng-stream: memory rate: 1847.16 MB/sec, 738.86 Mflop/sec (instance 1)
stress-ng: info: [2195] successful run completed in 60.01s (1 min, 0.01 secs)
stress-ng: info: [2195] stressor bogo ops real time usr time sys time bogo ops/s bogo ops/s
stress-ng: info: [2195] (secs) (secs) (secs) (real time) (usr+sys time)
stress-ng: info: [2195] stream 22101 60.01 239.93 0.04 368.31 92.10
stress-ng: info: [2195] stream:
stress-ng: info: [2195] 547,520,600,744 CPU Cycles 9.12 B/sec
stress-ng: info: [2195] 69,959,954,760 Instructions 1.17 B/sec (0.128 instr. per cycle)
stress-ng: info: [2195] 11,066,905,620 Cache References 0.18 B/sec
stress-ng: info: [2195] 11,065,068,064 Cache Misses 0.18 B/sec (99.98%)
stress-ng: info: [2195] 8,759,154,716 Branch Instructions 0.15 B/sec
stress-ng: info: [2195] 2,205,904 Branch Misses 36.76 K/sec ( 0.03%)
stress-ng: info: [2195] 23,856,890,232 Bus Cycles 0.40 B/sec
stress-ng: info: [2195] 477,143,689,444 Total Cycles 7.95 B/sec
stress-ng: info: [2195] 36 Page Faults Minor 0.60 sec
stress-ng: info: [2195] 0 Page Faults Major 0.00 sec
stress-ng: info: [2195] 96 Context Switches 1.60 sec
stress-ng: info: [2195] 0 CPU Migrations 0.00 sec
stress-ng: info: [2195] 0 Alignment Faults 0.00 sec
.. so about 1842 MB/sec memory rate and 736 Mflop/sec per CPU across 4 CPUs.  And pqos shows the cache/memory actitivity as:
sudo pqos -r
TIME 2016-02-04 10:35:27
0 0.14 43060k 1104.0 2487.9 0.0
1 0.12 3981523k 2616.0 2893.8 0.0
2 0.26 320k 48.0 18.0 0.0
3 0.12 3980489k 1800.0 2572.2 0.0
4 0.12 3979094k 1728.0 2870.3 0.0
5 0.12 3970996k 2112.0 2734.5 0.0
6 0.04 20k 0.0 0.3 0.0
7 0.04 29k 0.0 1.9 0.0
8 0.09 143k 0.0 5.9 0.0
9 0.15 0k 0.0 0.0 0.0
10 0.07 2k 0.0 0.0 0.0
11 0.13 0k 0.0 0.0 0.0
Using pqos again, we can find out how much LLC cache the processor has:
sudo pqos -v
NOTE: Mixed use of MSR and kernel interfaces to manage
CAT or CMT & MBM may lead to unexpected behavior.
INFO: Monitoring capability detected
INFO: CPUID.0x7.0: CAT supported
INFO: CAT details: CDP support=0, CDP on=0, #COS=16, #ways=12, ways contention bit-mask 0xc00
INFO: LLC cache size 9437184 bytes, 12 ways
INFO: LLC cache way size 786432 bytes
INFO: L3CA capability detected
INFO: Detected PID API (perf) support for LLC Occupancy
INFO: Detected PID API (perf) support for Instructions/Cycle
INFO: Detected PID API (perf) support for LLC Misses
ERROR: IPC and/or LLC miss performance counters already in use!
Use -r option to start monitoring anyway.
Monitoring start error on core(s) 5, status 6
So this CPU has 12 cache "ways", each of 786432 bytes (768K).  One or more  "Class of Service" (COS)  types can be defined that can use one or more of these ways.  One uses a bitmap with each bit representing a way to indicate how the ways are to be used by a COS.  For example, to use all the 12 ways on my example machine, the bit map is 0xfff  (111111111111).   A way can be exclusively mapped to a COS or shared, or not used at all.   Note that the ways in the bitmap must be contiguously allocated, so a mask such as 0xf3f (111100111111) is invalid and cannot be used.

In my experiment, I want to create 2 COS types, the first COS will have just 1 cache way assigned to it and CPU 0 will be bound to this COS as well as pinning the VM instance to CPU 0  The second COS will have the other 11 cache ways assigned to it, and all the other CPUs can use this COS.

So, create COS #1 with just 1 way of cache, and bind CPU 0 to this COS, and pin the VM to CPU 0:
sudo pqos -e llc:1=0x0001
sudo pqos -a llc:1=0
sudo taskset -apc 0 $(pidof qemu-system-x86_64)
And create COS #2, with 11 ways of cache and bind CPUs 1-11 to this COS:
sudo pqos -e "llc:2=0x0ffe"
sudo pqos -a "llc:2=1-11"
And let's see the new configuration:
sudo pqos  -s
NOTE: Mixed use of MSR and kernel interfaces to manage
CAT or CMT & MBM may lead to unexpected behavior.
L3CA COS definitions for Socket 0:
L3CA COS0 => MASK 0xfff
L3CA COS1 => MASK 0x1
L3CA COS2 => MASK 0xffe
L3CA COS3 => MASK 0xfff
L3CA COS4 => MASK 0xfff
L3CA COS5 => MASK 0xfff
L3CA COS6 => MASK 0xfff
L3CA COS7 => MASK 0xfff
L3CA COS8 => MASK 0xfff
L3CA COS9 => MASK 0xfff
L3CA COS10 => MASK 0xfff
L3CA COS11 => MASK 0xfff
L3CA COS12 => MASK 0xfff
L3CA COS13 => MASK 0xfff
L3CA COS14 => MASK 0xfff
L3CA COS15 => MASK 0xfff
Core information for socket 0:
Core 0 => COS1, RMID0
Core 1 => COS2, RMID0
Core 2 => COS2, RMID0
Core 3 => COS2, RMID0
Core 4 => COS2, RMID0
Core 5 => COS2, RMID0
Core 6 => COS2, RMID0
Core 7 => COS2, RMID0
Core 8 => COS2, RMID0
Core 9 => COS2, RMID0
Core 10 => COS2, RMID0
Core 11 => COS2, RMID0
..showing Core 0 bound to COS1, and Cores 1-11 bound to COS2, with COS1 with 1 cache way and COS2 with the remaining 11 cache ways.
Now re-run the stream stressor and see if the VM has less impact on the LL3 cache:
stress-ng --stream 4 --stream-l3-size 1M --perf --metrics-brief -t 60
stress-ng: info: [2232] dispatching hogs: 4 stream
stress-ng: info: [2233] stress-ng-stream: stressor loosely based on a variant of the STREAM benchmark code
stress-ng: info: [2233] stress-ng-stream: do NOT submit any of these results to the STREAM benchmark results
stress-ng: info: [2233] stress-ng-stream: Using L3 CPU cache size of 1024K
stress-ng: info: [2235] stress-ng-stream: memory rate: 2616.90 MB/sec, 1046.76 Mflop/sec (instance 2)
stress-ng: info: [2233] stress-ng-stream: memory rate: 2562.97 MB/sec, 1025.19 Mflop/sec (instance 0)
stress-ng: info: [2234] stress-ng-stream: memory rate: 2541.10 MB/sec, 1016.44 Mflop/sec (instance 1)
stress-ng: info: [2236] stress-ng-stream: memory rate: 2652.02 MB/sec, 1060.81 Mflop/sec (instance 3)
stress-ng: info: [2232] successful run completed in 60.00s (1 min, 0.00 secs)
stress-ng: info: [2232] stressor bogo ops real time usr time sys time bogo ops/s bogo ops/s
stress-ng: info: [2232] (secs) (secs) (secs) (real time) (usr+sys time)
stress-ng: info: [2232] stream 62223 60.00 239.97 0.00 1037.01 259.29
stress-ng: info: [2232] stream:
stress-ng: info: [2232] 547,364,185,528 CPU Cycles 9.12 B/sec
stress-ng: info: [2232] 97,037,047,444 Instructions 1.62 B/sec (0.177 instr. per cycle)
stress-ng: info: [2232] 14,396,274,512 Cache References 0.24 B/sec
stress-ng: info: [2232] 14,390,808,440 Cache Misses 0.24 B/sec (99.96%)
stress-ng: info: [2232] 12,144,372,800 Branch Instructions 0.20 B/sec
stress-ng: info: [2232] 1,732,264 Branch Misses 28.87 K/sec ( 0.01%)
stress-ng: info: [2232] 23,856,388,872 Bus Cycles 0.40 B/sec
stress-ng: info: [2232] 477,136,188,248 Total Cycles 7.95 B/sec
stress-ng: info: [2232] 44 Page Faults Minor 0.73 sec
stress-ng: info: [2232] 0 Page Faults Major 0.00 sec
stress-ng: info: [2232] 72 Context Switches 1.20 sec
stress-ng: info: [2232] 0 CPU Migrations 0.00 sec
stress-ng: info: [2232] 0 Alignment Faults 0.00 sec
Now with the noisy neighbour VM constrained to use just 1 way of LL3 cache, the stream stressor on the host now can achieve about 2592 MB/sec and about 1030 Mflop/sec per CPU across 4 CPUs.

This is a relatively simple example.  With the ability to monitor cache and memory bandwidth activity with one can carefully tune a system to make best use of the limited LL3 cache resource and maximise throughput where needed.

There are many applications where Intel CMT/CAT can be useful, for example fine tuning containers or VM instances, or pinning user space networking buffers to cache ways in DPDK for improved throughput.

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Colin Ian King

Pagemon improvements

Over the past month I've been finding the odd moments [1] to add some small improvements and fix a few bugs to pagemon (a tool to monitor process memory).  The original code went from a sketchy proof of concept prototype to a somewhat more usable tool in a few weeks, so my main concern recently was to clean up the code and make it more efficient.

With the use of tools such as valgrind's cachegrind and perf I was able to work on some of the code hot-spots [2] and reduce it from ~50-60% CPU down to 5-9% CPU utilisation on my laptop, so it's definitely more machine friendly now.  In addition I've added the following small features:

  • Now one can specify the name of a process to monitor as well as the PID.  This also allows one to run pagemon on itself(!), which is a bit meta.
  • Perf events showing Page Faults and Kernel Page Allocates and Frees, toggled on/off with the 'p' key.
  • Improved and snappier clean up and exit when a monitored process exits.
  • Far more efficient page map reading and rendering.
  • Out of Memory (OOM) scores added to VM statistics window.
  • Process activity (busy, sleeping, etc) to VM statistics window.
  • Zoom mode min/max with '[' (min) and ']' (max) keys.
  • Close pop-up windows with key 'c'.
  • Improved handling of rapid map expansion and shrinking.
  • Jump to end of map using 'End' key.
  • Improve the man page.
I've tried to keep the tool small and focused and I don't want feature bloat to make it unwieldy and overly complexed.  "Do one job, and do it well" is the philosophy behind pagemon. At just 1500 lines of C, it is as complex as I want it to be for now.

Version 0.01.08 should be hitting the Ubuntu 16.04 Xenial Xerus archive in the next 24 hours or so.  I have also the lastest version in my PPA (ppa:colin-king/pagemon) built for Trusty, Vivid, Wily and Xenial.

Pagemon is useful for spotting unexpected memory activity and it is just interesting watching the behaviour memory hungry processes such as web-browsers and Virtual Machines.

[1] Mainly very late at night when I can't sleep (but that's another story...).  The git log says it all.
[2] Reading in /proc/$PID/maps and efficiently reading per page data from /proc/$PID/pagemap

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Colin Ian King

A useful feature on modern x86 CPUs is the Running Average Power Limit (RAPL) that allows one to monitor System on Chip (SoC) power consumption.  Combine this data with the ability to accurately measure CPU cycles and instructions via perf and we can get some way to get a rough estimate energy consumed to perform a single operation on the CPU.

power-calibrate is a simple tool that  hacked up to perform some synthetic loading of the processor, gather the RAPL and CPU stats and using simple linear regression to compute some power related metrics.

In the example below, I run power-calibrate on an Intel  i5-3210M (2 Cores, 4 threads) with each test run taking 10 seconds (-r 10),  using the RAPL interface to measure power and gathering 11 samples on CPU threads 1..4:

power-calibrate -r 10 -R  -s 11
CPU load User Sys Idle Run Ctxt/s IRQ/s Ops/s Cycl/s Inst/s Watts
0% x 1 0.1 0.1 99.8 1.0 181.6 61.1 0.0 2.5K 380.2 2.485
0% x 2 0.0 1.0 98.9 1.2 161.8 63.8 0.0 5.7K 0.8K 2.366
0% x 3 0.1 1.3 98.5 1.1 204.2 75.2 0.0 7.6K 1.9K 2.518
0% x 4 0.1 0.1 99.9 1.0 124.7 44.9 0.0 11.4K 2.7K 2.167
10% x 1 2.4 0.2 97.4 1.5 203.8 104.9 21.3M 123.1M 297.8M 2.636
10% x 2 5.1 0.0 94.9 1.3 185.0 137.1 42.0M 243.0M 0.6B 2.754
10% x 3 7.5 0.2 92.3 1.2 275.3 190.3 58.1M 386.9M 0.8B 3.058
10% x 4 10.0 0.1 89.9 1.9 213.5 206.1 64.5M 486.1M 0.9B 2.826
20% x 1 5.0 0.1 94.9 1.0 288.8 170.0 69.6M 403.0M 1.0B 3.283
20% x 2 10.0 0.1 89.9 1.6 310.2 248.7 96.4M 0.8B 1.3B 3.248
20% x 3 14.6 0.4 85.0 1.7 640.8 450.4 238.9M 1.7B 3.3B 5.234
20% x 4 20.0 0.2 79.8 2.1 633.4 514.6 270.5M 2.1B 3.8B 4.736
30% x 1 7.5 0.2 92.3 1.4 444.3 278.7 149.9M 0.9B 2.1B 4.631
30% x 2 14.8 1.2 84.0 1.2 541.5 418.1 200.4M 1.7B 2.8B 4.617
30% x 3 22.6 1.5 75.9 2.2 960.9 694.3 365.8M 2.6B 5.1B 7.080
30% x 4 30.0 0.2 69.8 2.4 959.2 774.8 421.1M 3.4B 5.9B 5.940
40% x 1 9.7 0.3 90.0 1.7 551.6 356.8 201.6M 1.2B 2.8B 5.498
40% x 2 19.9 0.3 79.8 1.4 668.0 539.4 288.0M 2.4B 4.0B 5.604
40% x 3 29.8 0.5 69.7 1.8 1124.5 851.8 481.4M 3.5B 6.7B 7.918
40% x 4 40.3 0.5 59.2 2.3 1186.4 1006.7 0.6B 4.6B 7.7B 6.982
50% x 1 12.1 0.4 87.4 1.7 536.4 378.6 193.1M 1.1B 2.7B 4.793
50% x 2 24.4 0.4 75.2 2.2 816.2 668.2 362.6M 3.0B 5.1B 6.493
50% x 3 35.8 0.5 63.7 3.1 1300.2 1004.6 0.6B 4.2B 8.2B 8.800
50% x 4 49.4 0.7 49.9 3.8 1455.2 1240.0 0.7B 5.7B 9.6B 8.130
60% x 1 14.5 0.4 85.1 1.8 735.0 502.7 295.7M 1.7B 4.1B 6.927
60% x 2 29.4 1.3 69.4 2.0 917.5 759.4 397.2M 3.3B 5.6B 6.791
60% x 3 44.1 1.7 54.2 3.1 1615.4 1243.6 0.7B 5.1B 9.9B 10.056
60% x 4 58.5 0.7 40.8 4.0 1728.1 1456.6 0.8B 6.8B 11.5B 9.226
70% x 1 16.8 0.3 82.9 1.9 841.8 579.5 349.3M 2.0B 4.9B 7.856
70% x 2 34.1 0.8 65.0 2.8 966.0 845.2 439.4M 3.7B 6.2B 6.800
70% x 3 49.7 0.5 49.8 3.5 1834.5 1401.2 0.8B 5.9B 11.8B 11.113
70% x 4 68.1 0.6 31.4 4.7 1771.3 1572.3 0.8B 7.0B 11.8B 8.809
80% x 1 18.9 0.4 80.7 1.9 871.9 613.0 357.1M 2.1B 5.0B 7.276
80% x 2 38.6 0.3 61.0 2.8 1268.6 1029.0 0.6B 4.8B 8.2B 9.253
80% x 3 58.8 0.3 40.8 3.5 2061.7 1623.3 1.0B 6.8B 13.6B 11.967
80% x 4 78.6 0.5 20.9 4.0 2356.3 1983.7 1.1B 9.0B 16.0B 12.047
90% x 1 21.8 0.3 78.0 2.0 1054.5 737.9 459.3M 2.6B 6.4B 9.613
90% x 2 44.2 1.2 54.7 2.7 1439.5 1174.7 0.7B 5.4B 9.2B 10.001
90% x 3 66.2 1.4 32.4 3.9 2326.2 1822.3 1.1B 7.6B 15.0B 12.579
90% x 4 88.5 0.2 11.4 4.8 2627.8 2219.1 1.3B 10.2B 17.8B 12.832
100% x 1 25.1 0.0 74.8 2.0 135.8 314.0 0.5B 3.1B 7.5B 10.278
100% x 2 50.0 0.0 50.0 3.0 91.9 560.4 0.7B 6.2B 10.4B 10.470
100% x 3 75.1 0.1 24.8 4.0 120.2 824.1 1.2B 8.7B 16.8B 13.028
100% x 4 100.0 0.0 0.0 5.0 76.8 1054.8 1.4B 11.6B 19.5B 13.156

For 4 CPUs (of a 4 CPU system):
Power (Watts) = (% CPU load * 1.176217e-01) + 3.461561
1% CPU load is about 117.62 mW
Coefficient of determination R^2 = 0.809961 (good)

Energy (Watt-seconds) = (bogo op * 8.465141e-09) + 3.201355
1 bogo op is about 8.47 nWs
Coefficient of determination R^2 = 0.911274 (strong)

Energy (Watt-seconds) = (CPU cycle * 1.026249e-09) + 3.542463
1 CPU cycle is about 1.03 nWs
Coefficient of determination R^2 = 0.841894 (good)

Energy (Watt-seconds) = (CPU instruction * 6.044204e-10) + 3.201433
1 CPU instruction is about 0.60 nWs
Coefficient of determination R^2 = 0.911272 (strong)

The results at the end are estimates based on the gathered samples. The samples are compared to the computed linear regression coefficients using the coefficient of determination (R^2);  a value of 1 is a perfect linear fit, less than 1 a poorer fit.

For more accurate results, increase the run time (-r option) and also increase the number of samples (-s option).

Power-calibrate is available in Ubuntu Wily 15.10.  It is just an academic toy for getting some power estimates and may be useful to compare compute vs power metrics across different x86 CPUs.  I've not been able to verify how accurate it really is, so I am interested to see how this works across a range of systems.

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Colin Ian King

NumaTop: A NUMA system monitoring tool

NumaTop is a useful tool developed by Intel for monitoring runtime memory locality and analysis of processes on Non-Uniform Memory Access (NUMA) systems.  NumaTop can identify potential NUMA related performance bottlenecks and hence help one to re-balance memory/CPU allocations to maximise the potential of a NUMA system.

Initial "Top" like process view

One can select specific processes and drill down and characteristics such as memory latencies or call chains to see where code is hot.

Observing a specific process..
..and observing memory latencies
Observing per Node CPU and memory statistics
The tool uses perf to collect deeper system statistics and hence needs to be run with root privileges will only run on NUMA systems. I've recently packaged NumaTop and it is now available in Ubuntu Wily 15.10 and the source is available on github.

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Colin Ian King

light-weight process stats with cpustat

A while ago I was working on identifying busy processes on small Ubuntu devices and required a tool that could look at per process stats (from /proc/$pid/stat) in a fast and efficient way with minimal overhead.   There are plenty of tools such as "top" and "atop" that can show per-process CPU utilisation stats, but most of these aren't useful on really slow low-power devices as they consume several tens of megacycles collecting and displaying the results.

I developed cpustat to be compact and efficient, as well as provide enough stats to allow me to easily identify CPU sucking processes.   To optimise the code, I used tools such as perf to identify code hotspots as well as valgrind's cachegrind to identify poorly designed cache inefficient data structures.

The majority of the savings were in the parsing of data from /proc - originally I used simple fscanf() style parsing; over several optimisation rounds I ended up with hand-crafted numeric and string scanning parsing that saved several hundred thousand cycles per iteration.

I also made some optimisations by tweaking the hash table sizes to match the input data more appropriately.  Also, by careful re-use of heap allocations, I was able to reduce malloc()/free() calls and save some heap management overhead.

Some very frequent string look-ups were replaced with hash lookups and frequently accessed data was duplicated rather than referenced indirectly to keep data local to reduce cache stalls and hence speed up data comparison lookup time.

The source has been statically checked by CoverityScan, cppcheck and also clang's scan-build to check for bugs introduced in the optimisation steps.

Example of cpustat
cpustat is now available in Ubuntu 15.10 Wily Werewolf.   Visit the cpustat project page for more details.

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Colin Ian King

static code analysis (revisited)

A while ago I was extolling the virtues of static analysis tools such as cppcheck, smatch and CoverityScan for C and C++ projects.  I've recently added to this armoury the clang analyser scan-build, which has been most helpful in finding even more obscure bugs that the previous three did not catch.

Using scan-build is very simple indeed, install clang and then in your source tree just build your project with scan-build, e.g. for a project built by make, use:

scan-build make
..and at the end of a build one will see a summary message:
scan-build make
scan-build: 366 bugs found.
scan-build: Run 'scan-view /tmp/scan-build-2015-09-08-094505-16657-1' 
to examine bug reports.
scan-build: The analyzer encountered problems on some source files.
scan-build: Preprocessed versions of these sources were deposited in 
scan-build: Please consider submitting a bug report using these files:

..and running scan-view will show the issues found.  For an example of the kind of results scan-build can find, I ran it against a systemd build (head commit 4df0514d299e349ce1d0649209155b9e83a23539). 

As one can see, scan-build is a powerful and easy to use open-source static analyser.  I heartily recommend using it on every C and C++ project.

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Colin Ian King

Monitoring temperatures with psensor

While doing some thermal debugging this weekend I stumbled upon the rather useful temperature monitoring utility "Psensor".   I configured it to update stats every second and according to perf it was only using 0.02 CPU's worth of compute, so it seems relatively lightweight and shouldn't contribute to warming the machine up!

I like the min/max values being clearly shown and also the ability to change graph colours and toggle data on or off.  Quick, easy and effective.  Not sure why I haven't found this tool earlier, but I wish I had!

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Colin Ian King

Identifying Suspend/Resume delays

The Intel SuspendResume project aims to help identify delays in suspend and resume.  After seeing it demonstrated by Len Brown (Intel) at this years Linux Plumbers conference I gave it a quick spin and was delighted to see how easy it is to use.

The project has some excellent "getting started" documentation describing how to configure a system and run the suspend resume analysis script which should be read before diving in too deep.

For the impatient, one can do try it out using the following:

git clone
cd suspendresume
sudo ./

..and manually resume once after the machine has completed a successful suspend.

This will create a directory containing dumps of the kernel log and ftrace output as well as an html web page that one can read into your favourite web browser to view the results.  One can zoom in/out of the web page to drill down and see where the delays are occurring, an example from the SuspendResume project page is shown below:

example webpage (from

It is a useful project, kudos to Intel for producing it.  I thoroughly recommend using it to identify the delays in suspend/resume.

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Colin Ian King

The Canonical Hardware Enablement Team and myself are continuing the work to add more ACPI table tests to the Firmware Test Suite (fwts).  The latest 15.08.00 release added sanity checks for the following tables:

The release also added a test for the ACPI _CPC revision 2 control method and we updated the ACPICA core to version 20150717.

Our aim is to continue to add support for existing and new ACPI tables to make fwts a comprehensive firmware test tool.  For more information about fwts, please refer to the fwts jump start wiki page.

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Colin Ian King

Powerstat and thermal zones

Last night I was mulling over an overheating laptop issue that was reported by a user that turned out to be fluff and dust clogging up the fan rather than the intel_pstate driver being broken.

While it is a relief that the kernel driver is not at fault, it still bothered me that this kind of issue should be very simple to diagnose but I overlooked the obvious.   When solving these issues it is very easy to doubt that the complex part of a system is working correctly (e.g. a kernel driver) rather than the simpler part (e.g. the fan not working efficiently).  Normally, I try to apply Occam's Razor which in the simplest form can be phrased as:

"when you have two competing theories that make exactly the same predictions, the simpler one is the better."

..e.g. in this case, the fan is clogged up.

Fortunately, laptops invariably provide Thermal Zone information that can be monitored and hence one can correlate CPU activity with the temperature of various components of a laptop.  So last night I added Thermal Zone sampling to powerstat 0.02.00 which is enabled with the new -t option.

powerstat -tfR 0.5
Running for 60.0 seconds (120 samples at 0.5 second intervals).
Power measurements will start in 0 seconds time.

Time User Nice Sys Idle IO Run Ctxt/s IRQ/s Watts x86_pk acpitz CPU Freq
11:13:15 5.1 0.0 2.1 92.8 0.0 1 7902 1152 7.97 62.00 63.00 1.93 GHz
11:13:16 3.9 0.0 2.5 93.1 0.5 1 7168 960 7.64 63.00 63.00 2.73 GHz
11:13:16 1.0 0.0 2.0 96.9 0.0 1 7014 950 7.20 63.00 63.00 2.61 GHz
11:13:17 2.0 0.0 3.0 94.5 0.5 1 6950 960 6.76 64.00 63.00 2.60 GHz
11:13:17 3.0 0.0 3.0 93.9 0.0 1 6738 994 6.21 63.00 63.00 1.68 GHz
11:13:18 3.5 0.0 2.5 93.6 0.5 1 6976 948 7.08 64.00 63.00 2.29 GHz

..the -t option now shows x86_pk (x86 CPU package temperature) and acpitz (APCI thermal zone) temperature readings in degrees Celsius.

Now this is where the fun begins.  I ran powerstat for 60 seconds at 2 samples per second and then imported the data into LibreOffice.  To easily show corrleations between CPU load, power consumption, temperature and CPU frequency I normalized the data so that the lowest values were 0.0 and the highest were 1.0 and produced the following graph:

One can see that the CPU frequency (green) scales with the the CPU load (blue) and so does the CPU power (orange).   CPU temperature (yellow) jumps up quickly when the CPU is loaded and then steadily increases.  Meanwhile, the ACPI thermal zone (purple) trails the CPU load because it takes time for the machine to warm up and then cool down (it takes time for a fan to pump out the heat from the machine).

So, next time a laptop runs hot, running powerstat will capture the activity and correlating temperature with CPU activity should allow one to see if the overheating is related to a real CPU frequency scaling issue or a clogged up fan (or broken heat pipe!).

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Colin Ian King

Snooping on I/O using iosnoop

A while ago I blogged about Brendan Gregg's excellent book for tracking down performance issues titled "Systems Performance, Enterprise and the Cloud".   Brendan has also produced a useful I/O diagnostic bash script iosnoop that uses ftrace to gather block device I/O events in real time.

The following example snoops on I/O for 1 second:

$ sudo iosnoop 1
Tracing block I/O for 1 seconds (buffered)...
kworker/u16:2 650 W 8,0 441077032 28672 1.46
kworker/u16:2 650 W 8,0 441077024 4096 1.45
kworker/u16:2 650 W 8,0 364810624 462848 1.35
kworker/u16:2 650 W 8,0 364810240 69632 1.34

And the next example snoops and shows start and end time stamps:
$ sudo iosnoop -ts  
Tracing block I/O. Ctrl-C to end.
35253.062020 35253.063148 jbd2/sda1-211 211 WS 8,0 29737200 53248 1.13
35253.063210 35253.063261 jbd2/sda1-211 211 FWS 8,0 18446744073709551615 0 0.05
35253.063282 35253.063616 <idle> 0 WS 8,0 29737304 4096 0.33
35253.063650 35253.063688 gawk 551 FWS 8,0 18446744073709551615 0 0.04
35253.766711 35253.767158 kworker/u16:0 305 W 8,0 433580264 4096 0.45
35253.766778 35253.767258 kworker/0:1H 321 FWS 8,0 18446744073709551615 0 0.48
35253.767289 35253.767635 <idle> 0 WS 8,0 273358464 4096 0.35
35253.767309 35253.767654 <idle> 0 W 8,0 118371312 4096 0.35
35253.767648 35253.767741 <idle> 0 FWS 8,0 18446744073709551615 0 0.09
Ending tracing...
One needs to run the tool as root as it uses ftrace. There are a selection of filtering options, such as showing I/O from a specific device, I/O issues of a specific I/O type, selecting I/O on a specific PID or a specific name. iosnoop also can display the I/O completion times, start times and Queue insertion I/O start time. On Ubuntu, iosnoop can be installed using:
sudo apt-get install perf-tools-unstable
A useful I/O analysis tool indeed. For more details, install the tool and read the iosnoop man page.

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Colin Ian King

Over the last few weeks I have been toying with the idea of adding more performance monitoring to stress-ng so one can see how much a stress test impacts on the CPU. The obvious choice to get such low level data is via Linux perf events using perf_event_open(2).

The man page for perf_event_open() provides plenty of information to get perf working from userspace, however, I was a bit stumped when I used several hardware perf events and then ran out of hardware Perf Monitoring Units (PMUs) resulting in some strange event counter readings. I discovered that when one runs out of PMUs, perf will multiplex event counting and so the perf counters need to be scaled by multiplying by PERF_FORMAT_TOTAL_TIME_ENABLED and divided by PERF_FORMAT_TOTAL_TIME_RUNNING.

Once I had figured this out, it was relatively plain sailing to get perf working in stress-ng.  So stress-ng V0.04.04 now supports the --perf option that just enables perf monitoring on each stress test being run, it is as simple as that. For multiple instances of a stress test, stress-ng will sum all the perf counters of each processes running the stress-test to provide an overall total.

The following example will run the stress-ng cache stress test.  The first run enables cache flushing and so fetches of data will cause cache misses.  The second run has no cache flushing and hence has far lower cache miss rate.

Note how the cache-flushing not only causes a far higher cache miss rate, but also reduces the effective number of instructions per cycle being executed and hence reduces the throughput (as one would expect).  With cache-flushing enabled I was seeing only 17.53 bogo ops per second compared to the 35.97 bogo ops per second with cache-flushing disabled.

The perf stats are enlightening. I still find it incredible that my laptop has so much computing power.  Some of the more compute bound stressors (such as the stress-ng bitops cpu stressor) are hitting over 20 billion instructions per second on my machine, which is rather impressive.  It seems that gcc optimization and the x86 superscaler micro-ops are working efficiently with some of these stress tests.

My hope is that the integrated perf monitoring in stress-ng will be instructive when comparing results on different processor architectures across the range of stress-ng stress tests.

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Colin Ian King

As simple experiment, I thought it would be interesting to investigate stress-ng compiled with GCC 4.9.1 and GCC 5.1.1 in terms of computational improvement and power consumption on various CPU stress methods.   The stress-ng CPU stress test contains various different mixes of integer, floating point, bit operations and logic operations that can be used for processor loading, so it makes a useful test to see how well the code gets optimized with GCC.

Stress-ng provides a "bogo-ops" mechanism to measure a "unit of operation", normally this is just a count of the number of operations performed in a unit of time, hence allowing us to compare the relative performance of each stress method when compiled with different versions of GCC.  Running each stress method for a relatively long time (a few minutes) on an idle machine allows us to get a fairly stable and accurate measurement of bogo-ops per second.  Tests were run on a Lenovo x230 with an i5-3210M CPU.

The first chart below shows the relative improvement in bogo-ops per second between the two versions of GCC.  A value of n indicates GCC 5.1.1 is n times faster  in terms of bogo-ops per second than GCC 4.9.1, hence values less than 1.0 show that GCC 5.1.1 has regressed in performance.

It appears that int64, int32, int16, int8 and rand show some remarkable improvements with GCC 5.1.1; these all perform various integer operations (add, subtract, multiply, divide, xor, and, or, shift).

In contrast, hamming, hanoi, parity and sieve show degraded performance with GCC 5.1.1.  Hanoi just exercises recursion of a function with a few arguments and some memory load/stores.  Hamming, parity and sieve exercise bit twiddling operations and memory load/stores.

Further to just measuring computation, I used the Intel RAPL CPU package power measurements (using powerstat) to next measure the power consumed and then compute bogo ops per Watt for stress-ng built with GCC 4.9.1 and 5.1.1.  I then compared the relative improvement of 5.1.1 compared to 4.9.1:
The chart above shows the same kind of characteristics as the first chart, but in terms of computational improvement per Watt.  Note that there are even better improvements in relative terms for the integer and rand CPU stress methods.  For example, the rand stress method shows a 1.6 x improvement in terms of computation per second and a 2.1 x improvement in terms of computation per Watt comparing GCC 4.9.1 with 5.1.1.

It seems that benchmarking performance in terms of just compute improvements really should take into consideration the power consumption too to get a better idea of how compiler optimization improvements.  Compute-per-watt rather than compute-per-second should perhaps be the preferred benchmark in the modern high-density compute farms.

Of course, these comparisons are just with one specific x86 micro-architecture,  so one would expect different results for different x86 CPUs..  I guess that is for another weekend to test if I get time.

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Colin Ian King

comparing cpuburn and stress-ng

The cpuburn package contains several hand crafted assembler "burn" programs to load x86 processors and to maximize heat production to stress a system.  This also is the intention of the stress-ng "cpu" stress test which contains a variety of methods to stress CPUs with a wide range of instruction mixes.   Stress-ng is written in C and relies on the the compiler to generate efficient code to hopefully load the CPU.  So how does stress-ng compared to the hand crafted cpuburn suite of programs on modern processors?

Since there is a correlation between power consumed and heat generated, I took the liberty to measure the CPU package power consumption measures using the Intel RAPL interface as one way of comparing cpuburn and stress-ng.  Recent versions of powerstat supports RAPL, so I ran each stressor for 120 seconds and took CPU package power measurements every 4 seconds over this interval with powerstat.

So, the cpuburn "burn" programs do well, however, some of the stress-ng CPU stress methods seem to do better.   The best stress-ng CPU methods are: ackermann, callfunc, hanoi, decimal128, dither, int128decimal128, trig and zeta.  It appears that ackermann, callfunc and hanoi do well because these are very localised deeply recursive function calls, so I expect register save/restores and some stack activity is the main power consumer.  The rest exercise the integer and floating point units and memory load/stores.

As it stands, a handful of stress-ng CPU stressors aren't as good as cpuburn. What is noticeable is that burnBX on an i3120M seems to do rather well in terms of loading the CPU.

One conclusion to draw from this is that modern C compilers such as gcc (in this case, gcc 4.9.2) with a suitably chosen mix of stores, loads and integer/floating point operations can outperform hand written assembler in terms of loading the full CPU package.  When I have a little more time, I will try and repeat this experiment with clang and gcc 5

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Colin Ian King

An on-going background project of mine is to add various interesting system stress tests to stress-ng.  Over the past several months I've been looking at the ways to exercise various less used or obscure system calls just to add more kernel coverage to the tool.

  • rlimit - generate tens of thousands of SIGXFSZ and many SIGXCPU signals
  • itimer - exercise ITIMER_PROF and generate SIGPROF signals
  • mlock - lock and unlock pages with mlock()/munlock()
  • timerfd - exercise rapid CLOCK_REALTIME events by select() and read() on a timerfd.
  • memfd - exercise anonymous populated page memory mapping and unmappoing using memfd.
  • more aggressive affinity stressor changes to force more CPU IPIs
  • hdd - add readv/writev I/O option
  • tee - tee data between a writer and reader process using tee()
  • crypt - encrypt data with MD5, SHA-256 and SHA-512 using libcrypt
  • mmapmany - perform tens of thousands of memory maps/unmaps to exhaust the per-process mapping limit.
  • zombie - fill up process table with tens of thousands of zombie processes
  • str - heavily exercise a range of glibc string functions
  • xattr - exercise file extended attributes
  • readahead - random reads with readaheads
  • vm - add a rowhammer memory stressor well as extra per-stressor configuration settings and a lot of code clean up and bug fixing.

I've recently been using stress-ng to exercise various kernels on a range of hardware and it has been useful in forcing bugs, especially with the memory specific stressors that seem to trip low memory corner cases.

stress-ng 0.04.01 will be soon available in Ubuntu 15.10 Wily Werewolf.  Visit the stress-ng project page for more details.

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