Blazing Performance
with
Flame Graphs
Brendan Gregg

An Interactive Visualization for Stack Traces

My Previous Visualizations Include
• Latency Heat Maps (and other heat map types), including:
• Quotes from LISA'13 yesterday:
• "Heat maps are a wonderful thing, use them" – Caskey Dickson
• "If you do distributed systems, you need this" – Theo Schlossnagle
• I did heat maps and visualizations in my LISA'10 talk

Audience
• This is for developers, sysadmins, support staff, and
performance engineers
• This is a skill-up for everyone: beginners to experts
• This helps analyze all software: kernels and applications

whoami
• G’Day, I’m Brendan
• Recipient of the LISA 2013 Award
for Outstanding Achievement
in System Administration!
(Thank you!)
• Work/Research: tools,
methodologies, visualizations
• Author of Systems Performance,
primary author of DTrace
(Prentice Hall, 2011)
• Lead Performance Engineer
@joyent; also teach classes:
Cloud Perf coming up: http://www.joyent.com/developers/training-services

Joyent
• High-Performance Cloud Infrastructure
• Public/private cloud provider
• OS-Virtualization for bare metal performance
• KVM for Linux guests
• Core developers of
SmartOS and node.js
• Office walls decorated
with Flame Graphs:

Agenda: Two Talks in One
• 1. CPU Flame Graphs
• Example
• Background
• Flame Graphs
• Generation
• Types: CPU
• 2. Advanced Flame Graphs
• Types: Memory, I/O, Off-CPU, Hot/Cold, Wakeup
• Developments
• SVG demos: https://github.com/brendangregg/FlameGraph/demos

CPU Flame Graphs

Example

Example
• As a short example, I’ll describe the real world performance
issue that led me to create flame graphs
• Then I’ll explain them in detail

Example: The Problem
• A production MySQL database had poor performance
• It was a heavy CPU consumer, so I used a CPU profiler to see
why. It sampled stack traces at timed intervals
• The profiler condensed its output by only printing unique
stacks along with their occurrence counts, sorted by count
• The following shows the profiler command and the two most
frequently sampled stacks...

Example: CPU Profiling
# dtrace -x ustackframes=100 -n 'profile-997 /execname == "mysqld"/ {
@[ustack()] = count(); } tick-60s { exit(0); }'
dtrace: description 'profile-997 ' matched 2 probes
CPU
FUNCTION:NAME
1 75195
:tick-60s
[...]
libc.so.1`__priocntlset+0xa
libc.so.1`getparam+0x83
libc.so.1`pthread_getschedparam+0x3c
libc.so.1`pthread_setschedprio+0x1f
mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x9ab
mysqld`_Z10do_commandP3THD+0x198
mysqld`handle_one_connection+0x1a6
libc.so.1`_thrp_setup+0x8d
libc.so.1`_lwp_start
mysqld`_Z13add_to_statusP17system_status_varS0_+0x47
mysqld`_Z22calc_sum_of_all_statusP17system_status_var+0x67
mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x1222
mysqld`_Z10do_commandP3THD+0x198
mysqld`handle_one_connection+0x1a6
libc.so.1`_thrp_setup+0x8d
libc.so.1`_lwp_start

Example: CPU Profiling
# dtrace -x ustackframes=100 -n 'profile-997 /execname == "mysqld"/ {
@[ustack()] = count(); } tick-60s { exit(0); }'
dtrace: description 'profile-997 ' matched 2 probes
CPU
FUNCTION:NAME
Profiling
1 75195
:tick-60s
Command
[...]
libc.so.1`__priocntlset+0xa
(DTrace)
libc.so.1`getparam+0x83
libc.so.1`pthread_getschedparam+0x3c
libc.so.1`pthread_setschedprio+0x1f
mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x9ab
mysqld`_Z10do_commandP3THD+0x198
mysqld`handle_one_connection+0x1a6
libc.so.1`_thrp_setup+0x8d
libc.so.1`_lwp_start
Stack
Trace
mysqld`_Z13add_to_statusP17system_status_varS0_+0x47
mysqld`_Z22calc_sum_of_all_statusP17system_status_var+0x67
mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x1222
mysqld`_Z10do_commandP3THD+0x198
mysqld`handle_one_connection+0x1a6
libc.so.1`_thrp_setup+0x8d
libc.so.1`_lwp_start
# of occurrences

Example: Profile Data
• Over 500,000 lines were elided from that output (“[...]”)
• Full output looks like this...

Example: Profile Data
60 seconds of on-CPU MySQL

Example: Profile Data
First
Stack
Size of
One Stack
Last
Stack
27,053
Unique
60 seconds
ofStacks
on-CPU MySQL

Example: Profile Data
• The most frequent stack, printed last, shows CPU usage in
add_to_status(), which is from the “show status” command.
Is that to blame?
• Hard to tell – it only accounts for 

Example:Visualizations
• To understand this profile data quickly, I created visualization
that worked very well, named “Flame Graph” for its
resemblance to fire (also as it was showing a “hot” CPU issue)
Profile Data.txt
Flame Graph.svg
some
Perl

Example: Flame Graph
Same profile data

Example: Flame Graph
Same profile data
Where CPU is
really consumed
The
"show
status"
Stack
All Stack Samples
One Stack
Sample

Example: Flame Graph
• All data in one picture
• Interactive using JavaScript and a browser: mouse overs
• Stack elements that are frequent can be seen, read, and
compared visually. Frame width is relative to sample count
• CPU usage was now understood properly and quickly,
leading to a 40% performance win

Background

Background: Stack Frame
• A stack frame shows a location in code
• Profilers usually show them on a single line. Eg:
libc.so.1`mutex_trylock_adaptive+0x112
module
function
instruction
offset

Background: Stack Trace
• A stack trace is a list of frames. Their index is the stack depth:
current
libc.so.1`mutex_trylock_adaptive+0x112 24
parent
parent
libc.so.1`mutex_lock_impl+0x165
parent
grand
parent
libc.so.1`mutex_lock+0xc
Stack
Depth
[...]
libc.so.1`_lwp_start

Background: Stack Trace
• One full stack:
libc.so.1`mutex_trylock_adaptive+0x112
libc.so.1`mutex_lock_impl+0x165
libc.so.1`mutex_lock+0xc
mysqld`key_cache_read+0x741
mysqld`_mi_fetch_keypage+0x48
mysqld`w_search+0x84
mysqld`_mi_ck_write_btree+0xa5
mysqld`mi_write+0x344
mysqld`ha_myisam::write_row+0x43
mysqld`handler::ha_write_row+0x8d
mysqld`end_write+0x1a3
mysqld`evaluate_join_record+0x11e
mysqld`sub_select+0x86
mysqld`do_select+0xd9
mysqld`JOIN::exec+0x482
mysqld`mysql_select+0x30e
mysqld`handle_select+0x17d
mysqld`execute_sqlcom_select+0xa6
mysqld`mysql_execute_command+0x124b
mysqld`mysql_parse+0x3e1
mysqld`dispatch_command+0x1619
mysqld`do_handle_one_connection+0x1e5
mysqld`handle_one_connection+0x4c
libc.so.1`_thrp_setup+0xbc
libc.so.1`_lwp_start

Background: Stack Trace
• Read top-down or bottom-up, and look for key functions
libc.so.1`mutex_trylock_adaptive+0x112
libc.so.1`mutex_lock_impl+0x165
libc.so.1`mutex_lock+0xc
mysqld`key_cache_read+0x741
mysqld`_mi_fetch_keypage+0x48
mysqld`w_search+0x84
mysqld`_mi_ck_write_btree+0xa5
mysqld`mi_write+0x344
mysqld`ha_myisam::write_row+0x43
mysqld`handler::ha_write_row+0x8d
mysqld`end_write+0x1a3
mysqld`evaluate_join_record+0x11e
mysqld`sub_select+0x86
mysqld`do_select+0xd9
mysqld`JOIN::exec+0x482
mysqld`mysql_select+0x30e
mysqld`handle_select+0x17d
mysqld`execute_sqlcom_select+0xa6
mysqld`mysql_execute_command+0x124b
mysqld`mysql_parse+0x3e1
mysqld`dispatch_command+0x1619
mysqld`do_handle_one_connection+0x1e5
mysqld`handle_one_connection+0x4c
libc.so.1`_thrp_setup+0xbc
libc.so.1`_lwp_start
Ancestry
Code Path

Background: Stack Modes
• Two types of stacks can be profiled:
• user-level for applications (user mode)
• kernel-level for the kernel (kernel mode)
• During a system call, an application may have both

Background: Software Internals
• You don’t need to be a programmer to understand stacks.
• Some function names are self explanatory, others require
source code browsing (if available). Not as bad as it sounds:
• MySQL has ~15,000 functions in >gt; 0.5 million lines of code
• The earlier stack has 20 MySQL functions. To understand
them, you may need to browse only 0.13%
(20 / 15000) of the code. Might take hours, but it is doable.
• If you have C++ signatures, you can use a demangler first:
mysqld`_ZN4JOIN4execEv+0x482
gc++filt, demangler.com
mysqld`JOIN::exec()+0x482

Background: Stack Visualization
• Stack frames can be visualized as rectangles (boxes)
• Function names can be truncated to fit
• In this case, color is chosen randomly (from a warm palette)
to differentiate adjacent frames
libc.so.1`mutex_trylock_adaptive+0x112
libc.so.1`mutex_trylock_...
libc.so.1`mutex_lock_impl+0x165
libc.so.1`mutex_lock_imp...
libc.so.1`mutex_lock+0xc
libc.so.1`mutex_lock+0xc
mysqld`key_cache_read+0x741
mysqld`key_cache_read+0x741
• A stack trace becomes a column of colored rectangles

Background: Time Series Stacks
• Time series ordering allows time-based pattern identification
• However, stacks can change thousands of times per second
Stack
Depth
Time (seconds)

Background: Time Series Stacks
• Time series ordering allows time-based pattern identification
• However, stacks can change thousands of times per second
One Stack
Sample
Stack
Depth
Time (seconds)

Background: Frame Merging
• When zoomed out, stacks appear as narrow stripes
• Adjacent identical functions can be merged to improve
readability, eg:
mu...
mu...
ge...
muex_tryl...
ge...
mu...
mu...
mu...
mutex_lock_impl()
mu...
mu...
mu...
mutex_lock()
ke...
ke...
ke...
key_cache_read()
• This sometimes works: eg, a repetitive single threaded app
• Often does not (previous slide already did this), due to code
execution between samples or parallel thread execution

Background: Frame Merging
• Time-series ordering isn’t necessary for the primary use case:
identify the most common (“hottest”) code path or paths
• By using a different x-axis sort order, frame merging can be
greatly improved...

Flame Graphs

Flame Graphs
• Flame Graphs sort stacks alphabetically. This sort is applied
from the bottom frame upwards. This increases merging and
visualizes code paths.
Stack
Depth
Alphabet

Flame Graphs: Definition
• Each box represents a function (a merged stack frame)
• y-axis shows stack depth
• top function led directly to the profiling event
• everything beneath it is ancestry (explains why)
• x-axis spans the sample population, sorted alphabetically
• Box width is proportional to the total time a function was
profiled directly or its children were profiled
• All threads can be shown in the same Flame Graph (the
default), or as separate per-thread Flame Graphs
• Flame Graphs can be interactive: mouse over for details

Flame Graphs:Variations
• Profile data can be anything: CPU, I/O, memory, ...
• Naming suggestion: [event] [units] Flame Graph
• Eg: "FS Latency Flame Graph"
• By default, Flame Graphs == CPU Sample Flame Graphs
• Colors can be used for another dimension
• by default, random colors are used to differentiate boxes
• --hash for hash-based on function name
• Distribution applications can be shown in the same Flame
Graph (merge samples from multiple systems)

Flame Graphs: A Simple Example
• A CPU Sample Flame Graph:
f()
d()
e()
c()
h()
b()
g()
a()
• I’ll illustrate how these are read by posing various questions

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
f()
d()
e()
c()
h()
b()
g()
a()
• Q: which function is on-CPU the most?

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
f()
d()
top edge shows
who is on-CPU
directly
e()
c()
h()
b()
g()
a()
• Q: which function is on-CPU the most?
• A: f()
e() is on-CPU a
little, but its runtime
is mostly spent in f(),
which is on-CPU directly

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
f()
d()
e()
c()
h()
b()
g()
a()
• Q: why is f() on-CPU?

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
f() was called by e()
e() was called by c()
f()
d()
e()
ancestry
c()
h()
b()
g()
a()
• Q: why is f() on-CPU?
• A: a() → b() → c() → e() → f()

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
f()
d()
e()
c()
h()
b()
g()
a()
• Q: how does b() compare to g()?

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
visually compare
lengths
f()
d()
e()
c()
h()
b()
g()
a()
• Q: how does b() compare to g()?
• A: b() looks like it is running (present) about 10 times more
often than g()

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
... or mouse over
f()
d()
e()
c()
h()
b()
g()
a()
status line
or tool tip:
b() is 90%
• Q: how does b() compare to g()?
• A: for interactive Flame Graphs, mouse over shows b() is
90%, g() is 10%

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
... or mouse over
f()
d()
e()
c()
h()
b()
g()
a()
status line
or tool tip:
g() is 10%
• Q: how does b() compare to g()?
• A: for interactive Flame Graphs, mouse over shows b() is
90%, g() is 10%

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
f()
d()
e()
c()
h()
b()
g()
a()
• Q: why are we running f()?

Flame Graphs: How to Read
• A CPU Sample Flame Graph:
look for
branches
f()
d()
e()
c()
h()
b()
g()
a()
• Q: why are we running f()?
• A: code path branches can reveal key functions:
• a() choose the b() path
• c() choose the e() path

Flame Graphs: Example 1
• Customer alerting software periodically checks a log, however,
it is taking too long (minutes).
• It includes grep(1) of an ~18 Mbyte log file, which takes
around 10 minutes!
• grep(1) appears to be on-CPU for this time. Why?

Flame Graphs: Example 1
• CPU Sample Flame Graph for grep(1) user-level stacks:

Flame Graphs: Example 1
• CPU Sample Flame Graph for grep(1) user-level stacks:
UTF8?
• 82% of samples are in check_multibyte_string() or its children.
This seems odd as the log file is plain ASCII.
• And why is UTF8 on the scene? ... Oh, LANG=en_US.UTF-8

Flame Graphs: Example 1
• CPU Sample Flame Graph for grep(1) user-level stacks:
UTF8?
• Switching to LANG=C improved performance by 2000x
• A simple example, but I did spot this from the raw profiler text
before the Flame Graph. You really need Flame Graphs when
the text gets too long and unwieldy.

Flame Graphs: Example 2
• A potential customer benchmarks disk I/O on a cloud instance.
The performance is not as fast as hoped.
• The host has new hardware and software. Issues with the new
type of disks is suspected.

Flame Graphs: Example 2
• A potential customer benchmarks disk I/O on a cloud instance.
The performance is not as fast as hoped.
• The host has new hardware and software. Issues with the new
type of disks is suspected.
• I take a look, and notice CPU time in the kernel is modest.
• I’d normally assume this was I/O overheads and not profile it
yet, instead beginning with I/O latency analysis.
• But Flame Graphs make it easy, and it may be useful to see
what code paths (illumos kernel) are on the table.

Flame Graphs: Example 2

Flame Graphs: Example 2
• 24% in tsc_read()? Time Stamp Counter? Checking ancestry...

Flame Graphs: Example 2
• 62% in zfs_zone_io_throttle? Oh, we had forgotten that this
new platform had ZFS I/O throttles turned on by default!

Flame Graphs: Example 3
• Application performance is about half that of a competitor
• Everything is believed identical (H/W, application, config,
workload) except for the OS and kernel
• Application is CPU busy, nearly 100% in user-mode. How can
the kernel cause a 2x delta when the app isn't in kernel-mode?
• Flame graphs on both platforms for user-mode were created:
• Linux, using perf
• SmartOS, using DTrace
• Added flamegraph.pl --hash option for consistent function
colors (not random), aiding comparisons

Flame Graphs: Example 3
Extra Function:
UnzipDocid()
Linux
SmartOS
• Function label formats are different, but that's just due to
different profilers/stackcollapse.pl's (should fix this)
• Widths slighly different, but we already know perf differs
• Extra function? This is executing different application software!
SphDocID_t
UnzipDocid ()
{ return UnzipOffset(); }
• Actually, a different compiler option was eliding this function

Flame Graphs: More Examples
• Flame Graphs are typically
more detailed, like the earlier
MySQL example
• Next, how to generate them,
then more examples

Generation

Generation
• I’ll describe the original Perl version I wrote and shared on
github:
• https://github.com/brendangregg/FlameGraph
• There are other great Flame Graph implementations with
different features and usage, which I’ll cover in the last section

Generation: Steps
• 1. Profile event of interest
• 2. stackcollapse.pl
• 3. flamegraph.pl

Generation: Overview
• Full command line example. This uses DTrace for CPU
profiling of the kernel:
# dtrace -x stackframes=100 -n 'profile-997 /arg0/ {
@[stack()] = count(); } tick-60s { exit(0); }' -o out.stacks
# stackcollapse.pl gt; out.folded
# flamegraph.pl gt; out.svg
• Then, open out.svg in a browser
• Intermediate files could be avoided (piping), but they can be
handy for some manual processing if needed (eg, using vi)

Generation: Profiling Data
• The profile data, at a minimum, is a series of stack traces
• These can also include stack trace counts. Eg:
mysqld`_Z13add_to_statusP17system_status_varS0_+0x47
mysqld`_Z22calc_sum_of_all_statusP17system_status_var+0x67
mysqld`_Z16dispatch_command19enum_server_commandP3THDPcj+0x1222
mysqld`_Z10do_commandP3THD+0x198
mysqld`handle_one_connection+0x1a6
libc.so.1`_thrp_setup+0x8d
libc.so.1`_lwp_start
# of occurrences for this stack
• This example is from DTrace, which prints a series of these.
The format of each group is: stack, count, newline
• Your profiler needs to print full (not truncated) stacks, with
symbols. This may be step 0: get the profiler to work!

Generation: Profiling Tools
• Solaris/FreeBSD/SmartOS/...:
• DTrace
• Linux:
• perf, SystemTap
• OS X:
• Instruments
• Windows:
• Xperf.exe

Generation: Profiling Examples: DTrace
• CPU profile kernel stacks at 997 Hertz, for 60 secs:
# dtrace -x stackframes=100 -n 'profile-997 /arg0/ {
@[stack()] = count(); } tick-60s { exit(0); }' -o out.kern_stacks
• CPU profile user-level stacks for PID 12345 at 99 Hertz, 60s:
# dtrace -x ustackframes=100 -n 'profile-97 /PID == 12345 && arg1/ {
@[ustack()] = count(); } tick-60s { exit(0); }' -o out.user_stacks
• Should also work on Mac OS X, but is pending some fixes
preventing stack walking (use Instruments instead)
• Should work for Linux one day with the DTrace ports

Generation: Profiling Examples: perf
• CPU profile full stacks at 97 Hertz, for 60 secs:
# perf record -a -g -F 97 sleep 60
# perf script >gt; out.stacks
• Need debug symbol packages installed (*dbgsym), otherwise
stack frames may show as hexidecimal
• May need compilers to cooperate (-fno-omit-frame-pointer)
• Has both user and kernel stacks, and the kernel idle thread.
Can filter the idle thread after stackcollapse-perf.pl using:
# stackcollapse-perf.pl 

Generation: Profiling Examples: SystemTap
• CPU profile kernel stacks at 100 Hertz, for 60 secs:
# stap -s 32 -D MAXTRACE=100 -D MAXSTRINGLEN=4096 -D MAXMAPENTRIES=10240 \
-D MAXACTION=10000 -D STP_OVERLOAD_THRESHOLD=5000000000 --all-modules \
-ve 'global s; probe timer.profile { s[backtrace()] gt; out.kern_stacks
• Need debug symbol packages installed (*dbgsym), otherwise
stack frames may show as hexidecimal
• May need compilers to cooperate (-fno-omit-frame-pointer)

Generation: Dynamic Languages
• C or C++ are usually easy to profile, runtime environments
(JVM, node.js, ...) are usually not, typically a way to show
program stacks and not just runtime internals.
• Eg, DTrace’s ustack helper for node.js:
0xfc618bc0
0xfc61bd62
0xfe870841
0xfc61c1f3
0xfc617685
0xfe870841
0xfc6154d7
0xfe870e1a
[...]
libc.so.1`gettimeofday+0x7
Date at position
gt;>gt;
gt;>gt;
(anon) as exports.active at timers.js position 7590
(anon) as Socket._write at net.js position 21336
(anon) as Socket.write at net.js position 19714
gt;>gt;
(anon) as OutgoingMessage._writeRaw at http.js p...
(anon) as OutgoingMessage._send at http.js posit...
gt;>gt;
(anon) as OutgoingMessage.end at http.js pos...
[...]
http://dtrace.org/blogs/dap/2012/01/05/where-does-your-node-program-spend-its-time/

Generation: stackcollapse.pl
• Converts profile data into a single line records
• Variants exist for DTrace, perf, SystemTap, Instruments, Xperf
• Eg, DTrace:
unix`i86_mwait+0xd
unix`cpu_idle_mwait+0xf1
unix`idle+0x114
unix`thread_start+0x8
# stackcollapse.pl gt; out.folded
unix`thread_start;unix`idle;unix`cpu_idle_mwait;unix`i86_mwait 19486

Generation: stackcollapse.pl
• Converts profile data into a single line records
• Variants exist for DTrace, perf, SystemTap, Instruments, Xperf
• Eg, DTrace:
unix`i86_mwait+0xd
unix`cpu_idle_mwait+0xf1
unix`idle+0x114
unix`thread_start+0x8
# stackcollapse.pl gt; out.folded
unix`thread_start;unix`idle;unix`cpu_idle_mwait;unix`i86_mwait 19486
stack trace, frames are ‘;’ delimited
count

Generation: stackcollapse.pl
• Full output is many lines, one line per stack
• Bonus: can be grepped
# ./stackcollapse-stap.pl out.stacks | grep ext4fs_dirhash
system_call_fastpath;sys_getdents;vfs_readdir;ext4_readdir;ext4_htree_fill_
tree;htree_dirblock_to_tree;ext4fs_dirhash 100
system_call_fastpath;sys_getdents;vfs_readdir;ext4_readdir;ext4_htree_fill_
tree;htree_dirblock_to_tree;ext4fs_dirhash;half_md4_transform 505
system_call_fastpath;sys_getdents;vfs_readdir;ext4_readdir;ext4_htree_fill_
tree;htree_dirblock_to_tree;ext4fs_dirhash;str2hashbuf_signed 353
[...]
• That shows all stacks containing ext4fs_dirhash(); useful
debug aid by itself
• grep can also be used to filter stacks before Flame Graphs
• eg: grep -v cpu_idle

Generation: Final Output
• Desires:
• Full control of output
• High density detail
• Portable: easily viewable
• Interactive

Generation: Final Output
• Desires:
• Full control of output
• High density detail
• Portable: easily viewable
PNG
SVG+JS
• Interactive
• SVG+JS: Scalable Vector Graphics with embedded JavaScript
• Common standards, and supported by web browsers
• Can print poster size (scalable); but loses interactivity!
• Can be emitted by a simple Perl program...

Generation: flamegraph.pl
• Converts folded stacks into an interactive SVG. Eg:
# flamegraph.pl --titletext="Flame Graph: MySQL" out.folded >gt; graph.svg
• Options:
--titletext
change the title text (default is “Flame Graph”)
--width
width of image (default is 1200)
--height
height of each frame (default is 16)
--minwidth
omit functions smaller than this width (default is 0.1 pixels)
--fonttype
font type (default “Verdana”)
--fontsize
font size (default 12)
--countname
count type label (default “samples”)
--nametype
name type label (default “Function:”)
--colors
color palette: "hot", "mem", "io"
--hash
colors are keyed by function name hash

Types

Types
• CPU
• Memory
• Off-CPU
• More

CPU

CPU
• Measure code paths that consume CPU
• Helps us understand and optimize CPU usage, improving
performance and scalability
• Commonly performed by sampling CPU stack traces at a
timed interval (eg, 100 Hertz for every 10 ms), on all CPUs
• DTrace/perf/SystemTap examples shown earlier
• Can also be performed by tracing function execution

CPU: Sampling
CPU stack sampling:
syscall
On-CPU
Off-CPU
block . . . . . . . . . interrupt
user-level
kernel

CPU: Tracing
CPU function tracing:
syscall
On-CPU
Off-CPU
block . . . . . . . . . interrupt
user-level
kernel

CPU: Profiling
• Sampling:
• Coarse but usually effective
• Can also be low overhead, depending on the stack type
and sample rate, which is fixed (eg, 100 Hz x CPU count)
• Tracing:
• Overheads can be too high, distorting results and hurting
the target (eg, millions of trace events per second)
• Most Flame Graphs are generated using stack sampling

CPU: Profiling Results
• Example results. Could you do this?
As an experiment to investigate the performance of the
resulting TCP/IP implementation ... the 11/750 is CPU
saturated, but the 11/780 has about 30% idle time. The time
spent in the system processing the data is spread out among
handling for the Ethernet (20%), IP packet processing (10%),
TCP processing (30%), checksumming (25%), and user
system call handling (15%), with no single part of the handling
dominating the time in the system.

CPU: Profiling Results
• Example results. Could you do this?
As an experiment to investigate the performance of the
resulting TCP/IP implementation ... the 11/750 is CPU
saturated, but the 11/780 has about 30% idle time. The time
spent in the system processing the data is spread out among
handling for the Ethernet (20%), IP packet processing (10%),
TCP processing (30%), checksumming (25%), and user
system call handling (15%), with no single part of the handling
dominating the time in the system.
– Bill Joy, 1981, TCP-IP Digest, Vol 1 #6
• An impressive report, that even today would be difficult to do
• Flame Graphs make this a lot easier

CPU: Another Example
• A file system is archived using tar(1).
• The files and directories are cached, and the run time is
mostly on-CPU in the kernel (Linux). Where exactly?

CPU: Another Example

CPU: Another Example
• 20% for reading directories

CPU: Another Example
• 54% for file statistics

CPU: Another Example
• Also good for learning kernel internals: browse the active code

CPU: Recognition
• Once you start profiling a target, you begin to recognize the
common stacks and patterns
• Linux getdents() ext4 path:
• The next slides show similar
example kernel-mode CPU
Sample Flame Graphs

CPU: Recognition: illumos localhost TCP
• From a TCP localhost latency issue (illumos kernel):
illumos
fused-TCP
receive
illumos
fused-TCP
send

CPU: Recognition: illumos IP DCE issue
DCE
lookup
DCE
lookup
DCE
lookup

CPU: Recognition: Linux TCP send
• Profiled from a KVM guest:
Linux TCP
sendmsg

CPU: Recognition: Syscall Towers

CPU: Recognition: Syscall Towers
lstat()
open()
close()
writev()
pollsys()
read()
write()
stat()
stat64()
bnx
xmit
sendfile()
bnx
recv
ip fanout
receive

CPU: Both Stacks
• Apart from showing either user- or kernel-level stacks, both
can be included by stacking kernel on top of user
• Linux perf does this by default
• DTrace can by aggregating @[stack(), ustack()]
• The different stacks can be highlighted in different ways:
• different colors or hues
• separator: flamegraph.pl will color gray any functions
called "-", which can be inserted as stack separators
• Kernel stacks are only present during syscalls or interrupts

CPU: Both Stacks Example: KVM/qemu
user
only
kernel
stack
user
stack

Advanced Flame Graphs

Other Targets
• Apart from CPU samples, stack traces can be collected for
any event; eg:
• disk, network, or FS I/O
• CPU events, including cache misses
• lock contention and holds
• memory allocation
• Other values, instead of sample counts, can also be used:
• latency
• bytes
• The next sections demonstrate memory allocation, I/O tracing,
and then all blocking types via off-CPU tracing

Memory

Memory
• Analyze memory growth or leaks by tracing one of the
following memory events:
• 1. Allocator functions: malloc(), free()
• 2. brk() syscall
• 3. mmap() syscall
• 4. Page faults
• Instead of stacks and
sample counts,
measure stacks
with byte counts
• Merging shows show total bytes by code path

Memory: Four Targets

Memory: Allocator
• Trace malloc(), free(), realloc(), calloc(), ...
• These operate on virtual memory
• *alloc() stacks show why memory was first allocated (as
opposed to populated): Memory Allocation Flame Graphs
• With free()/realloc()/..., suspected memory leaks during tracing
can be identified: Memory Leak Flame Graphs!
• Down side: allocator functions are frequent, so tracing can
slow the target somewhat (eg, 25%)
• For comparison: Valgrind memcheck is more thorough, but its
CPU simulation can slow the target 20 - 30x

Memory: Allocator: malloc()
• As a simple example, just tracing malloc() calls with user-level
stacks and bytes requested, using DTrace:
# dtrace -x ustackframes=100 -n 'pid$target::malloc:entry {
@[ustack()] = sum(arg0); } tick-60s { exit(0); }' -p 529 -o out.malloc
• malloc() Bytes Flame Graph:
# stackcollapse.pl out.malloc | flamegraph.pl --title="malloc() bytes" \
--countname="bytes" --colors=mem >gt; out.malloc.svg
• The options customize the title, countname, and color palette

Memory: Allocator: malloc()

Memory: Allocator: Leaks
• Yichun Zhang developed Memory Leak Flame Graphs using
SystemTap to trace allocator functions, and applied them to
leaks in Nginx (web server):

Memory: brk()
• Many apps grow their virtual memory size using brk(), which
sets the heap pointer
• A stack trace on brk() shows what triggered growth
• Eg, this script (brkbytes.d) traces brk() growth for “mysqld”:
#!/usr/sbin/dtrace -s
inline string target = "mysqld";
uint brk[int];
syscall::brk:entry /execname == target/ { self->gt;p = arg0; }
syscall::brk:return /arg0 == 0 && self->gt;p && brk[pid]/ {
@[ustack()] = sum(self->gt;p - brk[pid]);
syscall::brk:return /arg0 == 0 && self->gt;p/ { brk[pid] = self->gt;p; }
syscall::brk:return /self->gt;p/ { self->gt;p = 0; }

Memory: brk(): Heap Expansion
# ./brkbytes.d -n 'tick-60s { exit(0); }' >gt; out.brk
# stackcollapse.pl out.brk | flamegraph.pl --countname="bytes" \
--title="Heap Expansion Flame Graph" --colors=mem >gt; out.brk.svg

Memory: brk()
• brk() tracing has low overhead: these calls are typically
infrequent
• Reasons for brk():
• A memory growth code path
• A memory leak code path
• An innocent application code path, that happened to spillover the current heap size
• Asynchronous allocator code path, that grew the
application in response to diminishing free space

Memory: mmap()
• mmap() may be used by the application or it’s user-level
allocator to map in large regions of virtual memory
• It may be followed by munmap() to free the area, which can
also be traced
• Eg, mmap() tracing, similar to brk tracing, to show bytes and
the stacks responsible:
# dtrace -n 'syscall::mmap:entry /execname == "mysqld"/ {
@[ustack()] = sum(arg1); }' -o out.mmap
# stackcollapse.pl out.mmap | flamegraph.pl --countname="bytes" \
--title="mmap() bytes Flame Graph" --colors=mem >gt; out.mmap.svg
• This should be low overhead – depends on the frequency

Memory: Page Faults
• brk() and mmap() expand virtual memory
• Page faults expand physical memory (RSS). This is demandbased allocation, deferring mapping to the actual write
• Tracing page faults show the stack responsible for consuming
(writing to) memory:
# dtrace -x ustackframes=100 -n 'vminfo:::as_fault /execname == "mysqld"/ {
@[ustack()] = count(); } tick-60s { exit(0); }' >gt; out.fault
# stackcollapse.pl out.mysqld_fault01 | flamegraph.pl --countname=pages \
--title="Page Fault Flame Graph" --colors=mem >gt; mysqld_fault.svg

Memory: Page Faults

I/O

I/O
• Show time spent in I/O, eg, storage I/O
• Measure I/O completion events with stacks and their latency;
merging to show total time waiting by code path
Application
system calls
VFS
Logical I/O:
Measure here for user stacks,
and real application latency
Block Device Interface
Disks
Physical I/O:
Measure here for kernel stacks,
and disk I/O latency

I/O: Logical I/O Laency
• For example, ZFS call latency using DTrace (zfsustack.d):
#!/usr/sbin/dtrace -s
#pragma D option quiet
#pragma D option ustackframes=100
fbt::zfs_read:entry, fbt::zfs_write:entry,
fbt::zfs_readdir:entry, fbt::zfs_getattr:entry,
fbt::zfs_setattr:entry
self->gt;start = timestamp;
Timestamp from
function start (entry)
fbt::zfs_read:return, fbt::zfs_write:return,
fbt::zfs_readdir:return, fbt::zfs_getattr:return,
fbt::zfs_setattr:return
/self->gt;start/
this->gt;time = timestamp - self->gt;start;
@[ustack(), execname] = sum(this->gt;time);
self->gt;start = 0;
... to function end (return)
dtrace:::END
printa("%k%s\n%@d\n", @);

I/O: Logical I/O Laency
• Making an I/O Time Flame Graph:
# ./zfsustacks.d -n 'tick-10s { exit(0); }' -o out.iostacks
# stackcollapse.pl out.iostacks | awk '{ print $1, $2 / 1000000 }' | \
flamegraph.pl --title="FS I/O Time Flame Graph" --color=io \
--countname=ms --width=500 >gt; out.iostacks.svg
• DTrace script measures all processes, for 10 seconds
• awk to covert ns to ms

I/O: Time Flame Graph: gzip
• gzip(1) waits more time in write()s than read()s

I/O: Time Flame Graph: MySQL

I/O: Flame Graphs
• I/O latency tracing: hugely useful
• But once you pick an I/O type, there usually isn't that many
different code paths calling it
• Flame Graphs are nice, but often not necessary

Off-CPU

Off-CPU
Off-CPU tracing:
off-CPU
on-CPU
syscall
On-CPU
Off-CPU
block . . . . . . . . . interrupt
user-level
kernel

Off-CPU: Performance Analysis
• Generic approach for all blocking events, including I/O
• An advanced performance analysis methodology:
• http://dtrace.org/blogs/brendan/2011/07/08/off-cpu-performance-analysis/
• Counterpart to (on-)CPU profiling
• Measure time a thread spent off-CPU, along with stacks
• Off-CPU reasons:
• Waiting (sleeping) on I/O, locks, timers
• Runnable waiting for CPU
• Runnable waiting for page/swap-ins
• The stack trace will explain which

Off-CPU: Time Flame Graphs
• Off-CPU profiling data (durations and stacks) can be rendered
as Off-CPU Time Flame Graphs
• As this involves many more code paths, Flame Graphs are
usually really useful
• Yichun Zhang created these, and has been using them on
Linux with SystemTap to collect the profile data. See:
• http://agentzh.org/misc/slides/off-cpu-flame-graphs.pdf
• Which describes their uses for Nginx performance analysis

Off-CPU: Profiling
• Example of off-CPU profiling for the bash shell:
# dtrace -x ustackframes=100 -n '
sched:::off-cpu /execname == "bash"/ { self->gt;ts = timestamp; }
sched:::on-cpu /self->gt;ts/ {
@[ustack()] = sum(timestamp - self->gt;ts); self->gt;ts = 0; }
tick-30s { exit(0); }' -o out.offcpu
• Traces time from when a thread switches off-CPU to when it
returns on-CPU, with user-level stacks. ie, time blocked or
sleeping
• Off-CPU Time Flame Graph:
# stackcollapse.pl gt; out.offcpu.svg
• This uses awk to convert nanoseconds into milliseconds

Off-CPU: Bash Shell

Off-CPU: Bash Shell
waiting for
child processes
waiting for
keystrokes

Off-CPU: Bash Shell
• For that simple example, the trace
data was so short it could have
just been read (54 lines, 4 unique
stacks):
• For multithreaded applications,
idle thread time can dominate
• For example, an idle MySQL
server...
libc.so.1`__forkx+0xb
libc.so.1`fork+0x1d
bash`make_child+0xb5
bash`execute_simple_command+0xb02
bash`execute_command_internal+0xae6
bash`execute_command+0x45
bash`reader_loop+0x240
bash`main+0xaff
bash`_start+0x83
libc.so.1`syscall+0x13
bash`file_status+0x19
bash`find_in_path_element+0x3e
bash`find_user_command_in_path+0x114
bash`find_user_command_internal+0x6f
bash`search_for_command+0x109
bash`execute_simple_command+0xa97
bash`execute_command_internal+0xae6
bash`execute_command+0x45
bash`reader_loop+0x240
bash`main+0xaff
bash`_start+0x83
libc.so.1`__waitid+0x15
libc.so.1`waitpid+0x65
bash`waitchld+0x87
bash`wait_for+0x2ce
bash`execute_command_internal+0x1758
bash`execute_command+0x45
bash`reader_loop+0x240
bash`main+0xaff
bash`_start+0x83
libc.so.1`__read+0x15
bash`rl_getc+0x2b
bash`rl_read_key+0x22d
bash`readline_internal_char+0x113
bash`readline+0x49
bash`yy_readline_get+0x52
bash`shell_getc+0xe1
bash`read_token+0x6f
bash`yyparse+0x4b9
bash`parse_command+0x67
bash`read_command+0x52
bash`reader_loop+0xa5
bash`main+0xaff
bash`_start+0x83

Off-CPU: MySQL Idle

Off-CPU: MySQL Idle
Columns from
_thrp_setup
are threads or
thread groups
MySQL gives thread routines
descriptive names (thanks!)
Mouse over each to identify
(profiling time was 30s)

Off-CPU: MySQL Idle
buf_flush_page_cleaner_thread
mysqld_main
dict_stats_thread
srv_monitor_thread
fts_optimize_thread
srv_master_thread
io_handler_thread
srv_error_monitor_thread
lock_wait_timeout_thread pfs_spawn_thread
mysqld Threads

Off-CPU: MySQL Idle
• Some thread columns are wider than the measurement time:
evidence of multiple threads
• This can be shown a number of ways. Eg, adding process
name, PID, and TID to the top of each user stack:
#!/usr/sbin/dtrace -s
#pragma D option ustackframes=100
sched:::off-cpu /execname == "mysqld"/ { self->gt;ts = timestamp; }
sched:::on-cpu
/self->gt;ts/
@[execname, pid, curlwpsinfo->gt;pr_lwpid, ustack()] =
sum(timestamp - self->gt;ts);
self->gt;ts = 0;
dtrace:::END { printa("\n%s-%d/%d%k%@d\n", @); }

Off-CPU: MySQL Idle
1 thread
many threads
2 threads
4 threads doing work
(less idle)
thread ID (TID)

Off-CPU: Challenges
• Including multiple threads in one Flame Graph might still be
confusing. Separate Flame Graphs for each can be created
• Off-CPU stacks often don't explain themselves:
• This is blocked on a conditional variable. The real reason it is
blocked and taking time isn't visible here
• Now lets look at a busy MySQL server, which presents
another challenge...

Off-CPU: MySQL Busy
net_read_packet() ->gt; pollsys()
idle threads

Off-CPU: MySQL Busy
random narrow
stacks during
work, with no
reason to
sleep?

Off-CPU: MySQL Busy
• Those were user-level stacks only. The kernel-level stack,
which can be included, will usually explain what happened
• eg, involuntary context switch due to time slice expired
• Those paths are likely hot in the CPU Sample Flame Graph

Hot/Cold

Hot/Cold: Profiling
On-CPU
Profiling
Off-CPU
Profiling
(everything else)
Thread State Transition Diagram

Hot/Cold: Profiling
• Profiling both on-CPU and off-CPU stacks shows everything
• In my LISA'12 talk I called this the Stack Profile Method:
profile all stacks
• Both on-CPU ("hot") and off-CPU ("cold") stacks can be
included in the same Flame Graph, colored differently:
Hot Cold Flame Graphs!
• Merging multiple threads gets even weirder. Creating a
separate graph per-thread makes much more sense, as
comparisons to see how a thread's time is divided between
on- and off-CPU activity
• For example, a single web server thread with kernel stacks...

Hot/Cold: Flame Graphs

Hot/Cold: Flame Graphs
On-CPU (!?)
Off-CPU

Hot/Cold: Challenges
• Sadly, this often doesn't work well for two reasons:
• 1. On-CPU time columns get compressed by off-CPU time
• Previous example dominated by the idle path – waiting for
a new connection – which is not very interesting!
• Works better with zoomable Flame Graphs, but then we've
lost the ability to see key details on first glance
• Pairs of on-CPU and off-CPU Flame Graphs may be the
best approach, giving both the full width
• 2. Has the same challenge from off-CPU Flame Graphs:
real reason for blocking may not be visible

State of the Art
• That was the end of Flame Graphs, but I can't stop here –
we're so close
• On + Off-CPU Flame Graphs can attack any issue
• 1. The compressed problem is solvable via one or more of:
• zoomable Flame Graphs
• separate on- and off-CPU Flame Graphs
• per-thread Flame Graphs
• 2. How do we show the real reason for blocking?

Wakeup Tracing
Wakeup tracing:
sleep
wakeup
user-level
kernel
On-CPU
Off-CPU
block . . . . . . . . . . . . . wakeup

Tracing Wakeups
• The systems knows who woke up who
• Tracing who performed the wakeup – and their stack – can
show the real reason for waiting
• Wakeup Latency Flame Graph
• Advanced activity
• Consider overheads – might trace too much
• Eg, consider ssh, starting with the Off CPU Time Flame Graph

Off-CPU Time Flame Graph: ssh
Waiting on a conditional variable
But why did we wait this long?
Object sleeping on

Wakeup Latency Flame Graph: ssh

Wakeup Latency Flame Graph: ssh
These code paths,
... woke up
these objects

Tracing Wakeup, Example (DTrace)
#!/usr/sbin/dtrace -s
#pragma D option quiet
#pragma D option ustackframes=100
#pragma D option stackframes=100
int related[uint64_t];
This example targets sshd
(previous example also matched
vmstat, after discovering that
sshd was blocked on vmstat,
which it was: "vmstat 1")
sched:::sleep
/execname == "sshd"/
ts[curlwpsinfo->gt;pr_addr] = timestamp;
Time from sleep to wakeup
sched:::wakeup
/ts[args[0]->gt;pr_addr]/
this->gt;d = timestamp - ts[args[0]->gt;pr_addr];
@[args[1]->gt;pr_fname, args[1]->gt;pr_pid, args[0]->gt;pr_lwpid, args[0]->gt;pr_wchan,
stack(), ustack(), execname, pid, curlwpsinfo->gt;pr_lwpid] = sum(this->gt;d);
ts[args[0]->gt;pr_addr] = 0;
Stack traces of who is doing the waking
dtrace:::END
printa("\n%s-%d/%d-%x%k-%k%s-%d/%d\n%@d\n", @);
Aggregate if possible instead of dumping, to minimize overheads

Following Stack Chains
• 1st level of wakeups often not enough
• Would like to programmatically follow multiple chains of
wakeup stacks, and visualize them
• I've discussed this with others before – it's a hard problem
• The following is in development!: Chain Graph

Chain Graph

Chain Graph
Wakeup Thread 2
I wokeup
Wakeup Thread 1
I wokeup
Wakeup Stacks
why I waited
Off CPU Stacks:
why I blocked

Chain Graph Visualization
• New, experimental; check for later improvements
• Stacks associated based on sleeping object address
• Retains the value of relative widths equals latency
• Wakeup stacks frames can be listed in reverse (may be less
confusing when following towers bottom-up)
• Towers can get very tall, tracing wakeups through different
software threads, back to metal

Following Wakeup Chains, Example (DTrace)
#!/usr/sbin/dtrace -s
#pragma D option quiet
#pragma D option ustackframes=100
#pragma D option stackframes=100
int related[uint64_t];
sched:::sleep
/execname == "sshd" || related[curlwpsinfo->gt;pr_addr]/
ts[curlwpsinfo->gt;pr_addr] = timestamp;
sched:::wakeup
/ts[args[0]->gt;pr_addr]/
this->gt;d = timestamp - ts[args[0]->gt;pr_addr];
@[args[1]->gt;pr_fname, args[1]->gt;pr_pid, args[0]->gt;pr_lwpid, args[0]->gt;pr_wchan,
stack(), ustack(), execname, pid, curlwpsinfo->gt;pr_lwpid] = sum(this->gt;d);
ts[args[0]->gt;pr_addr] = 0;
related[curlwpsinfo->gt;pr_addr] = 1;
dtrace:::END
printa("\n%s-%d/%d-%x%k-%k%s-%d/%d\n%@d\n", @);
Also follow who
wakes up the waker

Developments

Developments
• There have been many other great developments in the world
of Flame Graphs. The following is a short tour.

node.js Flame Graphs
• Dave Pacheco developed the DTrace ustack helper for v8,
and created Flame Graphs with node.js functions
http://dtrace.org/blogs/dap/2012/01/05/where-does-your-node-program-spend-its-time/

OS X Instruments Flame Graphs
• Mark Probst developed a
way to produce Flame
Graphs from Instruments
1. Use the Time Profile instrument
2. Instrument ->gt; Export Track
3. stackcollapse-instruments.pl
4. flamegraphs.pl
http://schani.wordpress.com/2012/11/16/flame-graphs-for-instruments/

Ruby Flame Graphs
• Sam Saffron developed Flame Graphs with the Ruby
MiniProfiler
• These stacks are very
deep (many frames),
so the function names
have been dropped
and only the rectangles
are drawn
• This preserves the
value of seeing the
big picture at first
glance!
http://samsaffron.com/archive/2013/03/19/flame-graphs-in-ruby-miniprofiler

Windows Xperf Flame Graphs
• Bruce Dawson developed Flame Graphs from Xperf data, and
an xperf_to_collapsedstacks.py script
Visual Studio CPU Usage
http://randomascii.wordpress.com/2013/03/26/summarizing-xperf-cpu-usage-with-flame-graphs/

WebKit Web Inspector Flame Charts
• Available in Google Chrome developer tools, these show
JavaScript CPU stacks as colored rectangles
• Inspired by Flame Graphs but
not the same: they show the
passage of time on the x-axis!
• This generally works here as:
• the target is single threaded
apps often with repetitive
code paths
• ability to zoom
• Can a "Flame Graph" mode be
provided for the same data?
https://bugs.webkit.org/show_bug.cgi?id=111162

Perl Devel::NYTProf Flame Graphs
• Tim Bunce has been adding Flame Graph features, and
included them in the Perl profiler: Devel::NYTProf
http://blog.timbunce.org/2013/04/08/nytprof-v5-flaming-precision/

Leak and Off-CPU Time Flame Graphs
• Yichun Zhang (agentzh) has created Memory Leak and OffCPU Time Flame Graphs, and has given good talks to explain
how Flame Graphs work
http://agentzh.org/#Presentations
http://agentzh.org/misc/slides/yapc-na-2013-flame-graphs.pdf
http://www.youtube.com/watch?v=rxn7HoNrv9A
http://agentzh.org/misc/slides/off-cpu-flame-graphs.pdf
http://agentzh.org/misc/flamegraph/nginx-leaks-2013-10-08.svg
https://github.com/agentzh/nginx-systemtap-toolkit
... these
also provide
examples of using
SystemTap on Linux

Color Schemes
• Colors can be used to convey data, instead of the default
random color scheme. This example from Dave Pacheco
colors each function by its degree of direct on-CPU execution
• A Flame Graph
tool could let you
select different
color schemes
• Another can be:
color by a hash on
the function name,
so colors are
consistent
https://npmjs.org/package/stackvis

Zoomable Flame Graphs
• Dave Pacheco has also used d3 to provide click to zoom!
Zoom
https://npmjs.org/package/stackvis

Flame Graph Differentials
• Robert Mustacchi has been experimenting with showing the
difference between two Flame Graphs, as a Flame Graph.
Great potential for non-regression testing, and comparisons!

Flame Graphs as a Service
• Pedro Teixeira has a project for node.js Flame Graphs as a
service: automatically generated for each github push!
http://www.youtube.com/watch?v=sMohaWP5YqA

References & Acknowledgements
• Neelakanth Nadgir (realneel): developed SVGs using Ruby
and JavaScript of time-series function trace data with stack
levels, inspired by Roch's work
• Roch Bourbonnais: developed Call Stack Analyzer, which
produced similar time-series visualizations
• Edward Tufte: inspired
me to explore
visualizations that show
all the data at once, as
Flame Graphs do
• Thanks to all who have
developed Flame
Graphs further!
realneel's function_call_graph.rb visualization

Thank you!
• Questions?
• Homepage: http://www.brendangregg.com (links to everything)
• Resources and further reading:
• http://dtrace.org/blogs/brendan/2011/12/16/flame-graphs/: see "Updates"
• http://dtrace.org/blogs/brendan/2012/03/17/linux-kernel-performance-flamegraphs/
• http://dtrace.org/blogs/brendan/2013/08/16/memory-leak-growth-flame-graphs/
• http://dtrace.org/blogs/brendan/2011/07/08/off-cpu-performance-analysis/
• http://dtrace.org/blogs/dap/2012/01/05/where-does-your-node-program-spendits-time/