How to get started with Codee: Canny example

Last updated on September 15, 2022

In this blog post, we show how to get started with Codee using the Canny edge detection image processing algorithm. You will see how it supports the performance optimization roadmap by providing human-readable actionable items to enable the optimization of sequential code as well as the exploitation of the parallelism available in the target hardware platform.

A structured report displays the actionable items for the optimization of the sequential code, including faster scalar processing, simpler control flow and more efficient usage of the memory of the computer. The report also displays items for the optimization of parallel processing through vectorization, multithreading and offloading. You will see how to dig deeper into the details of each human-readable actionable item in order to understand where the performance issue is, what it is about and how to fix it.

Code structure

You can find the Canny code in our performance-demos public repository. It consists of a single canny.c file and a Makefile to build and run the code for a test image. Please note that the image is located in the parent folder in the repository and you need to ensure that it is located there for the make run target to work.

The algorithm itself essentially highlights the edges of a given image, which is illustrated in the following image:

How to build and run

As described before, there is a Makefile that you can use to build and run. By simply invoking make all the targets will be invoked resulting in cleaning any previous builds, building the binary and invoking it for the test image:

$ make
rm -fr canny testvecs
cc canny.c -fopenmp -O3 -lm -o canny
unzip ../15360_8640.zip
Archive:  ../15360_8640.zip
   creating: testvecs/
   creating: testvecs/input/
  inflating: testvecs/input/15360_8640.pgm  
./canny testvecs/input/15360_8640.pgm 0.5 0.7 0.9
Total time: 11.688

Using Codee

1. Ensure you have the latest version

Please run the following to ensure that you have the latest version of Codee matching the steps and outputs described here:

$ pwreport --version
Codee: 1.5.1 (rev 31fd1db053e6)

C/C++/Fortran source code syntactic parsing based on:
- Clang 13.0
- Flang 'fir-dev' branch (rev 0b95852b0d00)
- LLVM 13.0

Supported scientific libraries:
- libmath C11
- CBLAS (levels 1, 2 and 3) 3.10.0
- OpenMP 4.5
- OpenACC 2.7

Vectorization diagnosis of compilers:
- gcc 4.8.1 - 11.2
- clang 3.5 - 12.0.1
- icc 19.0 - 2021.1

Note that the output also shows the supported versions of third-party software tools, like compilers and parallel programming application program interfaces.

2. Get the performance optimization report of the whole code

You should always start by invoking the pwreport tool:

$ pwreport --screening canny.c
SCREENING REPORT

Target  Lines of code Analyzed lines Analysis time # actions Effort Cost    Profiling
------- ------------- -------------- ------------- --------- ------ ------- ---------
canny.c 656           252            154 ms        91        264 h  8639€   n/a
------- ------------- -------------- ------------- --------- ------ ------- ---------
Total   656           252            154 ms        91        264 h  8639€   n/a

ACTIONS PER STAGE OF THE PERFORMANCE OPTIMIZATION ROADMAP
Target  Scalar Control Memory Vector Multi Offload
------- ------ ------- ------ ------ ----- -------
canny.c 22     45      8      15     n/a   n/a
------- ------ ------- ------ ------ ----- -------
Total   22     45      8      15     n/a   n/a

TOTAL NUMBER OF LOOPS FOR CODEE AUTO AND GUIDED MODES
                      --------------- # actions ----------------
Codee support # Loops Scalar Control Memory Vector Multi Offload
------------- ------- ------ ------- ------ ------ ----- -------
Auto          1       0      0       0      2      n/a   n/a
Guided        28      20     39      8      6      n/a   n/a
Develop       5       1      5       0      7      n/a   n/a
Roadmap       7       0      0       0      0      n/a   n/a

Target : analyzed directory or source code file
Lines of code : total lines of code found in the target (computed the same way as the sloccount tool)
Analyzed lines : relevant lines of code successfully analyzed
Analysis time : time required to analyze the target
# actions : total actionable items (opportunities, recommendations, defects and remarks) detected
Effort : estimated number of hours it would take to carry out all actions (scalar, control, memory, vector, multi and offload with 1, 2, 4, 8, 12 and 16 hours respectively)
Cost : estimated cost in euros to carry out all the actions, paying the average salary of 56,286€/year for a professional C/C++ developer working 1720 hours per year
Profiling : estimation of overall execution time required by this target

Codee support : Codee support level given for the loop:
  - Auto: the loop can be automatically optimized by Codee auto mode (pwdirectives --auto)
  - Guided: Codee assists  the user in order to optimize the loop
  - Develop: Codee lacks support for the loop, but the feature is under development
  - Roadmap: Codee lacks support for the loop, and the feature is on the long term roadmap

SUGGESTIONS
  Use --level 1|2 to get more details, e.g:
        pwreport --level 2 --screening canny.c

  You can specify multiple inputs which will be displayed as multiple rows (ie. targets) in the table, eg:
        pwreport --screening some/other/dir canny.c

  Use --actions to find out details about the detected actions:
        pwreport --actions canny.c

  You can automatically vectorize every vectorizable loop of one function with:
        pwdirectives --auto --simd omp --in-place canny.c

  Multithreading and offloading actions are filtered by default. Use --include-tags to enable them:
        pwreport --screening --include-tags all canny.c

  You can focus on a specific optimization type by filtering by its tag (scalar, control, memory, vector, multi, offload), eg.:
        pwreport --actions --include-tags scalar canny.c

1 file successfully analyzed and 0 failures in 154 ms

You can see that the first table provides high level metrics of the analysis of your code. It reports how many lines of code it has and how many of them were successfully analyzed, how long it took to do so, how many human-readable actionable items were detected and an estimation of the cost both in terms of hours and money to address all of them. Finally, this table also indicates “n/a”, meaning that there is no profiling information available to Code.

The second table provides a breakdown of the human-readable actions into the six stages of the performance optimization roadmap. The first three stages correspond to sequential optimizations that take advantage of faster scalar processing (Sequential Scalar), simpler control flow (Sequential Control Flow) and more efficient memory usage (Sequential Memory). The latter three stages correspond to performance optimization that exploit a type of parallelism, from vectorization to multithreading and offloading.

The suggestions provided at the bottom are key for the usage of Codee since they will provide you with hints on what your next steps could be.

Note the “n/a” in the Multithreading and Offloading columns of the second table. This is because Codee disables those actions by default in order to improve the out-of-the-box experience. We can enable this functionality explicitly, as suggested in the third suggestion, by using the flag --include-tags all.

$ pwreport --screening --include-tags all canny.c
SCREENING REPORT

Target  Lines of code Analyzed lines Analysis time # actions Effort Cost     Profiling
------- ------------- -------------- ------------- --------- ------ -------- ---------
canny.c 656           252            154 ms        97        348 h  11388€   n/a
------- ------------- -------------- ------------- --------- ------ -------- ---------
Total   656           252            154 ms        97        348 h  11388€   n/a

ACTIONS PER STAGE OF THE PERFORMANCE OPTIMIZATION ROADMAP
Target  Scalar Control Memory Vector Multi Offload
------- ------ ------- ------ ------ ----- -------
canny.c 22     45      8      15     3     3
------- ------ ------- ------ ------ ----- -------
Total   22     45      8      15     3     3

TOTAL NUMBER OF LOOPS FOR CODEE AUTO AND GUIDED MODES
                      --------------- # actions ----------------
Codee support # Loops Scalar Control Memory Vector Multi Offload
------------- ------- ------ ------- ------ ------ ----- -------
Auto          1       0      0       0      2      0     0
Guided        28      20     39      8      6      3     3
Develop       5       1      5       0      7      0     0
Roadmap       7       0      0       0      0      0     0

Target : analyzed directory or source code file
Lines of code : total lines of code found in the target (computed the same way as the sloccount tool)
Analyzed lines : relevant lines of code successfully analyzed
Analysis time : time required to analyze the target
# actions : total actionable items (opportunities, recommendations, defects and remarks) detected
Effort : estimated number of hours it would take to carry out all actions (scalar, control, memory, vector, multi and offload with 1, 2, 4, 8, 12 and 16 hours respectively)
Cost : estimated cost in euros to carry out all the actions, paying the average salary of 56,286€/year for a professional C/C++ developer working 1720 hours per year
Profiling : estimation of overall execution time required by this target

Codee support : Codee support level given for the loop:
  - Auto: the loop can be automatically optimized by Codee auto mode (pwdirectives --auto)
  - Guided: Codee assists  the user in order to optimize the loop
  - Develop: Codee lacks support for the loop, but the feature is under development
  - Roadmap: Codee lacks support for the loop, and the feature is on the long term roadmap

SUGGESTIONS
  Use --level 1|2 to get more details, e.g:
        pwreport --level 2 --screening --include-tags all canny.c

  You can specify multiple inputs which will be displayed as multiple rows (ie. targets) in the table, eg:
        pwreport --screening some/other/dir --include-tags all canny.c

  Use --actions to find out details about the detected actions:
        pwreport --actions --include-tags all canny.c

  You can automatically vectorize every vectorizable loop of one function with:
        pwdirectives --auto --simd omp --in-place canny.c

  You can focus on a specific optimization type by filtering by its tag (scalar, control, memory, vector, multi, offload), eg.:
        pwreport --actions --include-tags scalar canny.c

1 file successfully analyzed and 0 failures in 154 ms

Now we can finally see all of the actions reported, including multithreading and offloading related actions.

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The natural next step is to follow the second suggestion and use the --actions analysis. Note that Codee requires the user to perform --actions analysis over a specific target function or loop, and not over the entire source code. This is because otherwise the output would be too large and hard to digest by the user. It is always a good idea to start using the --actions analysis over hotspot functions, as we will see in the next step.

3. Get the performance optimization report of the hotspot

From profiling, we know that the hotspot for this code corresponds to the gaussian_smooth function. You can narrow the analysis to that function by appending :gaussian_smooth to the filename. The pwreport invocation is as follows:

$ pwreport --actions --include-tags all canny.c:gaussian_smooth
ACTIONS REPORT

  FUNCTION BEGIN at canny.c:gaussian_smooth:439:1
    439: void gaussian_smooth(unsigned char *image, int rows, int cols, float sigma,

    LOOP BEGIN at canny.c:gaussian_smooth:474:4 (support: guided)
      474:    for(r=0;r<rows;r++){

      LOOP BEGIN at canny.c:gaussian_smooth:475:7 (support: guided)
        475:       for(c=0;c<cols;c++){

        LOOP BEGIN at canny.c:gaussian_smooth:478:10 (support: guided)
          478:          for(cc=(-center);cc<=center;cc++){

          [RMK012] canny.c:478:10 the vectorization cost model states the loop is not a SIMD opportunity because conditional execution renders vectorization inefficient
        LOOP END
        [PWR002] canny.c:442:18 'cc' not declared in the innermost scope possible
        [PWR002] canny.c:447:10 'dot' not declared in the innermost scope possible
        [PWR002] canny.c:448:10 'sum' not declared in the innermost scope possible
      LOOP END
      [PWR002] canny.c:442:11 'c' not declared in the innermost scope possible
      [PWR002] canny.c:442:18 'cc' not declared in the innermost scope possible
      [PWR002] canny.c:447:10 'dot' not declared in the innermost scope possible
      [PWR002] canny.c:448:10 'sum' not declared in the innermost scope possible

      [OPP001] canny.c:474:4 is a multi-threading opportunity
      [OPP003] canny.c:474:4 is an offload opportunity
    LOOP END

    LOOP BEGIN at canny.c:gaussian_smooth:492:4 (support: guided)
      492:    for(c=0;c<cols;c++){

      LOOP BEGIN at canny.c:gaussian_smooth:493:7 (support: guided)
        493:       for(r=0;r<rows;r++){

        LOOP BEGIN at canny.c:gaussian_smooth:496:10 (support: guided)
          496:          for(rr=(-center);rr<=center;rr++){

          [PWR034] canny.c:496:10 avoid strided array access for variable 'tempim' to improve performance
          [RMK010] canny.c:496:10 the vectorization cost model states the loop is not a SIMD opportunity due to strided memory accesses in the loop body
        LOOP END
        [PWR002] canny.c:442:14 'rr' not declared in the innermost scope possible
        [PWR002] canny.c:447:10 'dot' not declared in the innermost scope possible
        [PWR002] canny.c:448:10 'sum' not declared in the innermost scope possible
        [PWR034] canny.c:493:7 avoid strided array access for variable 'smoothedim' to improve performance
      LOOP END
      [PWR002] canny.c:442:8 'r' not declared in the innermost scope possible
      [PWR002] canny.c:442:14 'rr' not declared in the innermost scope possible
      [PWR002] canny.c:447:10 'dot' not declared in the innermost scope possible
      [PWR002] canny.c:448:10 'sum' not declared in the innermost scope possible

      [OPP001] canny.c:492:4 is a multi-threading opportunity
      [OPP003] canny.c:492:4 is an offload opportunity
    LOOP END
  FUNCTION END

METRICS SUMMARY
  Total recommendations:        16
  Total opportunities:           4
  Total defects:                 0
  Total remarks:                 2

SUGGESTIONS

  Use --level 1|2 to get more details, e.g:
        pwreport --level 2 --actions --include-tags all canny.c:gaussian_smooth

  16 recommendations were found in your code, get more information with pwreport:
        pwreport --actions --include-tags pwr canny.c:gaussian_smooth

  4 opportunities for parallelization were found in your code, get more information with pwloops:
        pwloops --include-tags all canny.c:gaussian_smooth

  More details on the defects, recommendations and more in the Knowledge Base:
        Knowledge base


1 file successfully analyzed and 0 failures in 50 ms

The hotspot analysis succeeds and a report is outputted with the following sections:

ACTIONS REPORT: structured report with actionable insights per function and loop.
CODE COVERAGE: summary of how much code could be analyzed.
METRICS SUMMARY: aggregated summary of the actionable insights detected in the analysis.
SUGGESTIONS: general Codee usage hints.

The CODE COVERAGE report shows that all the code was successfully analyzed and the METRICS SUMMARY shows the different actionable insights detected. As hinted in the SUGGESTIONS section at the end, you can add --level to increase the level of the detail of the ACTIONS REPORT.

4. Dig deeper into the actionable insights for your hotspot

By invoking pwreport with the --level 2 flag, we get a very verbose report. In order to narrow the analysis, we will invoke pwreport for the target loop at line 474. The output is as follows:

$ pwreport --actions --level 2 --include-tags all canny.c:474
ACTIONS REPORT
. . .
  	[OPP001] canny.c:492:4 is a multi-threading opportunity
    	Compute patterns:
      	- 'forall' over the variable 'smoothedim'

    	SUGGESTION: use pwloops to get more details or pwdirectives to generate directives:
      	pwloops canny.c:gaussian_smooth:492:4 --include-tags all
      	pwdirectives --omp multi canny.c:gaussian_smooth:492:4 --in-place

    	More information on: https://www.codee.com/knowledge/opp001

. . .

You can see suggestions on how to use other tools of Codee: use pwloops to get detailed information at the loop-level or pwdirectives to rewrite the source code and create a performance-optimized version automatically.

Building performance into the code from day one with Codee
Request a demo ›

Particularly, for the hotspot loop of Canny, Codee reports the OPP001 opportunity, which indicates that you can optimize the performance of the loop by applying multi-threading to it. It also points out that you can pwdirectives to rewrite the source code of the loop automatically. Let’s see how to do it.

5. Optimize the performance of your hotspot

Let’s give the latter a try to add multi-threading. First, let’s build and run canny to see how long it takes for the sequential version to execute. You can use the Makefile to do so:

$ make
rm -fr canny testvecs
cc canny.c -fopenmp -O3 -lm -o canny
unzip ../15360_8640.zip
Archive:  ../15360_8640.zip
   creating: testvecs/
   creating: testvecs/input/
  inflating: testvecs/input/15360_8640.pgm  
./canny testvecs/input/15360_8640.pgm 0.5 0.7 0.9
Total time: 11.688

Now copy the command suggested by pwreport (note that using --in-place will modify the file, you can use -o canny_omp.c instead to create a new file):

$ pwdirectives --omp multi canny.c:gaussian_smooth:474:4 --in-place
Results for file 'canny.c':
  Successfully parallelized loop at 'canny.c:gaussian_smooth:474:4' [using multi-threading]:
      [INFO] canny.c:474:4 Parallel forall: variable 'tempim'
      [INFO] canny.c:474:4 Loop parallelized with multithreading using OpenMP directive 'for'
      [INFO] canny.c:474:4 Parallel region defined by OpenMP directive 'parallel'
      [INFO] canny.c:474:4 Make sure there is no aliasing among variables: kernel, tempim
Successfully updated canny.c

Minimum software stack requirements: OpenMP version 3.0 with multithreading capabilities

Note that the hotspot function gaussian_smooth contains two groups of nested loops, and we have just optimized the first one. In order to optimize the second group as well, just execute once again pwreport –actions to discover that there is another opportunity for multithreading available:

$ pwreport --actions --level 2 --include-tags all canny.c:gaussian_smooth
ACTIONS REPORT
  FUNCTION BEGIN at canny.c:gaussian_smooth:439:1
    439: void gaussian_smooth(unsigned char *image, int rows, int cols, float sigma,
    . . .
    LOOP BEGIN at canny.c:gaussian_smooth:496:4 (difficulty: low)
      . . .       
     [OPP001] canny.c:496:4 is a multi-threading opportunity
        Compute patterns:
          - 'forall' over the variable 'smoothedim'

        SUGGESTION: use pwloops to get more details or pwdirectives to generate directives:
          pwloops canny.c:gaussian_smooth:496:4 --include-tags all
          pwdirectives --omp multi canny.c:gaussian_smooth:496:4 --in-place

        More information on: https://www.codee.com/knowledge/opp001
    . . .
    LOOP END
  . . .
  FUNCTION END

Follow the suggestion again to do the same for that second group of loops:

$ pwdirectives --omp multi canny.c:gaussian_smooth:496:4 --in-place

Results for file 'canny.c':
  Successfully parallelized loop at 'canny.c:gaussian_smooth:496:4' [using multi-threading]:
      [INFO] canny.c:496:4 Parallel forall: variable 'smoothedim'
      [INFO] canny.c:496:4 Loop parallelized with multithreading using OpenMP directive 'for'
      [INFO] canny.c:496:4 Parallel region defined by OpenMP directive 'parallel'
      [INFO] canny.c:496:4 Make sure there is no aliasing among variables: tempim, kernel
Successfully updated canny.c

Minimum software stack requirements: OpenMP version 3.0 with multithreading capabilities

Build and run again to compare the performance:

$ make
rm -fr canny testvecs
cc canny.c -fopenmp -O3 -lm -o canny
unzip ../15360_8640.zip
Archive:  ../15360_8640.zip
   creating: testvecs/
   creating: testvecs/input/
  inflating: testvecs/input/15360_8640.pgm  
./canny testvecs/input/15360_8640.pgm 0.5 0.7 0.9
Total time: 5.942

On a laptop equipped with an AMD Ryzen 7 4800HS CPU (8 cores, 16 threads), the execution went from 11.7 to just 5.9 seconds: almost a 2x speedup!

Building performance into the code from day one with Codee

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ad-id	1	7 months	Provided by amazon-adsystem.com for tracking user actions on other websites to provide targeted content
demdex	0	5 months	This cookie is set under the domain demdex.net and is used by Adobe Audience Manager to help identify a unique visitor across domains.
GPS	0	30 minutes	This cookie is set by Youtube and registers a unique ID for tracking users based on their geographical location
pardot	0		The cookie is set when the visitor is logged in as a Pardot user.
tk_lr	0	1 year	This cookie is set by JetPack plugin on sites using WooCommerce. This is a referral cookie used for analyzing referrer behavior for Jetpack
tk_or	0	5 years	This cookie is set by JetPack plugin on sites using WooCommerce. This is a referral cookie used for analyzing referrer behavior for Jetpack
tk_r3d	0	3 days	The cookie is installed by JetPack. Used for the internal metrics fo user activities to improve user experience

Cookie	Type	Duration	Description
_fbp	0	2 months	This cookie is set by Facebook to deliver advertisement when they are on Facebook or a digital platform powered by Facebook advertising after visiting this website.
_kuid_	0	5 months	The cookie is set by Krux Digital under the domain krxd.net. The cookie stores a unique ID to identify a returning user for the purpose of targeted advertising.
ad-privacy	1	5 years	Provided by amazon-adsystem.com for tracking user actions on other websites to provide targeted content to the users.
ATN	1	2 years	The cookie is set by atdmt.com. The cookies stores data about the user behavior on multiple websites. The data is then used to serve relevant advertisements to the users on the website.
dpm	0	5 months	The cookie is set by demdex.net. This cookie assigns a unique ID to each visiting user that allows third-party advertisers target that users with relevant ads.
everest_g_v2	0	1 year	The cookie is set under eversttech.net domain. The purpose of the cookie is to map clicks to other events on the client's website.
fr	1	2 months	The cookie is set by Facebook to show relevant advertisments to the users and measure and improve the advertisements. The cookie also tracks the behavior of the user across the web on sites that have Facebook pixel or Facebook social plugin.
IDE	1	2 years	Used by Google DoubleClick and stores information about how the user uses the website and any other advertisement before visiting the website. This is used to present users with ads that are relevant to them according to the user profile.
khaos	0	1 year	This cookie is set by rubiconproject.com. The cookie is used to store user data in an anonymous form such as the IP address, geographical location, websites visited, and the ads clicked. The purpose of the cookie is to tailor the ads displayed to the users based on the users movement on other site in the same ad network.
ljt_reader	0	1 year	This is a Lijit Advertising Platform cookie. The cookie is used for recognizing the browser or device when users return to their site or one of their partner's site.
mako_uid	0	1 year	This cookie is set under the domain ps.eyeota.net. The cookies is used to collect data about the users' visit to the website such as the pages visited. The data is used to create a users' profile in terms of their interest and demographic. This data is used for targeted advertising and marketing.
mc	0	1 year	This cookie is associated with Quantserve to track anonymously how a user interact with the website.
NID	1	6 months	This cookie is used to a profile based on user's interest and display personalized ads to the users.
p2	0	1 week	The cookies is set by ownerIQ for the purpose of providing relevant advertisement.
personalization_id	0	2 years	This cookie is set by twitter.com. It is used integrate the sharing features of this social media. It also stores information about how the user uses the website for tracking and targeting.
PUBMDCID	0	2 months	This cookie is set by pubmatic.com. The cookie stores an ID that is used to display ads on the users' browser.
si	0	5 years	The cookies is set by ownerIQ for the purpose of providing relevant advertisement.
TDCPM	0	1 year	The cookie is set by CloudFare service to store a unique ID to identify a returning users device which then is used for targeted advertising.
TDID	0	1 year	The cookie is set by CloudFare service to store a unique ID to identify a returning users device which then is used for targeted advertising.
test_cookie	0	15 minutes	This cookie is set by doubleclick.net. The purpose of the cookie is to determine if the users' browser supports cookies.
tuuid	0	1 year	This cookie is set by .bidswitch.net. The cookies stores a unique ID for the purpose of the determining what adverts the users have seen if you have visited any of the advertisers website. The information is used for determining when and how often users will see a certain banner.
tuuid_lu	0	1 year	This cookie is set by .bidswitch.net. The cookies stores a unique ID for the purpose of the determining what adverts the users have seen if you have visited any of the advertisers website. The information is used for determining when and how often users will see a certain banner.
uid	0	1 month	This cookie is used to measure the number and behavior of the visitors to the website anonymously. The data includes the number of visits, average duration of the visit on the website, pages visited, etc. for the purpose of better understanding user preferences for targeted advertisments.
uuid	0	1 month	To optimize ad relevance by collecting visitor data from multiple websites such as what pages have been loaded.
VISITOR_INFO1_LIVE	1	5 months	This cookie is set by Youtube. Used to track the information of the embedded YouTube videos on a website.

Cookie	Type	Duration	Description
__utma	0	2 years	This cookie is set by Google Analytics and is used to distinguish users and sessions. The cookie is created when the JavaScript library executes and there are no existing __utma cookies. The cookie is updated every time data is sent to Google Analytics.
__utmb	0	30 minutes	The cookie is set by Google Analytics. The cookie is used to determine new sessions/visits. The cookie is created when the JavaScript library executes and there are no existing __utma cookies. The cookie is updated every time data is sent to Google Analytics.
__utmc	0		The cookie is set by Google Analytics and is deleted when the user closes the browser. The cookie is not used by ga.js. The cookie is used to enable interoperability with urchin.js which is an older version of Google analytics and used in conjunction with the __utmb cookie to determine new sessions/visits.
__utmt	0	10 minutes	The cookie is set by Google Analytics and is used to throttle the request rate.
__utmz	0	6 months	This cookie is set by Google analytics and is used to store the traffic source or campaign through which the visitor reached your site.
_gat	0	1 minute	This cookies is installed by Google Universal Analytics to throttle the request rate to limit the colllection of data on high traffic sites.
_gat_UA-5144860-2	0	1 minute	This is a pattern type cookie set by Google Analytics, where the pattern element on the name contains the unique identity number of the account or website it relates to. It appears to be a variation of the _gat cookie which is used to limit the amount of data recorded by Google on high traffic volume websites.
AMP_TOKEN	0	1 hour	This cookie is set by Google Analytics - This cookie contains a token that can be used to retrieve a Client ID from AMP Client ID service. Other possible values indicate opt-out, inflight request or an error retrieving a Client ID from AMP Client ID service.
audit	0	1 year	This cookie is set by Rubicon Project and is used for recording cookie consent data.
bcookie	0	2 years	This cookie is set by linkedIn. The purpose of the cookie is to enable LinkedIn functionalities on the page.
lang	0		This cookie is used to store the language preferences of a user to serve up content in that stored language the next time user visit the website.
lidc	0	1 day	This cookie is set by LinkedIn and used for routing.
mailchimp_landing_site	0	4 weeks	The cookie is set by the email marketing service MailChimp.
na_id	0	1 year	This cookie is set by Addthis.com to enable sharing of links on social media platforms like Facebook and Twitter
ouid	0	1 year	The cookie is set by Addthis which enables the content of the website to be shared across different networking and social sharing websites.
pid	0	1 year	Helps users identify the users and lets the users use twitter related features from the webpage they are visiting.
PugT	0	1 month	This cookie is set by pubmatic.com. The purpose of the cookie is to check when the cookies were last updated on the browser in order to limit the number of calls to the server-side cookie store.
sid	0		This cookie is very common and is used for session state management.
test_cookie	0	11 months	This cookie is set by doubleclick.net. The purpose of the cookie is to determine if the users' browser supports cookies.
YSC	1		This cookies is set by Youtube and is used to track the views of embedded videos.

How to get started with Codee: Canny example

Code structure

How to build and run

Using Codee

1. Ensure you have the latest version

2. Get the performance optimization report of the whole code

3. Get the performance optimization report of the hotspot

4. Dig deeper into the actionable insights for your hotspot

5. Optimize the performance of your hotspot

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Code structure

How to build and run

Using Codee

1. Ensure you have the latest version

2. Get the performance optimization report of the whole code

3. Get the performance optimization report of the hotspot

4. Dig deeper into the actionable insights for your hotspot

5. Optimize the performance of your hotspot

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