How to Install and Uninstall coz-profiler Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 20,2024

1. Install "coz-profiler" package

Please follow the guidelines below to install coz-profiler on Ubuntu 20.10 (Groovy Gorilla)

$ sudo apt update $ sudo apt install coz-profiler

2. Uninstall "coz-profiler" package

Please follow the guidelines below to uninstall coz-profiler on Ubuntu 20.10 (Groovy Gorilla):

$ sudo apt remove coz-profiler $ sudo apt autoclean && sudo apt autoremove

3. Information about the coz-profiler package on Ubuntu 20.10 (Groovy Gorilla)

Package: coz-profiler
Architecture: amd64
Version: 0.2.2-1
Priority: optional
Section: universe/net
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Lluís Vilanova
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 9173
Depends: libc6 (>= 2.30), libdwarf++0 (>= 0.2), libelf++0 (>= 0.2), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), python3, fonts-font-awesome, libjs-jquery, libjs-d3, libjs-d3-tip
Filename: pool/universe/c/coz-profiler/coz-profiler_0.2.2-1_amd64.deb
Size: 657804
MD5sum: 9d4d2e3c3ac958c9ef7ba16506ff209a
SHA1: a50b1bf7c5a80cadbb698646899cdca80af794aa
SHA256: 7e1ad7dd6e7582a9fcfd0d3ab3813fa9145ce39fb4341d0d3dea4af42f240713
SHA512: 3407a1e335b8f7663c2684d2931dc944e65fce8faf4f53c3071dd3eba35792e852fc759f84002da2a2fbe9bb5051cbd36a0a8cbe902dab372e6c4e80c5898647
Homepage: http://coz-profiler.org/
Description-en: Finding Code that Counts with Causal Profiling
Coz is a code profiler that find optimization opportunities
missed by traditional profilers. Coz employs a technique called
causal profiling that measures optimization potential. This measurement
matches developers' assumptions about profilers: that optimizing
highly-ranked code will have the greatest impact on performance. Causal
profiling measures optimization potential for serial, parallel, and
asynchronous programs without instrumentation of special handling for
library calls and concurrency primitives. Instead, a causal profiler
uses performance experiments to predict the effect of
optimizations. This allows the profiler to establish causality:
"optimizing function X will have effect Y," exactly the measurement
developers had assumed they were getting all along.
Description-md5: bd06ad3ef15124be956a5814dc6562a3