Demonstration of customizable Collective Knowledge autotuning workflows with a portable package manager to teach students how to compile, run, benchmark and optimize various workloads across diverse platforms in a unified way!
- BB CY 4.0
2016-2018 (c) dividiti
Stable
- Collective Knowledge framework (@GitHub)
- Python 2.7 or 3.3+
- Python PIP
- Git client
The minimal installation requires:
- Python 2.7 or 3.3+ (limitation is mainly due to unitests)
- Git command line client.
You can install CK in your local user space as follows:
$ git clone http://github.com/ctuning/ck
$ export PATH=$PWD/ck/bin:$PATH
$ export PYTHONPATH=$PWD/ck:$PYTHONPATH
You can also install CK via PIP with sudo to avoid setting up environment variables yourself:
$ sudo pip install ck
Install this CK repository:
$ ck pull repo --url=https://github.com/dividiti/ck-rpi-optimization
Update all CK repositories at any time
$ ck pull all
List available programs and data sets
$ ck ls ck-rpi-optimization:program:*
$ ck ls ck-rpi-optimization:dataset:*
Compile a given program on your platform via unified CK autotuning workflow with portable package manager:
$ ck compile program:rhash --speed
Run a given program via unified CK autotuning workflow:
$ ck run program:rhash
Please check out a related report with shared aritfacts in another CK repository: