CK repository to collaboratively optimize benchmarks, kernels and realistic workloads across Raspberry Pi devices provided by volunteers (starting from compiler flag autotuning). Interactive article with all results:
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README.md

README.md

compatibility License: CC BY 4.0

Introduction

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!

License

  • BB CY 4.0

Copyright

2016-2018 (c) dividiti

Status

Stable

Prerequisites

  • Collective Knowledge framework (@GitHub)
  • Python 2.7 or 3.3+
  • Python PIP
  • Git client

Minimal CK installation

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

CK workflow installation

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

More details

Please check out a related report with shared aritfacts in another CK repository: