Loop Kernel Analysis and Performance Modeling Toolkit

This tool allows automatic analysis of loop kernels using the Execution Cache Memory (ECM) model, the Roofline model and actual benchmarks. kerncraft provides a framework to investigate the data reuse and cache requirements by static code analysis. In combination with the Intel IACA tool kerncraft can give a good overview of both in-core and memory bottlenecks and use that data to apply performance models.

For a detailed documentation see publications in doc/.

On most systems with python pip and setuputils installed, just run: pip install --user kerncraft for the latest release.

If you want to build from source: Clone this repository and run python ./setup.py install.

If you are unfamiliar with python, here is a tutorial on how to install python packages: https://packaging.python.org/installing/ . The use of virtual enviornments is usually a good choice.

Additional requirements are:

• Intel Achitecture Code Analyzer (IACA), with (working) iaca.sh in PATH environment variable (used by ECM, ECMCPU and RooflineIACA models)
• likwid (used in Benchmark model and by likwid_bench_auto.py)

1. Get an example kernel and machine file from the examples directory

wget https://raw.githubusercontent.com/RRZE-HPC/kerncraft/master/examples/machine-files/SandyBridgeEP_E5-2680.yml

wget https://raw.githubusercontent.com/RRZE-HPC/kerncraft/master/examples/kernels/2d-5pt.c

2. Have a look at the machine file and change it to match your targeted machine (above we downloaded a file for a Sandy Bridge EP machine)
3. Run kerncraft

kerncraft -p ECM -m SandyBridgeEP_E5-2680.yml 2d-5pt.c -D N 10000 -D M 10000 add -vv for more information on the kernel and ECM model analysis.

Implementation: Julian Hammer ECM Model (theory): Georg Hager, Holger Stengel, Jan Treibig LC generalization: Julian Hammer

AGPLv3