Skip to main content

Loop Kernel Analysis and Performance Modeling Toolkit

Project description


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. In order to get the Intel Achitecture Code Analyzer (IACA), required by the ECM, ECMCPU and RooflineASM performance models, read this and run:

iaca_get --I-accept-the-Intel-What-If-Pre-Release-License-Agreement-and-please-take-my-soul

Additional requirements are:
  • likwid (used in Benchmark model and by likwid_bench_auto)


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



  1. 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)
  2. 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.


When using Kerncraft for your work, please consider citing the following publication:

Kerncraft: A Tool for Analytic Performance Modeling of Loop Kernels (preprint)

J. Hammer, J. Eitzinger, G. Hager, and G. Wellein: Kerncraft: A Tool for Analytic Performance Modeling of Loop Kernels. In: Tools for High Performance Computing 2016, ISBN 978-3-319-56702-0, 1-22 (2017). Proceedings of IPTW 2016, the 10th International Parallel Tools Workshop, October 4-5, 2016, Stuttgart, Germany. Springer, Cham. DOI: 10.1007/978-3-319-56702-0_1, Preprint: arXiv:1702.04653``


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



Project details

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kerncraft-0.8.14.tar.gz (598.6 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page