The 5th International Workshop on Adaptive Self-tuning Computing Systems

(co-located HiPEAC 2015)

January 19-21, 2015, Amsterdam, The Netherlands


Along with the traditional publication model, we will be validating, for the first time, our new open publication model where community pre-reviews articles and experimental results! Please, stay tuned and check out our proposal [ arXivACM DL ] together with collaborative Wiki page!

Workshop organisers / Program chairs

Program committee

Rather than pre-selecting a dedicated committee for conferences, we select reviewers for reseach material (artifacts) and publications from a pool of our supporters based on submitted publications and their keywords as discussed in our new publication model proposal above. If you are interested to help and join our effort, please get in touch! The final list of reviewers will be published here at the end of the reviewing.

Call for papers

Computing systems are rapidly evolving into heterogeneous machines featuring many processor cores. This leads to a tremendous complexity with an unprecedented number of available design and optimization choices for architectures, applications, compilers and run-time systems. Using outdated, non-adaptive technology results in an enormous waste of expensive computing resources and energy, while slowing down time to market.

The 5th International Workshop on Adaptive Self-tuning Computing Systems is an interdisciplinary forum for researchers, practitioners, developers and application writers to discuss ideas, experience, methodology, applications, practical techniques and tools to improve or change current and future computing systems using self-tuning technology. Such systems should be able to automatically adjust their behavior to multi-objective usage scenarios at all levels (hardware and software) based on empirical, dynamic, iterative, statistical, collective, bio-inspired, machine learning and alternative techniques while fully utilizing available resources.

Submission guidelines


Procedure will be based on our proposal [ arXiv, ACM DL ].

Although not mandatory, to support our public effort started in 2007 to enable collaborative, systematic and reproducible research and experimentation on auto-tuning combined with machine learning, crowdsourcing and run-time adaptation, we particularly welcome papers where research material is shared and experimental results can be validated by the community. However, rather than enforcing specific procedure for validation, we decided to allow authors of the accepted papers to include an archive with all related research material and readme.txt file describing how to validate their experiments.




If you would like to sponsor our workshop, please contact us