Artificial bee colony-inspired run-time task management for many-core systems

Ali Mohamed Ahmed Abuassal, Gianluca Tempesti, Martin Albrecht Trefzer

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Efficient resource and application management
is one of the most complex and challenging tasks in high
performance computing. Large-scale computing systems
that contain hundreds, thousands or even millions of cores
demand solutions that can operate in a distributed, robust,
and scalable fashion. However, while hardware parallelism
is relatively straight forward to achieve, this is not generally
the case for software. This leads to under-utilization of the
hardware parallelism as well as imbalanced load distribution
causing inefficiency and hotspots. In response to this challenge,
this paper introduces a novel distributed and decentralized
run-time management algorithm. The proposed method is
guided by an optimization model inspired by artificial bee
colonies (ABC). While ABC have proven useful for optimizing
large sets of numerical test functions, this is the first time they
are applied in the context of many-core system management.
The initial result shows that, the ABC model is promising in
context of run-time management for many-core systems. It is
also anticipated that the algorithms bio-inspired foundations
will inherently enable scalability, reliability, and adaptation.
We are showing initial experiments, where the initial results
indicate the capability of our model to improve the thermal
distribution across the system.
Original languageEnglish
Title of host publication2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Place of PublicationUSA
Number of pages8
ISBN (Electronic)978-1-5386-9276-9
ISBN (Print)978-1-5386-9277-6
Publication statusPublished - 30 Nov 2018
Event2018 SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE - Sheraton Grand Bangalore Hotel @ Brigade Gateway, Bengaluru, India
Duration: 18 Nov 201821 Nov 2018


Abbreviated titleIEEE-SSCI 2018
Internet address

Bibliographical note

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  • Many-core system
  • Bio-inspired Hardware
  • bee colony
  • run-time management

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