Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Marcin BOGDAŃSKI, Peter R. LEWIS, Tobias BECKER, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

7 Citations (Scopus)


Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations. © 2011 IEEE.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011
Number of pages6
Publication statusPublished - Jun 2011
Externally publishedYes


  • Artificial Neural Network
  • Dynamic Optimisation
  • FPGA
  • Genetic Algorithm
  • GPU
  • Heterogeneous System
  • On-Line Learning
  • Scheduling


Dive into the research topics of 'Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations'. Together they form a unique fingerprint.

Cite this