Can your program run faster, compile faster, and adapt faster?

There is no limit of speed in the logic world.

Adapt faster through self-tuning

Quality requirements of a software system cannot be optimally met, especially when it is running in an uncertain and changing environment. In principle, a controller at runtime can monitor the change impact on quality requirements of the system, update the expectations and priorities from the environment, and take reasonable actions to improve the overall satisfaction. In practice, however, existing controllers are mostly designed for tuning low- level performance indicators rather than high-level requirements. By maintaining a live goal model to represent the runtime requirements and linking the overall satisfaction to an earned business value indicator as feedback, we propose a control-theoretic self-tuning method that can dynamically tune the preferences of different quality requirements, and can autonomously make the tradeoff decisions among different quality requirements through our preference-based goal reasoning. The reasoning result is involved to reconfigure the variation points of the goal model, and accordingly mapped to the system architecture reconfiguration. The effectiveness of our self-tuning method is evaluated by comparing the earned business value with the static and ad-hoc methods and analysing the self-tuning process.
  • Peng, Xin; Chen, Bihuan; Yu, Yijun and Zhao, Wenyun (2012). Self-tuning of software systems through goal-based feedback control loop. Journal of Systems and Software, 85(12) pp. 2707-2719.
  • Compiler faster through precompilation

    Large-scale legacy programs take long time to compile, thereby hampering productivity. This paper presents algorithms that reduce compilation time by analyzing syntactic dependencies in fine-grain program units, and by removing redundancies as well as false dependencies. These algorithms are combined with parallel compilation techniques (compiler farms, compiler caches), to further reduce build time. We demonstrate through experiments their effectiveness in achieving significant speedup for both fresh and incremental builds.
  • Yijun Yu, Homayoun Dayani-Fard, John Mylopoulos, and Periklis Andritsos (2005). "Reducing Build Time through Precompilations for Evolving Large Software". In: 21st IEEE International Conference on Software Maintenance, 26-29 Sep 2005, Budapest, Hungary.
  • Run faster through visualisation

    Cache behavior of a program has an ever-growing strong impact on its execution time. Characterizing the behavior by visible patterns is considered a way to pinpoint the bottleneck against performance. This paper presents a framework of visualization for trace distributions to extract the useful cache behavior patterns. We focus on cache misses, reuse distances, temporal or spatial localities, etc. The histograms of these distribution patterns measure the behavior in quantity, revealing effective program optimizations. The performance bottlenecks are exposed as hot spots highlighted in the source code, showing the exact locations to apply suitable optimizations. The impact of the source-level program optimizations, again, can be verified by the visualization tool.
  • Yijun Yu, Kristof Beyls, and Erik H. D'Hollander (2004). Visualizing the Impact of the Cache on Program Execution. Journal for the Integrated Study of AI, Cognitive Science and Applied Epistemology, 19(3-4), pp. 1-23.

  • Email: y.yu@open.ac.uk Office: +44 (0) 1908 6 55562