ETD

Digital archive of theses discussed at the University of Pisa

 

Thesis etd-09262012-101649


Thesis type
Tesi di laurea magistrale
Author
XHAGJIKA, VAMIS
email address
vamis.xhagjika@gmail.com, xhagjika@cli.di.unipi.it
URN
etd-09262012-101649
Thesis title
Behavioural Skeletons in FastFlow
Department
INFORMATICA
Course of study
INFORMATICA
Supervisors
relatore Prof. Danelutto, Marco
Keywords
  • algorithmic skeleton
  • programmazione parallela
  • autonomic computing
  • fastflow framework
  • behavioural skeleton
Graduation session start date
12/10/2012
Availability
Withheld
Release date
12/10/2052
Summary
This thesis work consists in the implementation of a version of the Behavioural Skeletons (BS)
within the structured parallel programming framework FastFlow (FF). Therefore design,
implementation and experimentation are here considered and discussed.

Furthermore, with the introduction of the BS in FastFlow, we implement a fully
functional Autonomic System for run-time optimization of non-functional concerns.
Extensive details are given for the design and implementation choices of the autonomic
components.

Moreover we discuss design and implementation choices for modifications
to the already present algorithmic skeletons of FF. The above mentioned variations give
the skeletons dynamic features, permitting run-time changes of their structure. As for the
management subsystem, we discuss the realization of sensors and actuators (Autonomic Controller)
for the normal FF skeletons and the different available models for the
management components (Atonomic Managers).

Experiments are conducted to demonstrate the features of the newly extended FastFlow framework,
with functional experiments covering the majority of the implemented components and an example
of run-time optimiziation of a composed complex Behavioral Skeleton structure.

In conclusion, we have demonstrated succsessful design, implementation and experimentation of
Behavioural Skeletons, typical constructs of distributed computing, in the different context
of parallel computing. Which leads to a fully functional autonomic system model for the FastFlow
framework.
File