Chronological Overview 
 Type-Hierarchical Overview 
Formal Methods in Computing
(Most of the papers antecedent to 1995
are not included in the list)
FRAMES  NO FRAME 

cwc:gpu:dcperf:14 (In proceedings)
Author(s) Marco Aldinucci, Maurizio Drocco, Guilherme Peretti Pezzi, Claudia Misale, Fabio Tordini and Massimo Torquati
Title« Exercising high-level parallel programming on streams: a systems biology use case »
InProc. of the 2014 IEEE 34th Intl. Conference on Distributed Computing Systems Workshops (ICDCS)
Year2014
PublisherIEEE
AddressMadrid, Spain
URLhttp://calvados.di.unipi.it/storage/paper_files/2014_dcperf_cwc_gpu.pdf
Abstract & Keywords
The stochastic modelling of biological systems, cou- pled with Monte Carlo simulation of models, is an increasingly popular technique in Bioinformatics. The simulation-analysis workflow may result into a computationally expensive task reducing the interactivity required in the model tuning. In this work, we advocate high-level software design as a vehicle for building efficient and portable parallel simulators for a variety of platforms, ranging from multi-core platforms to GPGPUs to cloud. In particular, the Calculus of Wrapped Compartments (CWC) parallel simulator for systems biology equipped with on- line mining of results, which is designed according to the FastFlow pattern-based approach, is discussed as a running example. In this work, the CWC simulator is used as a paradigmatic example of a complex C++ application where the quality of results is correlated with both computation and I/O bounds, and where high-quality results might turn into big data. The FastFlow parallel programming framework, which advocates C++ pattern- based parallel programming makes it possible to develop portable parallel code without relinquish neither run-time efficiency nor performance tuning opportunities. Performance and effectiveness of the approach are validated on a variety of platforms, inter-alia cache-coherent multi-cores, cluster of multi-core (Ethernet and Infiniband) and the Amazon Elastic Compute Cloud.

Keywords: fastflow, gpu, bioinformatics

BibTeX code

@inproceedings{cwc:gpu:dcperf:14,
  keywords = {fastflow, gpu, bioinformatics},
  booktitle = {Proc. of the 2014 IEEE 34th Intl. Conference on Distributed
               Computing Systems Workshops (ICDCS)},
  url = {http://calvados.di.unipi.it/storage/paper_files/2014_dcperf_cwc_gpu.pdf},
  title = {Exercising high-level parallel programming on streams: a systems
           biology use case},
  author = {Marco Aldinucci and Maurizio Drocco and Guilherme {Peretti Pezzi}
            and Claudia Misale and Fabio Tordini and Massimo Torquati},
  address = {Madrid, Spain},
  abstract = {The stochastic modelling of biological systems, cou- pled with
              Monte Carlo simulation of models, is an increasingly popular
              technique in Bioinformatics. The simulation-analysis workflow may
              result into a computationally expensive task reducing the
              interactivity required in the model tuning. In this work, we
              advocate high-level software design as a vehicle for building
              efficient and portable parallel simulators for a variety of
              platforms, ranging from multi-core platforms to GPGPUs to cloud.
              In particular, the Calculus of Wrapped Compartments (CWC) parallel
              simulator for systems biology equipped with on- line mining of
              results, which is designed according to the FastFlow pattern-based
              approach, is discussed as a running example. In this work, the CWC
              simulator is used as a paradigmatic example of a complex C++
              application where the quality of results is correlated with both
              computation and I/O bounds, and where high-quality results might
              turn into big data. The FastFlow parallel programming framework,
              which advocates C++ pattern- based parallel programming makes it
              possible to develop portable parallel code without relinquish
              neither run-time efficiency nor performance tuning opportunities.
              Performance and effectiveness of the approach are validated on a
              variety of platforms, inter-alia cache-coherent multi-cores,
              cluster of multi-core (Ethernet and Infiniband) and the Amazon
              Elastic Compute Cloud.},
  publisher = {IEEE},
  year = {2014},
}


 Chronological Overview 
 Type-Hierarchical Overview 
Formal Methods in Computing
(Most of the papers antecedent to 1995
are not included in the list)
FRAMES  NO FRAME 

This document was generated by bib2html 3.3.
(Modified by Luca Paolini, under the GNU General Public License)

Valid HTML 4.01!