11959

Exercising high-level parallel programming on streams: a systems biology use case

Marco Aldinucci, Maurizio Drocco, Guilherme Peretti Pezzi, Claudia Misale, Fabio Tordini, Massimo Torquati
Computer Science Department, University of Torino, Italy
IEEE 34th Intl. Conference on Distributed Computing Systems Workshops (ICDCS), 2014

@inproceedings{cwc:gpu:dcperf:14,

   address={Madrid, Spain},

   author={Marco Aldinucci and Maurizio Drocco and Guilherme {Peretti Pezzi} and Claudia Misale and Fabio Tordini},

   booktitle={Proc. of the 2014 IEEE 34th Intl. Conference on Distributed Computing Systems Workshops (ICDCS)},

   date-added={2014-04-19 12:44:39 +0000},

   date-modified={2014-04-19 12:58:44 +0000},

   keywords={fastflow, gpu, bioinformatics},

   publisher={IEEE},

   title={Exercising high-level parallel programming on streams: a systems biology use case},

   url={http://calvados.di.unipi.it/storage/paper_files/2014_dcperf_cwc_gpu.pdf},

   year={2014}

}

Download Download (PDF)   View View   Source Source   

791

views

The stochastic modelling of biological systems, coupled 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 online 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.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: