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GPU-based Architectures and their Benefit for Accurate and Efficient Wireless Network Simulations

Andelfinger, P.; Mittag, J.; Hartenstein, H.

Abstract:
In recent years, a trend towards the usage of physical layer models with increased accuracy can be observed within the wireless network community. This trend has several reasons. The consideration of signals - instead of packets - as the smallest unit of a wireless network simulation enables the ability to reflect complex radio propagation characteristics properly, and to study novel PHY/MAC/NET cross-layer optimizations that were not directly possible before, e.g. cognitive radio networks and interference cancelation. Yet, there is a price to pay for the increase of accuracy, namely a significant decrease of runtime performance due to computationally expensive signal processing. In this paper we study whether this price can be reduced - or even eliminated - if GPU-based signal processing is employed. In particular, we present and discuss four different architectures that can be used to exploit GPU-based signal processing in discrete event-based simulations. Our evaluation shows that the runtime costs can not be cut down completely, but significant speedups can be expected compared to a non GPU-based solution.


Zugehörige Institution(en) am KIT Institut für Telematik (TM)
Steinbuch Centre for Computing (SCC)
Publikationstyp Proceedingsbeitrag
Jahr 2011
Sprache Englisch
Identifikator KITopen ID: 1000023373
Erschienen in Proceedings of the 19th Annual Meeting of the IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), Singapore - July 25 -27, 2011
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