DocumentCode :
2534500
Title :
Exploiting GPU On-chip Shared Memory for Accelerating Schedulability Analysis
Author :
Nunna, Swaroop ; Bordoloi, Unmesh D. ; Chakraborty, Samarjit ; Eles, Petru ; Peng, Zebo
Author_Institution :
Tech. Univ. Munich, Munich, Germany
fYear :
2010
fDate :
20-22 Dec. 2010
Firstpage :
147
Lastpage :
152
Abstract :
Embedded electronic devices like mobile phones and automotive control units must perform under strict timing constraints. As such, schedulability analysis constitutes an important phase of the design cycle of these devices. Unfortunately, schedulability analysis for most realistic task models turn out to be computationally intractable (NP-hard). Naturally, in the recent past, different techniques have been proposed to accelerate schedulability analysis algorithms, including parallel computing on Graphics Processing Units (GPUs). However, applying traditional GPU programming methods in this context restricts the effective usage of on-chip memory and in turn imposes limitations on fully exploiting the inherent parallel processing capabilities of GPUs. In this paper, we explore the possibility of accelerating schedulability analysis algorithms on GPUs while exploiting the usage of on-chip memory. Experimental results demonstrate upto 9× speedup of our GPU-based algorithms over the implementations on sequential CPUs.
Keywords :
computer graphic equipment; embedded systems; microprocessor chips; parallel processing; GPU on-chip shared memory; NP-hard; embedded electronic devices; graphics processing units; parallel computing; parallel processing; schedulability analysis; Algorithm design and analysis; Computational modeling; Graphics processing unit; Instruction sets; Programming; Real time systems; System-on-a-chip;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic System Design (ISED), 2010 International Symposium on
Conference_Location :
Bhubaneswar
Print_ISBN :
978-1-4244-8979-4
Electronic_ISBN :
978-0-7695-4294-2
Type :
conf
DOI :
10.1109/ISED.2010.36
Filename :
5715166
Link To Document :
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