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ePRO-MP: A Tool for Profiling and Optimizing Energy and Performance of Mobile Multiprocessor Applications
Author(s) -
Wonil Choi,
Hyun-Hee Kim,
Wook Song,
Jiseok Song,
Jihong Kim
Publication year - 2009
Publication title -
scientific programming
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.269
H-Index - 36
eISSN - 1875-919X
pISSN - 1058-9244
DOI - 10.1155/2009/838745
Subject(s) - computer science , energy consumption , profiling (computer programming) , programmer , multiprocessing , power consumption , key (lock) , embedded system , baseline (sea) , energy (signal processing) , real time computing , operating system , power (physics) , electrical engineering , engineering , mathematics , oceanography , physics , statistics , quantum mechanics , geology
For mobile multiprocessor applications, achieving high performance with low energy consumption is a challenging task. In order to help programmers to meet these design requirements, system development tools play an important role. In this paper, we describe one such development tool, ePRO-MP, which profiles and optimizes both performance and energy consumption of multi-threaded applications running on top of Linux for ARM11 MPCore-based embedded systems. One of the key features of ePRO-MP is that it can accurately estimate the energy consumption of multi-threaded applications without requiring a power measurement equipment, using a regression-based energy model. We also describe another key benefit of ePRO-MP, an automatic optimization function, using two example problems. Using the automatic optimization function, ePRO-MP can achieve high performance and low power consumption without programmer intervention. Our experimental results show that ePRO-MP can improve the performance and energy consumption by 6.1% and 4.1%, respectively, over a baseline version for the co-running applications optimization example. For the producer-consumer application optimization example, ePRO-MP improves the performance and energy consumption by 60.5% and 43.3%, respectively over a baseline version.

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