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Hardware transactional memory architecture with adaptive version management for multi-processor FPGA platforms
Affiliation:1. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia;2. Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia Kuala Lumpur, 54100 KL, Malaysia;3. IJN-UTM Cardiovascular Engineering Centre, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia;1. Informatics Institute, University of Amsterdam, Science Park 904, 1098XH Amsterdam, The Netherlands;2. School of Computer Science, National University of Defence Technology, Yanwachi Main Street 47, Changsha, Hunan, China;1. Goethe-University Frankfurt, Frankfurt am Main, Germany;2. Intedis GmbH & Co. KG, Würzburg, Germany;1. LIMOSE Laboratory, University of Boumerdes, Independence Avenue, 35000 Algeria;2. INRIA, LHS-PEC 263 Avenue Général Leclerc, 35042 Rennes, France
Abstract:Multiprocessor embedded systems integrates diverse dedicated processing units to handle high performance applications such as in multimedia and network processing. However, lock-based synchronization limits the efficiency of such heterogeneous concurrent systems. Hardware Transactional Memory (HTM) is a promising approach in creating an abstraction layer for multi-threaded programming. However, HTM performance is application-specific and determined by version and conflict management configurations. Most previous HTM implementations for embedded system in literature were built on fixed version management that result in significant performance loss when transaction behaviour changes. In this paper, we propose a HTM targeted for embedded applications which is able to adapt its version management based on application behaviour at runtime. It is prototyped and analysed on Altera Cyclone IV platform. Random requests at different contention levels and different transaction sizes are used to verify the performance of the proposed HTM. Based on our experiments, lazy version management is able to obtain up to 12.82% speed-up compared to eager version management at high contention level. Meanwhile, eager version management obtains up to 37.84% speed-up compared to lazy version management at low contention. The adaptive mechanism is able to switch configuration at runtime based on applications behaviour for maximum performance.
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