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Big data is a buzzword used to describe massive volumes of data that providesopportunities of exploring new insights through data analytics. However, bigdata is mostly structured but can be semi-structured or unstructured. It isnormally so large that it is not only difficult but also slow to process usingtraditional computing systems. One of the solutions is to format the data asgraph data structures and process them on shared memory architecture to usefast and novel policies such as transactional memory. In most graphapplications in big data type problems such as bioinformatics, social networks,and cyber security, graphs are sparse in nature. Due to this sparsity, we havethe opportunity to use Transactional Memory (TM) as the synchronization policyfor critical sections to speedup applications. At low conflict probability TMperforms better than most synchronization policies due to its inherentnon-blocking characteristics. TM can be implemented in Software, Hardware or acombination of both. However, hardware TM implementations are fast but limitedby scarce hardware resources while software implementations have high overheadswhich can degrade performance. In this paper, we develop a low overhead, yetsimple, dynamically adaptive (i.e. at runtime) hybrid (i.e. combines hardwareand software) TM (DyAdHyTM) scheme that combines the best features of bothHardware TM (HTM) and Software TM (STM) while adapting to applicationrequirements. It performs better than coarse grain lock by up to 8.12x, a lowoverhead STM by up to 2.68x, a couple of implementations of HTMs (by up to2.59x), and other HyTMs (by up to 1.55x) for SSCA2 graph benchmark running on amulticore machine with a large shared memory.

DyAdHyTM: A Low Overhead Dynamically Adaptive Hybrid Transactional Memory on Big Data Graphs