StAdHyTM: A Statically Adaptive Hybrid Transactional Memory: A scalability study on large parallel graphs
Conference
Qayum, MA, Badawy, AHA, Cook, J. (2017). StAdHyTM: A Statically Adaptive Hybrid Transactional Memory: A scalability study on large parallel graphs
. 10.1109/CCWC.2017.7868398
Qayum, MA, Badawy, AHA, Cook, J. (2017). StAdHyTM: A Statically Adaptive Hybrid Transactional Memory: A scalability study on large parallel graphs
. 10.1109/CCWC.2017.7868398
In this paper, we present a Statically Adaptive Hybrid Transactional Memory (StAdHyTM) that outperforms not only existing Hardware TM (HTM) and Software TM (STMs) but also common synchronization schemes such as locks. StAdHyTM is statically tuned to adapt to the application behavior to improve the performance. We focus in particular on large parallel graph applications. Our StAdHyTM implementation outperforms coarse-grain locks by up to 8.1× and STM by up to 2.6× in total execution time for computation kernel in the SSCA-2 benchmark. It also outperforms HTM by up to 2.1× on a 28-cores and 64GB machine. We tested large graphs of up to 268 million vertices and 2.147 billion edges on a 64-core and 128 GB machine. To the best of our knowledge, this work is the first scalability study of synchronizations involving all the TM implementations- HTM, STM, HyTM, and Adaptive HyTM.
