DAdHTM: Low overhead dynamically adaptive hardware transactional memory for large graphs a scalability study

摘要

With the availability of multicore and manycore systems with large main memories, Symmetric Multiprocessors (SMPs) can process large scale problems such as graphs spanning millions of nodes and billions of edges which used to be the domain of large clusters. Due to the shared memory architecture of SMPs, we can apply fast, novel policies such as Transactional Memory (TM) to speed up the processing of such problems. Interestingly, most applications in bioinformatics, social networks, and cybersecurity can be represented as large graphs. However, these graphs are sparse in nature. Therefore, TM as a synchronization policy for critical sections can provide better performance since it performs better in low conflict scenarios. Many TM variants exist including: Software, Hardware and combinations of both. Furthermore, a TM can adapt to applications' behavior. In this paper, we introduce DAdHTM. A Dynamically Adaptive Hardware TM (DAdHTM) designed to adapt the HTM to the application's behavior. DAdHTM outperforms not only locks but also low overhead Software TM (STM) and even Adaptive Hybrid TM (DyAdHyTM) on a 48 core 64GB memory dual socket SMP machine. Our experiments show that DAdHTM has low overhead. It is competitive at core/thread counts below 24 (close to the best) and outperforms all other techniques at core/thread counts above 24 by up to 43%.