Eunomia: Scaling Concurrent Index Structures Under Contention Using HTM

Abstract

Hardware transactional memory (HTM) is an emerging hardware feature. HTM simplifies the programming model of concurrent programs while preserving high and scalable performance. With the commercial availability of HTM-capable processors, HTM has recently been adopted to construct efficient concurrent index structures. However, with the expansion of data volume and user amount, data management systems have to process workloads exhibiting high contention; meanwhile, according to our experiments, the conventional HTM-base concurrent index structures fail to provide scalable performance under highly-contented workloads. Such performance pathology strictly constrains the usage of HTM on data management systems. In this paper, we first conduct a thorough analysis on HTM-based concurrent index structures, and uncover several reasons for excessive HTM aborts incurred by both false and true conflicts under contention. Based on the analysis, we advocate Eunomia, a design pattern for HTM-based concurrent index structure which contains several principles to improve HTM performance, including splitting HTM regions with version-based concurrency control to reduce HTM working sets, partitioned data layout to reduce false conflicts, proactively detecting and avoiding conflicting requests, and adaptive concurrency control strategy. To validate their effectiveness, we apply such design principles to construct a scalable concurrent B+Tree and a skip list using HTM. Evaluation using key-value store and database benchmarks on a 20-core HTM-capable multi-core machine shows that Eunomia leads to substantial speedup under high contention, while incurring small overhead under low contention.