Abstract-Nowadays, high-performance transaction processing applications increasingly run on multisocket multicore servers. Such architectures exhibit non-uniform memory access latency as well as non-uniform thread communication costs. Unfortunately, traditional shared-everything database management systems are designed for uniform inter-core communication speeds. This causes unpredictable access latencies in the critical path. While lack of data locality may be a minor nuisance on systems with fewer than 4 processors, it becomes a serious scalability limitation on larger systems due to accesses to centralized data structures. In this paper, we propose ATraPos, a storage manager design that is aware of the non-uniform access latencies of multisocket systems. ATraPos achieves good data locality by carefully partitioning the data as well as internal data structures (e.g., state information) to the available processors and by assigning threads to specific partitions. Furthermore, ATraPos dynamically adapts to the workload characteristics, i.e., when the workload changes, ATraPos detects the change and automatically revises the data partitioning and thread placement to fit the current access patterns and hardware topology. We prototype ATraPos on top of an open-source storage manager Shore-MT and we present a detailed experimental analysis with both synthetic and standard (TPC-C and TATP) benchmarks. We show that ATraPos exhibits performance improvements of a factor ranging from 1.4 to 6.7x for a wide collection of transactional workloads. In addition, we show that the adaptive monitoring and partitioning scheme of ATraPos poses a negligible cost, while it allows the system to dynamically and gracefully adapt when the workload changes.
ATraPos REF , which is based on PLP, is a storage manager design that is aware of the nonuniform access latencies of multisocket systems.
ATraPos: Adaptive transaction processing on hardware Islands
2014 IEEE 30th International Conference on Data Engineering
2014 IEEE 30th International Conference on Data Engineering
Computer Science