Fast and Energy-Efficient OLAP Data Management on Hybrid Main Memory Systems

This paper studies the problem of efficiently utilizing hybrid memory systems, consisting of both Dynamic Random Access Memory (DRAM) and novel Non-Volatile Memory (NVM) in database management systems (DBMS) for online analytical
processing (OLAP) workloads. We present a methodology to determine the database operators that are responsible for most main memory accesses. Our analysis uses both cost models and empirical measurements. We develop heuristic decision procedures to allocate data in hybrid memory at the time that the data buffers are allocated, depending on the expected memory access frequency. We implement these heuristics in the MonetDB column-oriented database and demonstrate performance improvement and energy-efficiency as compared to state-of-the-art application-agnostic hybrid memory management techniques.