In a typical commercial multi-core processor, the last level cache (LLC) is shared by two or more cores. While the shared LLC is beneficial to concurrent query processes with commonly shared data sets, it can also be a performance bottleneck to concurrent queries, each of which has private data structures, such as a hash table for the widely used hash join operator, causing serious cache conflicts. To address this problem, we have designed and implemented a hybrid system method called MCC-DB for accelerating executions of warehouse-style queries, which relies on the DBMS knowledge of data access patterns to minimize LLC conflicts in multi-core systems through an enhanced OS facility of cache partitioning.

MCC-DB consists of three components: (1) a cache-aware query optimizer carefully selects query plans in order to balance the numbers of cache-sensitive and cache-insensitive plans; (2) a query execution scheduler makes decisions to co-run queries with an objective of minimizing LLC conflicts; and (3) an enhanced OS kernel facility partitions the shared LLC according to each query’s cache capacity need and locality strength. We have implemented MCC-DB by patching the three components in PostgreSQL and Linux kernel. Our intensive measurements on an Intel multi-core system with warehouse-style queries show that MCC-DB can reduce query execution times significantly.

Xiaodong Zhang is the Robert M. Critchfield Professor in Engineering, and Chairman of the Department of Computer Science and Engineering at the Ohio State University. His research interests cover a wide spectrum in the areas of high performance and distributed systems. Several of technical innovations and research results from his team have been widely adopted in commercial processors and major operating systems and databases with direct contributions to the advancement of the memory systems.

Xiaodong received his Ph.D. in Computer Science from University of Colorado at Boulder. He is a Fellow of the IEEE.