Memory performance of DSS commercial workloads in shared-memory multiprocessors

Although cache-coherent shared-memory multiprocessors are often used to run commercial workloads, little work has been done to characterize how well these machines support such workloads. In particular, we do not have much insight into the demands of commercial workloads on the memory subsystem of these machines. In this paper, we analyze in detail the memory access patterns of several queries that are representative of Decision Support System (DSS) databases. Our analysis shows that the memory use of queries differs largely depending on how the queries access the database data, namely via indices or by sequentially scanning the records. The former queries, which we call Index queries, suffer most of their shared-data misses on indices and on lock-related metadata structures. The latter queries, which we call Sequential queries, suffer most of their shared-data misses on the database records as they are scanned. An analysis of the data locality in the queries shows that both Index and Sequential queries exhibit spatial locality and, therefore, can benefit from relatively long cache lines. interestingly, shared data is reused very little inside queries. However, there is data reuse across Sequential queries. Finally, we show that the performance of Sequential queries can be improved moderately with data prefetching.