Improving Database Performance on Simultaneous Multithreading Processors

Simultaneous multithreading (SMT) allows multiple threads to supply instructions to the instruction pipeline of a superscalar processor. Because threads share processor resources, an SMT system is inherently different from a multiprocessor system and, therefore, utilizing multiple threads on an SMT processor creates new challenges for database implementers. We investigate three thread-based techniques to exploit SMT architectures on memory-resident data. First, we consider running independent operations in separate threads, a technique applied to conventional multiprocessor systems. Second, we describe a novel implementation strategy in which individual operators are implemented in a multi-threaded fashion. Finally, we introduce a new data-structure called a work-ahead set that allows us to use one of the threads to aggressively preload data into the cache for use by the other thread. We evaluate each method with respect to its performance, implementation complexity, and other measures. We also provide guidance regarding when and how to best utilize the various threading techniques. Our experimental results show that by taking advantage of SMT technology we achieve a 30\% to 70\% improvement in throughput over single threaded implementations on in-memory database operations