Speeding-up Multiprocessors Running DSS Workloads through Coherence Protocols

In this work, we analyze how a DSS (Decision Support System) workload can be accelerated in the case of a shared-bus shared-memory multiprocessor, by adding simple support to the classical MESI solution for the coherence protocol. The DSS workload has been set-up utilizing the TPC-D benchmark on the PostgreSQL DBMS. Analysis has been performed via trace driven simulation and the operating system effects are also considered in our evaluation. We analyzed a basic four-processor and a high-end sixteen-processor machine, implementing MESI and two coherence protocols which deal with migration of processes and data, PSCR and AMSD. Results show that, even in the four processor case, for a DSS workload the use of a write-update protocol with a selective invalidation strategy for private data improves performance, and scalability, with respect to a classical MESI based architecturesolution, because of the access pattern to shared data and the lower bus utilization due to the absence of invalidation miss when we eliminate the contribution of passive sharing. In the 16 processor case, and especially in situation when the scheduler cannot apply the affinity requirements, the gain becomes more important, the advantage of a write-update protocol with a selective invalidation strategy for private data, in term of execution time, could be quantified in a 20\% relatively to the other evaluated protocols. This advantage is about 50\% in the case of high cache-to-cache transfer latency.