Accelerating DSS Workloads through Coherence Protocols

n 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 and inexpensive support for a coherence protocol, in order to reduce the overhead produced by thread migration. Indeed, it is well known that, in this kind of systems, the bus is the critical element that can limit the scalability of the machine. Nevertheless, many factors that influence bus utilization have not been yet investigated for this kind of workload, in particular the effects of thread migration. For the sake of completeness the DSS workload is not running alone in the machine but together with other programs that might be spawned by the DSS application, like system commands or other software typically running in this kind of machine. The operating system effects are also considered in our evaluation. We analyzed a basic four-processor and a high-end sixteen-processor machine, implementing three different coherence protocols (including MESI and another solution from the literature). We show that even in the four-processor case, the overhead induced by the sharing of private data, as a consequence of process migration, namely passive sharing, cannot be neglected. Indeed, the analysis shows that a protocol based on a selective strategy for dealing with private and shared data has a better performance than protocols either relying on the detection of migratory access-pattern or purely using a Write-Invalidate strategy, like MESI.DAWe varied the architectural parameters to show how passive sharing and other coherence overhead are influenced by different cache choices. Then, we considered the sixteen-processor case, where the effects on performance are more evident. We also end up that performance can take advantage of large caches and cache affinity scheduling. However, even with affinity scheduling, a selective protocol delivers better performance