Characterizing parallel workloads to reduce multiple writer overhead in shared virtual memory systems

Shared virtual memory (SVM) systems, because of their software implementation, enable shared-memory programming at a low design and maintenance cost. Nevertheless, as hardware implementations become faster, their performance is still far from that achieved by distributed shared memory (DSM) systems. Nowadays, SVM systems use relaxed memory consistency models and multiple writer protocols as techniques to reduce latencies and false sharing, respectively. However, these techniques induce additional overhead that decreases system performance. We performed a study of workload behavior aimed at improving the design of SVM protocols. The work focused on the identification of the type of shared data patterns that can appear in the accesses to protected sections using semaphores. Most coherence actions in SVM systems are performed as a consequence of the write operations executed in critical sections, so we pay special attention to the write operations performed when multiple writers are allowed. As these write operations may present spatial locality, we also study the write patterns on shared pages with similar behaviour. Different software filters are applied in the instrumented parallel workloads selected to capture and classify the most common sharing patterns. This enables the recognition of those patterns in which coherence overhead can be reduced by modifying the coherence actions performed by the protocol. Despite the fact that the performance evaluation of new coherence solutions is not our main goal, the ideas presented to improve the behaviour of SVM systems can be implemented at a reasonable hardware/software cost