Scalable subspace snooping: Exploiting temporal sharing stability

Abstract

Although snoop-based coherence protocols provide fast cache-to-cache transfers with a simple and robust coherence mechanism, scaling the protocols has been difficult due to the overheads of broadcast snooping. In this paper, we propose a coherence filtering technique called subspace snooping, which stores the potential sharers of each memory page in the page table entry. By using the sharer information in the page table entry, coherence transactions for a page generate snoop requests only to the subset of nodes in the system (subspace). However, the coherence subspace of a page may evolve, as the phases of applications may change or the operating system may migrate threads to different nodes. To adjust subspaces dynamically, subspace snooping supports two shrinking mechanisms, which remove obsolete nodes from subspaces. Subspace snooping can be integrated to any type of coherence protocols and network topologies. As subspace snooping guarantees that a subspace always contains the precise sharers of a page except for speculative shrink, it does not restrict the designs of coherence protocols and networks. We evaluate subspace snooping with Token Coherence on un-ordered mesh networks. For scientific and server applications on a 16-core system, subspace snooping reduces 62% of snoops on average.