A new scalable directory architecture for large-scale multiprocessors

Abstract

The memory overhead introduced by directories constitutes a major hurdle in the scalability of cc-NUMA architectures, which makes the shared-memory paradigm unfeasible for very large-scale systems. This work is focused on improving the scalability of shared-memory multiprocessors by significantly reducing the size of the directory. We propose multilayer clustering as an effective approach to reduce the directory-entry width. Detailed evaluation for 64 processors shows that using this approach we can drastically reduce the memory overhead, while suffering a performance degradation we similar to previous compressed schemes (such as Coarse Vector). In addition, a novel two-level directory architecture is proposed in order to eliminate the penalty caused by these compressed directories. This organization consists of a small Full-Map first-level directory (which provides precise information for the most recently referenced lines) and a compressed second-level directory (which provides in-excess information). Results show that a system with this directory architecture can achieve the same performance as a multiprocessor with a big and non-scalable Full-Map directory with a very significant reduction of the memory overhead.