Pattern matching on large graphs is the foundation for a variety of application domains. Strict latency requirements and continuously increasing graph sizes demand the usage of highly parallel in-memory graph processing engines that need to consider non-uniform memory access (NUMA) and concurrency issues to scale up on modern multiprocessor systems. To tackle these aspects, graph partitioning becomes increasingly important. Hence, we present a technique to process graph pattern matching on NUMA systems in this paper. As a scalable pattern matching processing infrastructure, we leverage a data-oriented architecture that preserves data locality and minimizes concurrency-related bottlenecks on NUMA systems. We show in detail, how graph pattern matching can be asynchronously processed on a multiprocessor system.
@article{arxiv.1706.03968,
title = {Asynchronous Graph Pattern Matching on Multiprocessor Systems},
author = {Alexander Krause and Annett Ungethüm and Thomas Kissinger and Dirk Habich and Wolfgang Lehner},
journal= {arXiv preprint arXiv:1706.03968},
year = {2017}
}