Abstract
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.
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This work is partly funded within the DFG-CRC 912 (HAEC).
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Krause, A., Ungethüm, A., Kissinger, T., Habich, D., Lehner, W. (2017). Asynchronous Graph Pattern Matching on Multiprocessor Systems. In: Kirikova, M., et al. New Trends in Databases and Information Systems. ADBIS 2017. Communications in Computer and Information Science, vol 767. Springer, Cham. https://doi.org/10.1007/978-3-319-67162-8_6
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DOI: https://doi.org/10.1007/978-3-319-67162-8_6
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