# Asynchronous Graph Pattern Matching on Multiprocessor Systems

## TL;DR

This paper introduces a scalable, data-oriented approach for asynchronous graph pattern matching on NUMA multiprocessor systems, addressing latency and scalability challenges in large graph processing.

## Contribution

It presents a novel technique leveraging data locality and asynchronous processing to improve pattern matching performance on NUMA architectures.

## Key findings

- Effective data locality preservation improves performance
- Asynchronous processing reduces concurrency bottlenecks
- Scalable pattern matching on large graphs achieved

## 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.