Hyperverse: A High Throughput Pattern Matching Engine for Metaverse

Cyberspace has continued to evolve since the In-ternet became widespread in the 1990s. A variety of computer-mediated virtual environments have been created, including social networks, video conferencing, virtual 3D worlds (e.g., VR chat), augmented reality applications (e.g., Ingress), and non-fungible token games. Such virtual environments, while not permanent and incoherent, have brought us varying degrees of digital transformation. The term “metaverse” was devised to further facilitate the digital transformation of all aspects of our physical lives. With the increasing number of devices and users such as loT, augmented reality and virtual reality glass connected to the metaverse, huge amounts of data need to be filtered or captured for metaverse security or user behavior analysis by utilizing pattern matching. However, directly utilizing the existing pattern matching engine is impossible since it cannot achieve the throughput that is required by the metaverse, where low throughput could cause poor user experiences. Thus, in this paper, we propose a new pattern matching design called Hyperverse. Hyperverse can significantly increase the throughput of pattern matching by designing a new algorithm that is based on instruction-level parallelism. We implement Hyperverse in Hyperscan, which is the fastest regular expression engine in the world. Compared with the existing solution, Hyperverse can achieve a throughput of up to 10.4Gbps per core, which is a 3.83x boost than the existing solution. Thus, the significantly increased throughput will prevent a negative impact on the user experience in the metaverse.