# Trie Compression for GPU Accelerated Multi-Pattern Matching

## TL;DR

This paper introduces a trie compression algorithm for GPU-based multi-pattern matching, significantly reducing memory usage while maintaining high throughput, enabling efficient large-scale pattern matching on GPUs.

## Contribution

The paper presents a novel trie compression method that reduces memory by 85% and achieves over 22 Gbps throughput for GPU-accelerated pattern matching.

## Key findings

- 85% reduction in space requirements
- Over 22 Gbps throughput achieved
- Effective use of GPU shared and texture memory

## Abstract

Graphics Processing Units allow for running massively parallel applications offloading the CPU from computationally intensive resources, however GPUs have a limited amount of memory. In this paper a trie compression algorithm for massively parallel pattern matching is presented demonstrating 85% less space requirements than the original highly efficient parallel failure-less aho-corasick, whilst demonstrating over 22 Gbps throughput. The algorithm presented takes advantage of compressed row storage matrices as well as shared and texture memory on the GPU.