The context mixing compression algorithm (CM) offers an extremely high compression ratio, effectively reducing data storage costs. However, its throughput on CPU platforms is extremely low, making it difficult to fully exploit the algorithm's inherent parallel potential: neither sustained byte-level SIMD execution is attainable, nor does the throughput scale close to linear in the number of threads on multicore CPUs. To address these issues, we propose a collaborative byte-level and task-level parallel design scheme, LPAQMP. At the byte level, LPAQMP completely eliminates residual dependencies between bits by restructuring the data path; at the task level, LPAQMP introduces a global rollback hash table (GRHT), significantly improving cache efficiency and achieving approximately linear throughput scaling as threads increase. Experiments show that while maintaining a high compression ratio, LPAQMP achieves a throughput of$152.67 \text{MB} / \mathrm{s}$, a$12.3 \times$improvement over LPAQ-CPU and$1.2 \times$higher than SOTA.

Shusheng Li, Jiale Li, Yifei Qu, Xinwei Shi, Yanliang Guo, Ziyi He, Yubo Wang, Wenjun Tan. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.