Multi-Prediction Compression: An Efficient and Scalable Memory Compression Framework for GP-GPU

Hoyong Jin, Donghun Jeong, Taewon Park, Jong Hwan Ko, Jungrae Kim

Abstract: Data-intensive applications and throughput-oriented processors demand more memory bandwidth. Memory compression can provide more data beyond physical limits, yet new data types and smaller block sizes are challenging. This paper presents a novel and lightweight memory compression framework, Multi-Prediction Compression (MPC), to increase the effective memory bandwidth. Based on multiple prediction models and data-driven algorithm tuning, MPC can provide 31.7% better compression than state-of-the-art (SOTA) algorithms for 32B blocks. Moreover, MPC is hardware-friendly and scalable to support a growing number of data patterns.