Exploring Approximate Communication Using Lossy Bitwise Compression on Interconnection Networks

The use of approximate communication has emerged as a promising approach for enhancing the efficiency of communication in parallel computer systems. By sending incomplete or imprecise messages, approximate communication can significantly reduce communication time. In this study, we examine application-level techniques for approximate communication to enable high portability on high-performance interconnection networks. Specifically, we focus on lossy compression of floating-point data, which is frequently exchanged between compute nodes in parallel applications. Our approach involves a simple application scenario where a source process compresses a communication dataset and a destination process decompresses it in an MPI parallel program. We use two bitwise procedures for compression: lossy bit-zip compression and lossless bit-mask compression. Our aim is to transmit the largest possible amount of approximate data with the least possible compression overhead. Additionally, we explore error check and correction techniques to ensure bit-flip fault tolerance for the compressed data during transmission. We implement our scheme in several communication-intensive MPI applications and demonstrate that our approximate communication approach effectively speeds up total execution time while staying within a specified quality-of-result error bound .