ZIP-IO: Architecture for application-specific compression of Big Data
Sang Woo Jun, Kermin Elliott Fleming, Michael W Adler, Joel Emer · 2012
We have entered the “Big Data” age: scaling of networks and sensors has led to exponentially increasing amounts of data. Compression is an effective way to deal with many of these large data sets, and application-specific compression algorithms have become popular in problems with large working sets. Unfortunately, these compression algorithms are often computationally difficult and can result in application-level slow-down when implemented in software. To address this issue, we investigate ZIP-IO, a framework for FPGA-accelerated compression. Using this system we demonstrate that an unmodified industrial software workload can be accelerated 3x while simultaneously achieving more than 1000x compression in its data set.