CDPU: Co-designing Compression and Decompression Processing Units for Hyperscale Systems

General-purpose lossless data compression and decompression ("(de)compression") are used widely in hyperscale systems and are key "datacenter taxes". However, designing optimal hardware compression and decompression processing units ("CDPUs") is challenging due to the variety of algorithms deployed, input data characteristics, and evolving costs of CPU cycles, network bandwidth, and memory/storage capacities.

To navigate this vast design space, we present the first largescale data-driven analysis of (de)compression usage at a major cloud provider by profiling Google's datacenter fleet. We find that (de)compression consumes 2.9% of fleet CPU cycles and 10-50% of cycles in key services. Demand is also artificially limited; 95% of bytes compressed in the fleet use less capable algorithms to reduce compute, motivating a CDPU that changes cost vs. size tradeoffs.

Prior work has improved the microarchitectural state-of-the-art for CDPUs supporting various algorithms in fixed contexts. However, we find that higher-level design parameters like CDPU placement, hash table sizing, history window sizes, and more have as significant of an impact on the viability of CDPU integration, but are not well-studied. Thus, we present the first end-to-end design/evaluation framework for CDPUs, including: 1. An open-source RTLbased CDPU generator that supports many run-time and compiletime parameters. 2. Integration into an open-source RISC-V SoC for