Accelerometer: Understanding Acceleration Opportunities for Data Center Overheads at Hyperscale
Session: Evaluation techniques--Accelerating accelerator adoption.
Authors: Akshitha Sriraman (University of Michigan); Abhishek Dhanotia (Facebook)
At global user population scale, important microservices in warehouse-scale data centers can grow to account for an enormous installed base of servers. With the end of Dennard scaling, successive server generations running these microservices exhibit diminishing performance returns. Hence, it is imperative to understand how important microservices spend their CPU cycles to determine acceleration opportunities across the global server fleet. To this end, we first undertake a comprehensive characterization of the top seven microservices that run on the compute-optimized data center fleet at Facebook. Our characterization reveals that microservices spend as few as 18% of CPU cycles executing core application logic (e.g., performing a key-value store); the remaining cycles are spent in common operations that are not core to the application logic (e.g., I/O processing, logging, and compression). Accelerating such common building blocks can greatly improve data center performance. Whereas developing specialized hardware acceleration for each building block might be beneficial, it becomes risky at scale if these accelerators do not yield expected gains due to performance bounds precipitated by offload-induced overheads. To identify such performance bounds early in the hardware design phase, we develop an analytical model, Accelerometer, for hardware acceleration that projects realistic speedup in microservices. We validate Accelerometer's utility in production using three retrospective case studies and demonstrate how it estimates the real speedup with <= 3.7% error. We then use Accelerometer to project gains from accelerating important common building blocks identified by our characterization.