CPU Microarchitectural Performance Characterization of Cloud Video Transcoding

Video streaming accounts for more than 75% of all Internet traffic. Videos streamed to end-users are encoded to reduce their size in order to efficiently use the Internet traffic, and are decoded when played at end-users' devices. Videos have to be transcoded-i.e., where one encoding format is converted to another-to fit users' different needs of resolution, framerate and encoding format. Global streaming service providers (e.g., YouTube, Netflix, and Facebook) employ a large number of transcoding operations. Optimizing the performance of transcoding to provide speedup of a few percent can save millions of dollars in computational and energy costs. While prior works identified microarchitectural characteristics of the transcoding operation for different classes of videos, other parameters of video transcoding and their impact on CPU performance has yet to be studied. In this work, we investigate the microarchitectural performance of video transcoding with all videos from vbench, a publicly available cloud video benchmark suite. We profile the leading multimedia transcoding software, FFmpeg with all of its major configurable parameters across videos with different complexity (e.g., videos with high motion and frequent scene transition are more complex). Based on our profiling results, we find key bottlenecks in instruction cache, data cache, and branch prediction unit for video transcoding workloads. Moreover, we observe that these bottlenecks vary widely in response to variation in transcoding parameters. We leverage several state-of-the-art compiler approaches to mitigate performance bottlenecks of video transcoding operations. We apply AutoFDO, a feedback-directed optimization (FDO) tool to improve instruction cache and branch prediction performance. To improve data cache performance, we leverage Graphite, a polyhedral optimizer. Across all videos, AutoFDO and Graphite provide average speedups of 4.66% and 4.42% respectively. We also set up simulation settings with different microarchitecture configurations, and explore the potential improvement using a smart scheduler that assigns transcoding tasks to the best-fit configuration based on transcoding parameter values. The smart scheduler performs 3.72% better than the random scheduler and matches the performance of the best scheduler 75% of the time.