We present FlexCPU, a new software model for CPU performance integrated into gem5. FlexCPU combines the benefits of trace-based models with execute-in-execute semantics which leads to more accurate simulation of multithreaded and full-system applications. Our design is heavily inspired by dataflow models, and it reduces modern out-of-order techniques to abstracted parameterized constraints. FlexCPU can be configured to match the behaviors of modern general purpose CPUs and used for limit studies. By reducing CPU behaviors to reasonable abstractions and stages, FlexCPU is simpler to understand and easier to extend than other execute-in-execute CPU models. We show that FlexCPU can achieve the maximum theoretical ILP for most workloads and show a case study of using FlexCPU to model multiple processor architectures.

360-degree video transmission consumes 4∼6× the bandwidth of a regular video, and thus imposes significant challenges to networks. To address this challenge, in this paper, we propose a motion-prediction-based transmission mechanism that matches network video transmission to viewer needs. Ideally, if viewer motion is perfectly known in advance, we could reduce bandwidth consumption by 80%. Practically, however, we have to address the random nature of viewer motion, in order to guarantee the quality of the viewing experience. Based on our experimental study of viewer motion (comprising 16 video clips and over 150 subjects), we propose a machine learning mechanism that predicts viewer motion. Based on such predictions, we propose a partial-content-transmission mechanism that reduces the overall bandwidth consumption while providing probabilistic performance guarantees. Real-trace-based evaluations show that the proposed scheme significantly reduces bandwidth consumption with negligible performance degradation. For example, given a failure ratio of 0.1%, we can reduce bandwidth consumption by more than 40%.