一句话：提出低延迟预测系统服务 PSS，用反馈学习决定何时应用优化，简化应用性能编程。
论文提出一种由低延迟预测系统服务（PSS）支撑的软件优化策略，以改进应用性能编程。其核心思想是让程序员只编写在特定情形或特定调优下有效的优化，而把如何以及何时应用这些优化的决策交给系统，通过反馈导向学习来自动选择。PSS 可借助共享库实现，并能被不同编程语言编写的软件复用，从而降低对领域知识或临时启发式的依赖，鼓励程序员探索新的优化杠杆。作者认为，这种预测服务可开启新的软件优化模式与硬件设计可能性。摘要未报告具体实验或量化结果。
To better facilitate application performance programming we propose a software optimization strategy enabled by a novel low-latency Prediction System Service (PSS). Rather than relying on nuanced domain-specific knowledge or slapdash heuristics, a system service for prediction encourages programmers to spend their time uncovering new levers for optimization rather than worrying about the details of their control. The core idea is to write optimizations that improve performance in specific cases, or under specific tunings, and leave the decision of how and when exactly to apply those optimizations to the system to learn through feedback-directed learning. Such a prediction service can be implemented in any number of ways, including as a shared library that can be easily reused by software written in different programming languages, and opens the door to both new software optimization patterns and hardware design possibilities.