Exploiting Value Locality in Shared Memory Multiprocessors

价值局部性是最近发现的一种程序属性，它描述了先前看到的程序值重复出现的可能性，在已发表的文献中得到了热烈的研究。该项目研究了价值局部性利用的一个新领域，即运行商业工作负载的共享内存多处理器（SMP）系统。最近对存储值局域性研究的初步结果表明，通过识别和压缩沉默存储和随机沉默存储，存在着减少多处理器数据和地址总线流量的巨大潜力。该项目描述并评估了存储压缩技术的确切机制，研究了利用值局域性的替代方法，最后，开发了针对SMP系统中读写数据共享和同步的特定问题的集中机制。该项目表明，克服由数据共享引起的性能瓶颈需要基于值局域性的推测技术，因为其他更传统的推测执行方法肯定会失败。这项研究实现了投机技术的潜力，利用价值局域性来提高性能和/或降低设计用于运行商业工作负载的下一代共享内存多处理器系统的实现成本和复杂性。

Value locality, a recently discovered program attribute that describes the likelihood of the recurrence of previously seen program values, has been studied enthusiastically in the published literature. This project investigates a new domain for the exploitation of value locality, namely shared-memory multiprocessor (SMP) systems running commercial workloads. Initial results from a recent study of store value locality indicate that significant potential exists for reducing multiprocessor data and address bus traffic by identifying and squashing silent and stochastically silent stores. This project describes and evaluates exact mechanisms for store squashing techniques, investigates alternative approaches for exploiting value locality, and finally, develops focused mechanisms for attacking the specific problem of read/write data sharing and synchronization in SMP systems. The project demonstrates that overcoming the performance bottlenecks caused by data sharing requires speculative techniques based on value locality, since other more conventional approaches to speculative execution are guaranteed to fail. This research realizes the potential of speculative techniques that exploit value locality to improve performance and/or reduce implementation cost and complexity in future generation shared-memory multiprocessor systems that are designed to run commercial workloads.