CSR: Small: Statistical Memory Monitoring in Hardware for Security and Performance

该项目应用统计抽样技术来检测各种应用程序的正常或所需行为的偏差，范围从相变存储器(PCM)磨损均衡和入侵检测等安全问题到多核通信、数据迁移和局部性优化等性能问题。由于各种原因，现有的性能监控和测量技术不适用于我们的应用。许多依赖于操作系统(1)可能受到危害，因此不适合与安全相关的监控，以及(2)以页面粒度跟踪数据，而内存层次结构的性能通常需要以块粒度进行监控。为了避免这些限制，本项目设计了一种对内存访问模式进行统计采样的监控体系结构。暴力监控将需要大型的、频繁搜索的硬件结构，这会增加复杂性和功率，而采样可以实现以低速率搜索的小得多的结构，从而产生更少的开销。通常，如果被监视的行为表现出较高的标准偏差，则采样会失去准确性或需要大量样本(以及大型硬件结构)。我们的主要见解是，我们可以将感兴趣区域内的行为的标准差绑定到应用程序，从而允许进行准确和低开销的采样。我们的主要智能优势是表明统计抽样和性能监控可以统一不同应用程序的目标，并能够以高精度和低开销进行监控。更广泛的影响包括为今后计算机系统中强有力的基于统计的业绩监测铺平道路，并为将统计和计算机体系结构结合起来的研究和教育努力铺平道路。

This project applies statistical sampling techniques to detect deviation from normal or desired behavior for a variety of applications, ranging from security problems such as phase-change memory (PCM) wear leveling and intrusion detection to performance problems such as multicore communication, data migration, and locality optimizations. Existing performance monitoring and measurement techniques are insufficient for our applications for a variety of reasons. Many depend upon the OS which (1) may be compromised and hence be unsuitable for security-related monitoring, and (2) tracks data at page granularity whereas memory hierarchy performance often needs monitoring at the block granularity. To avoid these limitations, this project designs a monitoring architecture for statistically sampling memory access patterns. Brute-force monitoring would require large, frequently-searched hardware structures that increase complexity and power, whereas sampling enables much smaller structures that are searched at low rates, incurring far less overhead. In general, sampling loses accuracy or requires a large number of samples (and large hardware structures) if the monitored behavior exhibits high standard deviation. Our key insight is that we can bound the standard deviation of the behavior within the region of interest to the application, thereby allowing accurate and low-overhead sampling. Our key intellectual merit is to show that statistical sampling and performance monitoring can unify the aims of disparate applications and enable monitoring with high accuracy and low overhead. The broader impacts include paving the way for robust statistics-based performance monitoring in future computer systems and for research and education efforts that combine statistics and computer architecture.