HMTT: a platform independent full-system memory trace monitoring system

• Authors: Yungang Bao;Mingyu Chen;Yuan Ruan;Li Liu;Jianping Fan;Qingbo Yuan;Bo Song;Jianwei Xu
• Venue: SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
• Year: 2008

Memory trace analysis is an important technology for architecture research, system software (i.e., OS, compiler) optimization, and application performance improvements. Many approaches have been used to track memory trace, such as simulation, binary instrumentation and hardware snooping. However, they usually have limitations of time, accuracy and capacity. In this paper we propose a platform independent memory trace monitoring system, which is able to track virtual memory reference trace of full systems (including OS, VMMs, libraries, and applications). The system adopts a DIMM-snooping mechanism that uses hardware boards plugged in DIMM slots to snoop. There are several advantages in this approach, such as fast, complete, undistorted, and portable. Three key techniques are proposed to address the system design challenges with this mechanism: (1) To keep up with memory speeds, the DDR protocol state machine is simplified, and large FIFOs are added between the state machine and the trace transmitting logic to handle burst memory accesses; (2) To reconstruct physical-tovirtual mapping and distinguish one process' address space from others, an OS kernel module, which collects page table information, and a synchronization mechanism, which synchronizes the page table information with the memory race, are developed; (3) To dump massive trace data, we employ a straightforward method to compress the trace and use Gigabit Ethernet and RAID to send and receive the compressed trace. We present our implementation of an initial monitoring system, named HMTT (Hyper Memory Trace Tracker). Using HMTT, we have observed that burst bandwidth utilization is much larger than average bandwidth utilization, by up to 5X in desktop applications. We have also confirmed that the stream memory accesses of many applications contribute even more than 40% of L2 Cache misses and OS virtual memory management may decrease stream accesses in view of memory controller (or L2 Cache), by up to 30.2%. Moreover, we have evaluated OS impact on memory performance in real systems. The evaluations and case studies show the feasibility and effectiveness of our proposed monitoring mechanism and techniques.