A Lightweight Hybrid Hardware/Software Approach for Object-Relative Memory Profiling

Abstract—Memory profiling is the process of collecting memory address traces during the execution of a program, then analyzing and characterizing the memory behavior of the program offline. With the trend that there will be more and more cores integrated in a processor chip, the “Memory Wall ” problem will become more serious in the chip multiprocessor (CMP) system. Thus accurate and effective memory profiling is becoming one of the keys to identify the source of memory system bottlenecks. A large body of work has been contributed to memory profiling, however, most adopted instrumentation, simulator which would suffer heavy overhead, or hardware performance counter which would be lack of detail trace information. Furthermore, correlating the raw memory address traces with the object-relative information allows us to separate regular pattern for certain object from the irregular mixed, thus helps the optimization. In this paper, we propose a lightweight hybrid hardware/software approach for object-relative memory profiling. We monitor the physical memory addresses through hardware snooping with negligible overhead; meanwhile we dump Linux kernel page tables of processes, as well as object-relative memory allocation information. Our approach supports not only to collect the full memory traces of applications with detail object relative information, but also to identify hardware-generated memory accesses such as page memory walks due to TLB miss at object level. The experimental results on real system show that our approach is highly accurate (the largest error is 2.04%) and low overhead (the average overhead is 1.60%). Furthermore, we profile two multi-thread applications in detail, and successfully identity the hot TLB-miss objects. With object-targeted optimization, we can improve applications ’ performance by nearly 6.86%. Keywords- hybrid; object; memory profiling; page memory walks; full memory trace