Abstract
Memory monitoring is of critical use in understanding applications and evaluating systems. Due to the dynamic nature in programs’ memory accesses, common practice today leaves large amounts of address examination and data recording at runtime, at the cost of substantial performance overhead (and large storage time/space consumption if memory traces are collected).Recognizing the memory access patterns available at compile time and redundancy in runtime checks, we propose a novel memory access monitoring and analysis framework, Spindle. Unlike methods delaying all checks to runtime or performing task-specific optimization at compile time, Spindle performs common static analysis to identify predictable memory access patterns into a compact program structure summary. Custom memory monitoring tools can then be developed on top of Spindle, leveraging the structural information extracted to dramatically reduce the amount of instrumentation that incurs heavy runtime memory address examination or recording. We implement Spindle in the popular LLVM compiler, supporting both single-thread and multi-threaded programs. Our evaluation demonstrated the effectiveness of two Spindle-based tools, performing memory bug detection and trace collection respectively, with a variety of programs. Results show that these tools are able to aggressively prune online memory monitoring processing, fulfilling desired tasks with performance overhead significantly reduced (2:54× on average for memory bug detection and over 200× on average for access tracing, over state-of-the-art solutions).
Haojie Wang
Postdoc
Jidong Zhai
Associate Professor
(特别研究员、博士生导师)
