Binary analysis for measurement and attribution of program performance

Modern programs frequently employ sophisticated modular designs. As a result, performance problems cannot be identified from costs attributed to routines in isolation; understanding code performance requires information about a routine’s calling context. Existing performance tools fall short in this respect. Prior strategies for attributing context-sensitive performance at the source level either compromise measurement accuracy, remain too close to the binary, or require custom compilers. To understand the performance of fully optimized modular code, we developed two novel binary analysis techniques: 1) on-the-fly analysis of optimized machine code to enable minimally intrusive and accurate attribution of costs to dynamic calling contexts; and 2) post-mortem analysis of optimized machine code and its debugging sections to recover its program structure and reconstruct a mapping back to its source code. By combining the recovered static program structure with dynamic calling context...

• Type: Conference
• Year: 2009
• Where: PLDI
• Authors: Nathan R. Tallent, John M. Mellor-Crummey, Michael W. Fagan
• Keywords: Calling Contexts, Performance, Performance Tools, PLDI 2009, Programming Languages