HPS: hybrid profiling support

摘要

Key to understanding and optimizing complex applications is our ability to dynamically monitor executing programs with low overhead and high accuracy. Toward this end, we present HPS, a hybrid profiling support system. HPS employs a hardware/software approach to program sampling that transparently, efficiently, and dynamically samples an executing instruction stream. Our system is an extension and application of dynamic instruction stream editing (DISE), a hardware technique that macro-expands instructions in the pipeline decode stage at runtime. HPS toggles profiling to sample the executing program as required by the profile consumer, e.g. a dynamic optimizer. Our system requires few hardware resources and introduces no "basic" overhead - the execution of instructions that triggers profiling. We use HPS to investigate the tradeoffs between overhead and accuracy of different profile types as well as different profiling schemes. In particular, we empirically evaluate hot data stream, hot call pair, and hot method identification using a number of parameterizations of bursty tracing, a popular sampling scheme used in dynamic optimization systems.