Profile data is valuable for identifying program hotspots and guiding optimizations. Traditional software profiling techniques incur significant overhead and are not suitable for DBT (Dynamic Binary Translation) systems. Hardware can support profile collection through either counters or timer interrupts that permit collection of statistical samples via software. Most hardware-support profiling systems can only achieve either high profile accuracy or low overhead. In this paper, we propose a novel profile approach on DBT using hardware support technique to achieve rapidly and accurately collecting profile information with minimal runtime overhead. This approach makes use of instrumentation code and a set of profiling hardware which supports operations of updating counters. It is believed that such a software-hardware collaborative approach will serve to provide a strong foundation for optimizing DBT systems.
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