Abstract
Application analysis and simulation tools are used extensively by embedded system designers to improve existing optimization techniques or develop new ones. We propose the Alleria framework to make it easier for designers to comprehensively collect critical information such as virtual and physical memory addresses, accessed values, and thread schedules about one or more target applications. Such profilers often incur substantial performance overheads that are orders of magnitude larger than native execution time. We discuss how that overhead can be significantly reduced using a novel profiling mechanism called adaptive profiling. We develop a heuristic-based adaptive profiling mechanism and evaluate its performance using single-threaded and multi-threaded applications. The proposed technique can improve profiling throughput by up to 145% and by 37% on an average, enabling Alleria to be used to comprehensively profile applications with a throughput of over 3 million instructions per second.
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