DBT path selection for holistic memory efficiency and performance

Dynamic binary translators(DBTs) provide powerful platforms for building dynamic program monitoring and adaptation tools. DBTs, however, have high memory demands because they cache translated code and auxiliary code to a software code cache and must also maintain data structures to support the code cache. The high memory demands make it difficult for memory-constrained embedded systems to take advantage of DBT-based tools. Previous research on DBT memory management focused on the translated code and auxiliary code only. However, we found that data structures are comparable to the code cache in size. We show that the translated code size, auxiliary code size and the data structure size interact in a complex manner, depending on the path selection (trace selection and link formation) strategy. Therefore, holistic memory efficiency (comprising translated code, auxiliary code and data structures) cannot be improved by focusing on the code cache only. In this paper, we use path selection for improving holistic memory efficiency which in turn impacts performance in memory-constrained environments. Although there has been previous research on path selection, such research only considered performance in memory-unconstrained environments. The challenge for holistic memory efficiency is that the path selection strategy results in complex interactions between the memory demand components. Also, individual aspects of path selection and the holistic memory efficiency may impact performance in complex ways. We explore these interactions to motivate path selection targeting holistic memory demand. We enumerate all the aspects involved in a path selection design and evaluate a comprehensive set of approaches for each aspect. Finally, we propose a path selection strategy that reduces memory demands by 20% and at the same time improves performance by 5-20% compared to an industrial-strength DBT.