Leveraging Managed Runtime Systems to Build, Analyze, and Optimize Memory Graphs.

Optimizing memory management is a major challenge of embedded systems programming, as memory is scarce. Further, embedded systems often have heterogeneous memory architectures, complicating the task of memory allocation during both compilation and migration. However, new opportunities for addressing these challenges have been created by the recent emergence of managed runtimes for embedded systems. By imposing structure on memory, these systems have opened the doors for new techniques for analyzing and optimizing memory usage within embedded systems. This paper presents GEM (Graphs of Embedded Memory), a tool which capitalizes on the structure that managed runtime systems provide in order to build memory graphs which facilitate memory analysis and optimization. At GEMu0027s core are a set of fundamental graph transformations which can be layered to support a wide range of use cases, including interactive memory visualization, de-duplication of objects and code, compilation for heterogeneous memory architectures, and transparent migration. Moreover, since the same underlying infrastructure supports all of these orthogonal functionalities, they can easily be applied together to complement each other.