Static memory management for efficient mobile sensing applications

Abstract

Memory management is a crucial aspect of mobile sensing applications that must process high-rate data streams in an energy-efficient manner. Our work is done in the context of synchronous data-flow models in which applications are implemented as a graph of components that exchange data at fixed and known rates over FIFO channels. In this paper, we show that it is feasible to leverage the restricted semantics of synchronous data-flow models to optimize memory management. Our memory optimization approach includes two components: (1) We use abstract interpretation to analyze the complete memory behavior of a mobile sensing application and identify data sharing opportunities across components according to the live ranges of exchanged samples. Experiments indicate that the static analysis is precise for a majority of considered stream applications whose control logic does not depend on input data. (2) We propose novel heuristics for memory allocation that leverage the graph structure of applications to optimize data exchanges between application components to achieve not only significantly lower memory footprints but also increased stream processing throughput. We incorporate code generation techniques that transform a stream program into efficient C code. The memory optimizations are implemented as a new compiler for the StreamIt programming language. Experiments show that our memory optimizations reduce memory footprint by as much as 96% while matching or improving the performance of the StreamIt compiler with cache optimizations enabled. These results suggest that highly efficient stream processing engines may be built using synchronous data-flow languages.