A combination of factors has resulted in an industry-wide move to build multiple cores on a single chip to sustain the rate of growth in the computing power of processors. At the same time, high-level programming systems (e.g., scripting languages) have gained tremendous popularity motivated by dramatically higher programming productivity that such systems afford compared to traditional programming languages, such as C/C++ or Java. Unfortunately, these two trends combine to increase the gap between application software and the underlying hardware, which negatively impacts hardware utilization. An important reason is the critical nature of memory subsystems on the modern multi-core processors and a lack of any systematic methods to derive the memory behavior of programs written in high-level programming systems. This research aims at addressing the problem by developing a theoretical model to estimate the memory behavior of programs written in MATLAB and implementing the model in a prototype compiler.
A quantitative metric that past researchers have found useful in memory-related optimizations is reuse distance. Defined as the volume of data accessed between two successive references to a memory location, the goal is to transform programs to lower most of their reuse distances to below a threshold (usually, related to cache size). By restricting the computation of reuse distances to locations accessible at the source-level it is possible to define source-level reuse distance. The metric, even though approximate, enables crucial analysis for high-level programming systems that often benefit remarkably from source-level transformations. This research develops efficient algorithms for source-level reuse distance analysis, validates it against the actual program behavior, and implements it in a MATLAB compiler to drive memory-related optimizations, especially those related to array accesses.
