Compiler-Directed Data Locality Optimization in MATLAB

Array programming languages, such as MATLAB, are often used for algorithm development by scientists and engineers without taking into consideration implementation related issues and with limited emphasis on relevant optimizations. Application code optimization, especially in terms of data storage and transfer behavior, is still an important issue and heavily affects implementations' quality in terms of performance, power consumption etc. Efficient approaches for the optimization of high level application code are required to derive high quality implementations while still reducing development time and cost. This paper presents MemAssist, a software tool supporting application developers in detecting parts of the application code in MATLAB that do not exploit efficiently the targeted processor architecture and especially the memory hierarchy. Furthermore, the proposed tool guides application developers in applying code transformations in MATLAB for the optimization of the algorithm's temporal data locality. An image processing algorithm has been optimized using MemAssist as a practical usage scenario. Experimental results prove that the use of MemAssist can heavily reduce cache misses (up to 40%) and improve execution time (up to 30% speedup) on two different processor architectures. Thus, MemAssist can be used for optimized application code development that can lead to efficient implementations while still reducing development time and cost.