Program slicing based on runtime dataflow measurements

Multicore architectures enable increasing the performance of the system with parallel processing. One of the challenges of a multicore embedded system is the correct usage of the processor cores. It is possible to achieve balanced processor load on the different cores, but the communication bandwidth between the cores is often a bottleneck. Passing large amounts of data between tasks mapped to different processor cores can result in cache misses in the local cache of a processor core. This paper introduces an analyzation method based on runtime generated data flow graphs to find the data paths of an algorithm. It shows that a spectral cluster analysis can help to discover data independent subsets in the algorithm under test. Finding the data independent parts helps to partition the program to multiple slices where the inter-slice communication is kept as low as possible. With our proposed method the communication bottleneck can be evaded in a multicore, multitask implementation, possibly resulting in better performance.