Aggregated Dynamic Dataflow Graph Generation and Visualization

Aggregated Dynamic Dataflow Graphs can assist programmers to uncover the main data paths of a given algorithm. This information can be useful when scaling a singlethreaded program into a multi-core architecture. The amount of data movements is crucial when targeting for cache incoherent and/or heterogeneous platforms. This paper presents two methods for generating function-level Aggregated Dynamic Dataflow Graphs. Instruction level trace log was used as a basis, which was generated by Microsoft Giano processor simulator platform. Top-down aggregation strategy and relational database was used to speed up the generation of different views of the aggregated dataflow and call graphs