DAGViz: a DAG visualization tool for analyzing task-parallel program traces

In task-based parallel programming, programmers can expose logical parallelism of their programs by creating fine-grained tasks at arbitrary places in their code. All other burdens in the parallel execution of these tasks such as thread management, task scheduling, and load balancing are handled automatically by runtime systems. This kind of parallel programming model has been conceived as a promising paradigm that brings intricate parallel programming techniques to a larger audience of programmers because of its high programmability. There have been many languages (e.g., OpenMP, Cilk Plus) and libraries (e.g, Intel TBB, Qthreads, MassiveThreads) supporting task parallelism. However, the nondeterministic nature of task parallel execution which hides runtime scheduling mechanisms from programmers has made it difficult for programmers to understand the cause of suboptimal performance of their programs. As an effort to tackle this problem, and also to clarify differences between task parallel runtime systems, we have developed a toolset that captures and visualizes the trace of an execution of a task parallel program in the form of a directed acyclic graph (DAG). A computation DAG of a task parallel program's run is extracted automatically by our lightweight portable wrapper around all five systems which incurs no intervention into the target systems' code. The DAG is stored in a file and then visualized to analyze performance. We leverage the hierarchical structure of the DAG to enhance the DAG file format and DAG visualization, and make them manageable even with a huge DAG of arbitrarily large numbers of nodes. This DAG visualization provides a task-centric view of the program, which is different from other popular visualizations such as thread-centric timeline visualization and code-centric hotspots analysis. Besides, DAGViz also provides an additional timeline visualization which is constructed by individual nodes of the DAG, and is useful in coordinating user attention to low-parallelism areas on the DAG. We demonstrate usefulness of our DAG visualizations in some case studies. We expect to build other kinds of effective visualizations based on this computation DAG in future work, and make DAGViz an effective tool supporting the process of analyzing task parallel performance and developing scheduling algorithms for task parallel runtime schedulers.