Modern task parallel programming models provide sophisticated runtime task schedulers for handling the scheduling of logical tasks on a large and varying number of hardware parallel resources at runtime. The performance of these programming models increasingly rely on how fast their runtime schedulers do their job. The more delay a scheduler incurs in matching a ready task to a free processor core at any point in time, the more impact it causes to the program's parallel execution. We have developed a tool that is able to detect these delayed intervals caused by the scheduler in a parallel execution, and spot them specifically on two kinds of visualizations: the logical task graph captured at runtime (DAG visualizations) and time-series visualizations of threads (timelines). By further analyzing positions of these delays on those visualizations the tool could identify possible scheduling issues in the scheduler that causes these delays, yielding improvement insights for the development of task parallel programming models. From an application programmer's perspective, our tool is useful by being able to contrast differences of various task parallel programming models executing the same program, helping users choose the right model for their application. We demonstrate that usefulness by using the tool to analyze 10 applications in BOTS benchmark suite in our case studies.