An adaptive cut-off for task parallelism

In task parallel languages, an important factor for achieving a good performance is the use of a cut-off technique to reduce the number of tasks created. Using a cut-off to avoid an excessive number of tasks helps the runtime system to reduce the total overhead associated with task creation, particularlt if the tasks are fine grain. Unfortunately, the best cut-off technique its usually dependent on the application structure or even the input data of the application. We propose a new cut-off technique that, using information from the application collected at runtime, decides which tasks should be pruned to improve the performance of the application. This technique does not rely on the programmer to determine the cut-off technique that is best suited for the application. We have implemented this cut-off in the context of the new OpenMP tasking model. Our evaluation, with a variety of applications, shows that our adaptive cut-off is able to make good decisions and most of the time matches the optimal cut-off that could be set by hand by a programmer.