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An Adaptive Cut-off for Task Parallelism

Authors:
Alejandro Duran  (Universitat Politecnica de Catalunya)
Julita Corbal√°n  (Universitat Politecnica de Catalunya)
Eduard Ayguad√©  (Universitat Politecnica de Catalunya)
Papers Session
Programming Models
Wednesday,  04:00PM - 04:30PM
Room Ballroom F
Abstract:
In task parallel languages an important factor in achieving a good performance is the use of a cut-off technique that reduces the number of tasks created. This helps the runtime to reduce the overhead of task creation, particularly if the tasks are very fine grained. 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 cut-off 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.
The full paper can be found in the IEEE Xplore Digital Library and ACM Digital Library
   IEEE Computer Society  /  ACM     2 0   Y E A R S   -   U N L E A S H I N G   T H E   P O W E R   O F   H P C