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Abstract.Choosing the appropriate assignment of loop iterations to threads is one of the most important decisions that need to be taken when parallelizing Loops, the main source of parallelism in numerical applications. This is not an easy task, even for expert programmers, and it can potentially take a large amount of time. OpenMP offers the schedule clause, with a set of predefined iteration scheduling strategies, to specify how (and when) this assignment of iterations to threads is done. In some cases, the best schedule depends on architectural characteristics of the target architecture, data input, ... making the code less portable. Even worse, the best schedule can change along execution time depending on dynamic changes in the behavior of the loop or changes in the resources available in the system. Also, for certain types of imbalanced loops, the schedulers already proposed in the literature are not able to extract the maximum parallelism because they do not appropriately trade-off load balancing and data locality. This paper proposes a new scheduling strategy, that derives at run time the best scheduling policy for each parallel loop in the program, based on information gathered at runtime by the library itself.

Tasking is the most significant feature included in the new OpenMP 3.0 standard. It was introduced to handle unstructured parallelism and broaden the range of applications that can be parallelized by OpenMP. This paper presents the design and implementation of the task model in the IBM XL parallelizing compilers. The task construct is significantly different from other OpenMP constructs. This paper discusses some of the unique challenges in implementing the task construct and its associated synchronization constructs in the compiler. We also present a performance evaluation of our implementation on a set of benchmarks and applications. We identify limitations in the current implentation and propose solutions for further improvement.

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