Abstract
Developing shared memory parallel programs using OpenMP is straightforward, but getting good performance in terms of speedup and scalability can be difficult. This paper demonstrates the functionality of a collector-based dynamic optimization framework called DARWIN that uses collected performance data as feedback to affect the behavior of the program through the OpenMP runtime, thus able to optimizing certain aspects. The DARWIN framework utilizes the OpenMP Collector API to drive the optimization activity and various open source libraries to support its data collection and optimizations.
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Wicaksono, B., Nanjegowda, R.C., Chapman, B. (2011). A Dynamic Optimization Framework for OpenMP. In: Chapman, B.M., Gropp, W.D., Kumaran, K., Müller, M.S. (eds) OpenMP in the Petascale Era. IWOMP 2011. Lecture Notes in Computer Science, vol 6665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21487-5_5
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DOI: https://doi.org/10.1007/978-3-642-21487-5_5
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