Dynamic thread mapping based on machine learning for transactional memory applications
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
Dynamic thread mapping of shared memory applications by exploiting cache coherence protocols
Journal of Parallel and Distributed Computing
Automatic Skeleton-Driven Memory Affinity for Transactional Worklist Applications
International Journal of Parallel Programming
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Process placement is a technique widely used on parallel machines with heterogeneous interconnects to reduce the overall communication time. For instance, two processes which communicate frequently are mapped close to each other. Finding the optimal mapping between threads and cores in a shared-memory environment (for example, OpenMP and Pthreads) is an even more complex task due to implicit communication. In this work, we examine data sharing patterns between threads in different workloads and use those patterns in a similar way as messages are used to map processes in cluster computers. We evaluated our technique on a state-of-the-art multicore processor and achieved moderate improvements in the common case and considerable improvements in some cases, reducing execution time by up to 45%.