Estimating cache misses and locality using stack distances
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
A Portable Programming Interface for Performance Evaluation on Modern Processors
International Journal of High Performance Computing Applications
KAAPI: A thread scheduling runtime system for data flow computations on cluster of multi-processors
Proceedings of the 2007 international workshop on Parallel symbolic computation
Deque-Free Work-Optimal Parallel STL Algorithms
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Does cache sharing on modern CMP matter to the performance of contemporary multithreaded programs?
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Binary Mesh Partitioning for Cache-Efficient Visualization
IEEE Transactions on Visualization and Computer Graphics
Case study of multithreaded in-core isosurface extraction algorithms
EG PGV'04 Proceedings of the 5th Eurographics conference on Parallel Graphics and Visualization
LIBKOMP, an efficient openMP runtime system for both fork-join and data flow paradigms
IWOMP'12 Proceedings of the 8th international conference on OpenMP in a Heterogeneous World
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Reordering instructions and data layout can bring significant performance improvement for memory bounded applications. Parallelizing such applications requires a careful design of the algorithm in order to keep the locality of the sequential execution. In this paper, we aim at finding a good parallelization of memory bounded applications on multicore that preserves the advantage of a shared cache. We focus on sequential applications with iteration through a sequence of memory references. Our solution relies on a work stealing scheduler combined with a dynamic sliding window that constrains cores sharing the same cache to process data close in memory. This parallel algorithm induces the same number of cache misses as the sequential algorithm at the expense of an increased number of synchronizations. Experiments with a memory bounded application confirm that core collaboration for shared cache access can bring significant performance improvements despite the incurred synchronization costs.