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IEEE Transactions on Software Engineering
Guided self-scheduling: A practical scheduling scheme for parallel supercomputers
IEEE Transactions on Computers
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Discrete & Computational Geometry - Selected papers from the fourth ACM symposium on computational geometry, Univ. of Illinois, Urbana-Champaign, June 6 8, 1988
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SCG '90 Proceedings of the sixth annual symposium on Computational geometry
On the construction of abstract Voronoi diagrams
Discrete & Computational Geometry
Computational Geometry: Theory and Applications
Factoring: a method for scheduling parallel loops
Communications of the ACM
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PLDI '92 Proceedings of the ACM SIGPLAN 1992 conference on Programming language design and implementation
Four results on randomized incremental constructions
Computational Geometry: Theory and Applications
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Handbook of discrete and computational geometry
Techniques for speculative run-time parallelization of loops
SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Trapezoid Self-Scheduling: A Practical Scheduling Scheme for Parallel Compilers
IEEE Transactions on Parallel and Distributed Systems
Toward efficient and robust software speculative parallelization on multiprocessors
Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming
The R-LRPD Test: Speculative Parallelization of Partially Parallel Loops
IPDPS '02 Proceedings of the 16th International Symposium on Parallel and Distributed Processing
Design Space Exploration of a Software Speculative Parallelization Scheme
IEEE Transactions on Parallel and Distributed Systems
ICPPW '05 Proceedings of the 2005 International Conference on Parallel Processing Workshops
A fast planar partition algorithm. I
SFCS '88 Proceedings of the 29th Annual Symposium on Foundations of Computer Science
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
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In this work, we address the problem of scheduling loops with dependences in the context of speculative parallelization. We show that the scheduling alternatives are highly influenced by the dependence violation pattern the code presents. We center our analysis in those algorithms where dependences are less likely to appear as the execution proceeds. Particularly, we focus on randomized incremental algorithms, widely used as a much more efficient solution to many problems than their deterministic counterparts. These important algorithms are, in general, hard to parallelize by hand and represent a challenge for any automatic parallelization scheme. Our analysis led us to the development of Meseta, a new scheduling strategy that takes into account the probability of a dependence violation to determine the number of iterations being scheduled. Meseta is compared with existing techniques, including Fixed-Size Chunking (FSC), the only scheduling alternative used so far in the context of speculative parallelization. Our experimental results show a 5.5 percent to 36.25 percent speedup improvement over FSC, leading to a better extraction of the parallelism inherent to randomized incremental algorithms. Moreover, when the cost of dependence violations is too high to obtain speedups, Meseta curves the performance degradation.