MESETA: A New Scheduling Strategy for Speculative Parallelization of Randomized Incremental Algorithms

Llanos, Diego R.; Orden, David; Palop, Belén

doi:10.1109/icppw.2005.49

Cited by 12 publications

(9 citation statements)

References 23 publications

(29 reference statements)

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“…In particular, for Randomized Incremental Algorithms, where dependences tend to accumulate in the first iterations of the loop, two methods have been shown to improve performance. The first one, called Meseta [16], divides the execution in three stages. In the first one, chunks of increasing sizes are scheduled, aiming to compensate for possible dependence violations, until a lower bound of the probability of finding a dependence is reached.…”

Section: Related Workmentioning

confidence: 99%

Moody Scheduling for Speculative Parallelization

Estebanez

Llanos

Orden

et al. 2015

Lecture Notes in Computer Science

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Scheduling is one of the factors that most directly affect performance in Thread-Level Speculation (TLS). Since loops may present dependences that cannot be predicted before runtime, finding a good chunk size is not a simple task. The most used mechanism, Fixed-Size Chunking (FSC), requires many "dry-runs" to set the optimal chunk size. If the loop does not present dependence violations at runtime, scheduling only needs to deal with load balancing issues. For loops where the general pattern of dependences is known, as is the case with Randomized Incremental Algorithms, specialized mechanisms have been designed to maximize performance. To make TLS available to a wider community, a general scheduling algorithm that does not require a-priori knowledge of the expected pattern of dependences nor previous dry-runs to adjust any parameter is needed. In this paper, we present an algorithm that estimates at runtime the best size of the next chunk to be scheduled. This algorithm takes advantage of our previous knowledge in the design and test of other scheduling mechanisms, and it has a solid mathematical basis. The result is a method that, using information of the execution of the previous chunks, decides the size of the next chunk to be scheduled. Our experimental results show that the use of the proposed scheduling function compares or even increases the performance that can be obtained by FSC, greatly reducing the need of a a costly and careful search for the best fixed chunk size.

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Section: Related Workmentioning

confidence: 99%

Moody Scheduling for Speculative Parallelization

Estebanez

Llanos

Orden

et al. 2015

Lecture Notes in Computer Science

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“…SPAA '16, July 11-13, 2016 26,18,46,5,33,15,50,59]. In theory, however, after 25 years, there are still no known bounds for parallel Delaunay triangulation using the incremental approach, nor for many other problems.…”

Section: Introductionmentioning

confidence: 99%

Parallelism in Randomized Incremental Algorithms

Blelloch

Shun

et al. 2016

Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures

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In this paper we show that many sequential randomized incremental algorithms are in fact parallel. We consider several random incremental algorithms including algorithms for comparison sorting and Delaunay triangulation; linear programming, closest pair, and smallest enclosing disk in constant dimensions; as well as least-element lists and strongly connected components on graphs. We analyze the dependence between iterations in an algorithm, and show that the dependence structure is shallow for all of the algorithms, implying high parallelism. We identify three types of dependences found in the algorithms studied and present a framework for analyzing each type of algorithm. Using the framework gives work-efficient polylogarithmic-depth parallel algorithms for most of the problems that we study. Some of these algorithms are straightforward (e.g., sorting and linear programming), while others are more novel and require more effort to obtain the desired bounds (e.g., Delaunay triangulation and strongly connected components). The most surprising of these results is for planar Delaunay triangulation for which the incremental approach is by far the most commonly used in practice, but for which it was not previously known whether it is theoretically efficient in parallel.

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“…We first show that the scheduling alternatives are highly influenced by the dependence violation pattern presented by the code. Then, we propose a new scheduling alternative, MESETA [11], for those algorithms where dependences are less likely to appear as the execution proceeds. Many incremental algorithms follow this pattern and, among them, randomized incremental algorithms have been very well studied and proven to achieve the best performance.…”

Section: Introductionmentioning

confidence: 99%

New Scheduling Strategies for Randomized Incremental Algorithms in the Context of Speculative Parallelization

Llanos

Orden

Palop

2007

IEEE Trans. Comput.

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Abstract-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.Index Terms-Parallelism and concurrency, load balancing and task assignment, scheduling and task partitioning, geometrical problems and computations.

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MESETA: A New Scheduling Strategy for Speculative Parallelization of Randomized Incremental Algorithms

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Cited by 12 publications

References 23 publications

Moody Scheduling for Speculative Parallelization

Moody Scheduling for Speculative Parallelization

Parallelism in Randomized Incremental Algorithms

New Scheduling Strategies for Randomized Incremental Algorithms in the Context of Speculative Parallelization

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