Superlinear speedup of an efficient sequential algorithm is not possible

Faber, Vance; Lubeck, O.; White, Andrew G.

doi:10.1016/0167-8191(86)90024-4

Cited by 35 publications

(13 citation statements)

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“…Speedup of a parallel computation is defined as Sp = T/Tp [33], where T is the sequential time of a problem and Tp is the parallel time to solve the same problem using p processors. Tp was argued to be no greater than P in [34]. However, in practice, people observed "super linear speedup" i.e.…”

Section: Parallel Computing and Cellular Genetic Algorithmsmentioning

confidence: 96%

Parallel Meta-heuristic Approaches to the Course Timetabling Problem

Soria-Alcaraz

Carpío

Soberanes

et al. 2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

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The Course Timetabling problem is one of the most difficult combinatorial problems that arises with a University. The main objective of this problem is to obtain a timetable that minimises student conflicts between assigned activities. This is a discrete combinatorial problem that can be extremely difficult to solve for a human expert so computational heuristics are usually implemented in order to find good solutions within a reasonable time. With the advent of multi-core and hyperthreading technologies, parallel heuristics can speed up the solution process and with a proper parallel design these heuristics can improve the quality of solutions with the same number of Fitness evaluations than sequential algorithms. This paper explores the implementation of a parallel set of heuristic algorithms based on genetic algorithms, Scatter Search and discrete PSO for CTTP problem. Our experiments used as benchmark set instances from ITC2007 Track 2. Also the algorithms described in this paper make use of a layer of independence called methodology of design in order to be easily adaptable to new instances. Every parallel algorithm is compared against its sequential counterpart through speed-up metrics like Weak speed-up proposed by Alba et al.

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Section: Parallel Computing and Cellular Genetic Algorithmsmentioning

confidence: 96%

Parallel Meta-heuristic Approaches to the Course Timetabling Problem

Soria-Alcaraz

Carpío

Soberanes

et al. 2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

View full text Add to dashboard Cite

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“…This relationship is derived from a simple model of parallel processing whose assumptions include: independence of parallel tasks, a negligible amount of overhead time incurred in initiating the parallel tasks, and negligible amounts of time for data organization and interprocessor communication and synchronization. Ideally then, if a code were executed completely in parallel cf= l), we would get a speedup of P; however, in practice this is rarely possible (Faber et al, 1986).…”

Section: Introductionmentioning

confidence: 99%

Parallel processing strategies for chemical process flowsheeting

Vegeais¹,

Stadtherr²

1992

AIChE Journal

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“…Super-linear speedup is an important matter in parallel computing and proving its existence would benefit computer science, since parallel machines would be literally more than the sum of their parts (Gustafson's conclusion in [50]). Smith [110] and Faber et al [35] state that it is not possible to achieve super-linear speedup and if such a parallel algorithm existed, then a single-core computation of the same algorithm would be no less than p times slower (leading to linear speedup again). On the opposite side, Parkinson's work [99] on parallel efficiency proposes that super-linear speedup is sometimes possible because the single processor has loop overhead.…”

Section: Speedupmentioning

confidence: 99%

A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures

Navarro

Hitschfeld

Mateu

2014

Commun. comput. phys.

152

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Abstract. Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions. The evolution of computer architectures (multi-core and many-core) towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm. In the last decade, the graphics processing unit, or GPU, has gained an important place in the field of high performance computing (HPC) because of its low cost and massive parallel processing power. Super-computing has become, for the first time, available to anyone at the price of a desktop computer. In this paper, we survey the concept of parallel computing and especially GPU computing. Achieving efficient parallel algorithms for the GPU is not a trivial task, there are several technical restrictions that must be satisfied in order to achieve the expected performance. Some of these limitations are consequences of the underlying architecture of the GPU and the theoretical models behind it. Our goal is to present a set of theoretical and technical concepts that are often required to understand the GPU and its massive parallelism model. In particular, we show how this new technology can help the field of computational physics, especially when the problem is data-parallel. We present four examples of computational physics problems; n-body, collision detection, Potts model and cellular automata simulations. These examples well represent the kind of problems that are suitable for GPU computing. By understanding the GPU architecture and its massive parallelism programming model, one can overcome many of the technical limitations found along the way, design better GPU-based algorithms for computational physics problems and achieve speedups that can reach up to two orders of magnitude when compared to sequential implementations.

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Superlinear speedup of an efficient sequential algorithm is not possible

Cited by 35 publications

References 0 publications

Parallel Meta-heuristic Approaches to the Course Timetabling Problem

Parallel Meta-heuristic Approaches to the Course Timetabling Problem

Parallel processing strategies for chemical process flowsheeting

A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures

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