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About 10,000 papers for “parallel algorithms”

Adaptive parallel aggregation algorithms

Ambuj Shatdal, Jeffrey F. Naughton · ACM SIGMOD Record · 1995

Aggregation and duplicate removal are common in SQL queries. However, in the parallel query processing literature, aggregate processing has received surprisingly little attention; furthermore, for each of the traditional parallel aggregation algorithms, there is a range of grouping selectivities where the algorithm performs poorly. In this work, we propose new algorithms that dynamically adapt, at query evaluation time, in response to observed grouping selectivities. Performance analysis via analytical modeling and an implementation on a workstation-cluster shows that the proposed algorithms are able to perform well for all grouping selectivities. Finally, we study the effect of data skew and show that for certain data sets the proposed algorithms can even outperform the best of traditional approaches.

Cited by 20Open access

FAST PARALLEL PERMUTATION ALGORITHMS

Torben Hagerup, Jörg Keller · Parallel Processing Letters · 1995

We investigate the problem of permuting n data items on an EREW PRAM with p processors using little additional storage. We present a simple algorithm with run time O((n/p) log n) and an improved algorithm with run time O(n/p + log n log log (n/p)). Both algorithms require n additional global bits and O(1) local storage per processor. If prefix summation is supported at the instruction level, the run time of the improved algorithm is O(n/p). The algorithms can be used to rehash the address space of a PRAM emulation.

Cited by 1Open access

Parallel algorithms and architectures

Cosnard, Michel, P. Quinton, Yves Robert et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 1986

International audience

Cited by 0

Parallel Asynchronous Memetic Algorithms

Richard Bradwell, Ken Brown · 2007

This paper discusses our recent research combining a GA (Goldberg 1989) with Tabu search (Glover 1993) in an asynchronous manner, which also extends the idea of Memetic Algorithms (Moscato 1989). Our application domain is the job-shop scheduling problem (Baker 1974). 2 The Job-Shop Scheduling Problem

Cited by 0

Parallel Algorithms for Bioinformatics

Shahid H. Bokhari, J.R. Sauer · 2005

This chapter contains sections titled: Introduction Parallel Computer Architecture Bioinformatics Algorithms on the Cray MTA System Summary References

Cited by 6

Practical parallel hypergraph algorithms

Julian Shun · 2020

While there has been significant work on parallel graph processing, there has been very surprisingly little work on high-performance hypergraph processing. This paper presents a collection of efficient parallel algorithms for hypergraph processing, including algorithms for betweenness centrality, maximal independent set, k-core decomposition, hypertrees, hyperpaths, connected components, PageRank, and single-source shortest paths. For these problems, we either provide new parallel algorithms or more efficient implementations than prior work. Furthermore, our algorithms are theoretically-efficient in terms of work and depth. To implement our algorithms, we extend the Ligra graph processing framework to support hypergraphs, and our implementations benefit from graph optimizations including switching between sparse and dense traversals based on the frontier size, edge-aware parallelization, using buckets to prioritize processing of vertices, and compression. Our experiments on a 72-core machine and show that our algorithms obtain excellent parallel speedups, and are significantly faster than algorithms in existing hypergraph processing frameworks.

Cited by 43Open access

Parallel Ant Colony Algorithms

Stefan Janson, Daniel Merkle, Martin Middendorf · 2005

This chapter contains sections titled: Introduction Ant Colony Optimization Parallel ACO Hardware Parallelization of ACO Other Ant Colony Approaches References

Cited by 31

Algorithms for parallel rendering

Michael Cox · 1995

This dissertation investigates which parallel algorithms and architectures are the best candidates likely to enable rates for renderers that support such features as high-level geometric primitives, tesselation for accurate representation of curved surfaces, and anti-aliasing by oversampling. Parallel algorithms are distinguished by where in the graphics pipeline they redistribute data in order to sort from object-space to image-space: very early in the pipeline (sort-first), after the natural translation in the pipeline to image-space (sort-middle), and after pixels are calculated (sort-last). The main modeling results give a tradeoff between sort-first, sort-middle, and sort-last. In particular, sort-first renderers that support high-level primitives and extensive oversampling are favored by low redistribution costs. However, sort-last renderers that support extensive oversampling require very substantial network bandwidth. Modeling shows that sort-middle is intermediate between sort-first and sort-last with respect to costs and benefits. To determine whether these results apply in practice, I have modified RenderMan, a commercial package, to support sort-first and sort-last. It is found that sort-first redistribution costs are indeed very low, but that in contrast to previously published results, redundancy is an impediment to scalability. Load imbalance, however, is the more significant cause of inefficiency for moderately-sized machines. For sort-last, redistribution costs are indeed a problem if rates are to be achieved. In addition, the particular renderer studied is extremely vulnerable to synchronization costs when implemented as sort-last; however, this is a limitation of the underlying uniprocessor algorithm rather than a limitation of sort-last. Thus, sort-first is a good candidate for except for load imbalance, sort-last except for redistribution costs. Combining theoretical and empirical findings, I propose and implement a hybrid algorithm that employs sort-last to balance load in sort-first. This hybrid is found to be effective at reducing load imbalance. Addressing sort-last redistribution costs, I propose and study two techniques to reduce network bandwidth requirements: the inclusion of local z-buffers, and the use of snooping on a shared bus in order to reduce the number of pixels that must be redistributed. These techniques are investigated by modeling and trace-driven simulations; it is shown that the first is effective at reducing network bandwidth requirements, the second is extremely effective. This dissertation concludes with an identification of further problems that must be solved if real-time realistic rendering is to be realized. (Abstract shortened by UMI.)

Cited by 22

Parallel Data Mining Algorithms

Xiaochun Wang · Nova Science Publishers, Inc. eBooks · 2026

In the big data era, many modern data mining problems cannot be solved efficiently by the traditional algorithms, (i.e., specifically intractable by a single-processor computing system, either spatially prohibitive, temporally prohibitive or both). Obtaining optimal solutions in a tolerable amount of time, parallel data mining algorithms can be chosen as a suitable tool to solve the aforesaid problems. However, most up-to-date books on parallel and distributed computing techniques, such as Hadoop and Spark, are not wholly on the data mining subjects, covering only a portion of the materials in one or a few chapters and touching on the subjects but without going into it deeply, and thus incomplete and lacking systematicity, specialty and comprehensiveness. Parallel Data Mining Algorithms uniquely combines systematicity and comprehensiveness, trying not only to present as many data mining algorithms developed throughout till now as possible but also to provide their parallel solutions developed by the research and industry community currently. Instead of scratching the surface, it covers a broad range of data mining algorithms in depth, provides a comprehensive coverage of the state of the arts and advances in parallel data mining algorithm research (covering such topics as the parallel algorithm design in general and the implementations by the divide-and-conquer scheme, the MapReduce programming model and the Resilient Distributed Dataset (RDD) in specific) and makes their design and analysis accessible to all levels of readers with self-contained chapters and algorithms in pseudocode and with updated notes and bibliography to reflect developments in the field, in the hope that it will become an introduction-level parallel data mining algorithm textbook in universities as well as the standard reference for professionals.

Cited by 0

Parallel Algorithms and Patterns

Robert Robey · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2016

This is a powerpoint presentation on parallel algorithms and patterns. A parallel algorithm is a well-defined, step-by-step computational procedure that emphasizes concurrency to solve a problem. Examples of problems include: Sorting, searching, optimization, matrix operations. A parallel pattern is a computational step in a sequence of independent, potentially concurrent operations that occurs in diverse scenarios with some frequency. Examples are: Reductions, prefix scans, ghost cell updates. We only touch on parallel patterns in this presentation. It really deserves its own detailed discussion which Gabe Rockefeller would like to develop.

Cited by 0Open access