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Impact of grid partitioning algorithms on combined distributed AC optimal power flow and parallel dynamic power grid simulation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第25期14卷 6133-6141页

作者： Kyesswa, Michael Murray, Alexander Schmurr, Philipp Cakmak, Hueseyin Kuehnapfel, Uwe Hagenmeyer, Veit Karlsruhe Inst Technol Inst Automat & Appl Informat Karlsruhe Germany

The complexity of most power grid simulation algorithms scales with the network size, which corresponds to the number of buses and branches in the grid. parallel and distributed computing is one approach that can be used to achieve improved scalability. However, the efficiency of these algorithms requires an optimal grid partitioning strategy. To obtain the requisite power grid partitionings, the authors first apply several graph theory based partitioning algorithms, such as the Karlsruhe fast flow partitioner (KaFFPa), spectral clustering, and METIS. The goal of this study is an examination and evaluation of the impact of grid partitioning on power system problems. To this end, the computational performance of AC optimal power flow (OPF) and dynamic power grid simulation are tested. The partitioned OPF-problem is solved using the augmented Lagrangian based alternating direction inexact Newton method, whose solution is the basis for the initialisation step in the partitioned dynamic simulation problem. The computational performance of the partitioned systems in the implemented parallel and distributed algorithms is tested using various IEEE standard benchmark test networks. KaFFPa not only outperforms other partitioning algorithms for the AC OPF problem, but also for dynamic power grid simulation with respect to computational speed and scalability.

关键词： Newton method optimisation load flow IEEE standards power grids graph theory pattern clustering parallel algorithms power system simulation computational complexity optimal grid partitioning strategy spectral clustering power system problems computational performance partitioned OPF-problem augmented Lagrangian based alternating direction inexact Newton method partitioned dynamic simulation problem partitioned systems parallel algorithm distributed algorithms AC OPF problem grid partitioning algorithms combined distributed AC optimal power flow parallel dynamic power grid simulation distributed computing power grid simulation algorithms power grid partitionings parallel computing graph theory Karlsruhe fast flow partitioner KaFFPa METIS IEEE standard benchmark test networks

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13th Workshop on parallel and AI-based Bioinformatics and Biomedicine (ParBio): Editorial 24

13th Workshop on Parallel and AI-based Bioinformatics and Bi...

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15th Conference on Bioinformatics Computational Biology and Health Informatics

作者： Agapito, Giuseppe Cannataro, Mario Lloyd, Wes J. Zucco, Chiara Magna Graecia Univ Catanzaro Dept Law Econ & Sociol Catanzaro Italy Magna Graecia Univ Catanzaro Dept Med & Surg Sci Catanzaro Italy Univ Washington Tacoma Sch Engn & Technol Washington DC USA

ISBN: (纸本)9798400713026

The goal of ParBio is to bring together scientists in high-performance computing, computational biology, and medicine to discuss parallel implementation of bioinformatics and biomedical applications and the challenges and opportunities of moving these applications to the cloud or edge. The workshop will also address Artificial Intelligence (AI), Large Language Models (LLMs), machine learning, and big data analytics in healthcare and bioinformatics, focusing on the integrated analysis of molecular and clinical data. This is motivated by the increasing production of experimental and clinical data and the shift towards data storage, integration, and analysis.

关键词： parallel algorithms Bioinformatics Machine Learning

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parallel Integer Sort: Theory and Practice 24

Parallel Integer Sort: Theory and Practice

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29th ACM SIGPLAN Annual Symposium on Principles and Practice of parallel Programming (PPoPP)

作者： Dong, Xiaojun Dhulipala, Laxman Gu, Yan Sun, Yihan UC Riverside Riverside CA 92521 USA Univ Maryland Baltimore MD USA

ISBN: (纸本)9798400704352

Integer sorting is a fundamental problem in computer science. This paper studies parallel integer sort both in theory and in practice. In theory, we show tighter bounds for a class of existing practical integer sort algorithms, which provides a solid theoretical foundation for their widespread usage in practice and strong performance. In practice, we design a new integer sorting algorithm, DovetailSort, that is theoreticallyefficient and has good practical performance. In particular, DovetailSort overcomes a common challenge in existing parallel integer sorting algorithms, which is the difficulty of detecting and taking advantage of duplicate keys. The key insight in DovetailSort is to combine algorithmic ideas from both integer- and comparison-sorting algorithms. In our experiments, DovetailSort achieves competitive or better performance than existing state-of-the-art parallel integer and comparison sorting algorithms on various synthetic and real-world datasets.

关键词： Integer Sort Radix Sort parallel algorithms

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The All Nearest Smaller Values Problem Revisited in Practice, parallel and External Memory 24

The All Nearest Smaller Values Problem Revisited in Practice...

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36th ACM Symposium on parallelism in algorithms and Architectures (SPAA)

作者： Sitchinava, Nodari Svenning, Rolf Univ Hawaii Manoa Honolulu HI 96822 USA Aarhus Univ Aarhus Denmark

ISBN: (纸本)9798400704161

We present a thorough investigation of the All Nearest Smaller Values (ANSV) problem from a practical perspective. The ANSV problem is defined as follows: given an array A consisting of n values, for each entry A(i) compute the largest index l < i and the smallest index r > i such that A(i) > A(l) and A(i) > A(r), i.e., the indices of the nearest smaller values to the left and to the right of A(i). The ANSV problem was solved by Berkman, Schieber, and Vishkin [J. algorithms, 1993] in the PRAM model. Their solution in the CREW PRAM model, which we will refer to as the BSV algorithm, achieves optimal O(n) work and O(log n) span. Until now, the BSV algorithm has been perceived as too complicated for practical use, and we are not aware of any publicly available implementations. Instead, the best existing practical solution to the ANSV problem is the implementation by Shun and Zhao presented at DCC'13. They implemented a simpler O(n log n)-work algorithm with an additional heuristic first proposed by Blelloch and Shun at ALENEX'11. We refer to this implementation as the BSZ algorithm. In this paper, we implement the original BSV algorithm and demonstrate its practical efficiency. Despite its perceived complexity, our results show that its performance is comparable to the BSZ algorithm. We also present the first theoretical analysis of the heuristic implemented in the BSZ algorithm and show that it provides a tunable trade-off between optimal work and optimal span. In particular, we show that it achieves O(n(1 + log n/k)) work and O(k(1 + log n/k)) span, for any integer parameter 1 <= k <= n. Thus, for k = Theta(log n), the BSZ algorithm can be made to be work-optimal, albeit at the expense of increased span compared to BSV. Our discussion includes a detailed examination of different input types, particularly highlighting that for random inputs, the low expected distance between values and their nearest smaller values renders simple algorithms efficient. Finally, we analy

关键词： Algorithm analysis parallel algorithms external memory PRAM algorithm engineering all nearest smaller values problem ANSV

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An overview of parallel processing of rectangular determinant calculation 13

An overview of parallel processing of rectangular determinan...

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13th Mediterranean Conference on Embedded Computing (MECO)

作者： Duriqi, Besnik Snopce, Halil Salihu, Armend Luma, Artan South East European Univ Fac Comp Sci Tetovo North Macedonia Fac Comp Sci Tetovo North Macedonia UNI Univ Int Coll Dept Comp Sci Prishtina Kosovo

ISBN: (纸本)9798350387568;9798350387575

This paper focuses on developing algorithms for parallel determinant processing, a crucial task in linear algebra and computational mathematics. The aim is to improve efficiency in high-performance computing environments by designing and analyzing algorithms that use parallel processing to expedite determinant computation for various matrices range. The research explores methods like Laplace expansion, LU decomposition, eigenvalue decomposition, Gaussian elimination, and cofactor expansion, assessing their efficiency, scalability, and applicability in different computational environments. The study employs advanced parallel programming techniques and architectures, utilizing multi-core processors with the focus aim into utilization of Chio's method of rectangular determinants processing in parallel etc. The research also investigates the mathematical underpinnings of parallel determinant algorithms, addressing challenges like load balancing, data distribution, and synchronization. The results show significant improvements in determinant calculations efficiency, reducing computation times for large matrices.

关键词： parallel computing determinant calculation linear algebra high-performance computing parallel algorithms

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parallel Dynamic Maximal Matching 24

Parallel Dynamic Maximal Matching

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36th ACM Symposium on parallelism in algorithms and Architectures (SPAA)

作者： Ghaffari, Mohsen Trygub, Anton MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798400704161

We present the first (randomized) parallel dynamic algorithm for maximal matching, which can process an arbitrary number of updates simultaneously. Given a batch of edge deletion or insertion updates to the graph, our parallel algorithm adjusts the maximal matching to these updates in poly(log n) depth and using poly(log n) amortized work per update. That is, the amortized work for processing a batch of k updates is k poly(log n), while all this work is done in poly(log n) depth, with high probability. This can be seen as a parallel counterpart of the sequential dynamic algorithms for constant-approximate and maximal matching [Onak and Rubinfeld STOC'10;Baswana, Gupta, and Sen FOCS'11;and Solomon FOCS'16]. Our algorithm readily generalizes to maximal matching in hypergraphs of rank r-where each hyperedge has at most r endpoints-with a poly(r) increase in work, while retaining the poly(log n) depth.

关键词： parallel algorithms dynamic algorithms maximal matching

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Distributed landmark labeling for social networks

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2025年 200卷

作者： Sener, labeling Arda Yenigun, Husnu Kaya, Kamer Sabanci Univ Fac Engn & Nat Sci Orta MahalleUniv Cd 27 TR-34956 Istanbul Turkiye

Distance queries are a fundamental part of many network analysis applications. They can be used to infer the closeness of two users in social networks, the relation between two sites in a web graph, or the importance of the interaction between two proteins or molecules. Being able to answer these queries rapidly has many benefits in the area of network analysis. Pruned Landmark Labeling (Pll) is a technique used to generate an index for a given graph that allows the shortest path queries to be completed in a fraction of the time when compared to a standard breadth-first or a depth-first search-based algorithm. parallel Shortest-distance Labeling (Psl) reorganizes the steps of Pll for the multithreaded setting and is designed particularly for social networks for which the index sizes can be much larger than what a single server can store. Even for a medium-size, 5 million vertex graph, the index size can be more than 40 GB. This paper proposes a hybrid, shared- and distributed-memory algorithm, DPSL, by partitioning the input graph via a vertex separator. The proposed method improves both the parallel execution time and the maximum memory consumption by distributing both the data and the work across multiple nodes of a cluster. For instance, on a graph with 5M vertices and 150M edges, using 4 nodes, DPSL reduces the execution time and maximum memory consumption by 2.13x and 1.87x, respectively, compared to our improved implementation of Psl.

关键词： Graphs High-performance computing parallel algorithms Distance queries

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Efficient sequential and parallel algorithms for record linkage

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2014年第2期21卷 252-262页

作者： Abdullah-Al Mamun Mi, Tian Aseltine, Robert Rajasekaran, Sanguthevar Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA Univ Connecticut Publ Hlth Res Inst E Hartford CT USA

Background and objective Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results Our sequential and parallel algorithms have been tested on a real dataset of 1083878 records and synthetic datasets ranging in size from 50000 to 9000000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm.

关键词： Data Integration Healthcare Records algorithms parallel algorithms Speedups

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parallel and (Nearly) Work-Efficient Dynamic Programming 24

Parallel and (Nearly) Work-Efficient Dynamic Programming

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36th ACM Symposium on parallelism in algorithms and Architectures (SPAA)

作者： Ding, Xiangyun Gu, Yan Sun, Yihan Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9798400704161

The idea of dynamic programming (DP), proposed by Bellman in the 1950s, is one of the most important algorithmic techniques. However, in parallel, many fundamental and sequentially simple problems become more challenging, and open to a (nearly) work-efficient solution (i.e., the work is off by at most a polylogarithmic factor over the best sequential solution). In fact, sequential DP algorithms employ many advanced optimizations such as decision monotonicity or special data structures, and achieve better work than straightforward solutions. Many such optimizations are inherently sequential, which creates extra challenges for a parallel algorithm to achieve the same work bound. The goal of this paper is to achieve (nearly) work-efficient parallel DP algorithms by parallelizing classic, highly-optimized and practical sequential algorithms. We show a general framework called the Cordon Algorithm for parallel DP algorithms, and use it to solve several classic problems. Our selection of problems includes Longest Increasing Subsequence (LIS), sparse Longest Common Subsequence (LCS), convex/concave generalized Least Weight Subsequence (LWS), Optimal Alphabetic Tree (OAT), and more. We show how the Cordon Algorithm can be used to achieve the same level of optimization as the sequential algorithms, and achieve good parallelism. Many of our algorithms are conceptually simple, and we show some experimental results as proofs-of-concept.

关键词： parallel algorithms Dynamic Programming Longest Increasing Subsequence Longest Common Subsequence Least Weighted Sum Optimal Alphabetic Tree Decision Monotonicity

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Splitting methods parallel algorithms for problems of pollution transport in atmosphere

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Journal of Automation and Information Sciences 2014年第10期46卷 58-71页

作者： Gladky, A.V. Blagoveshchenskaya, T.Yu. Bohaienko, V.A. V.M. Glushkov Institute of Cybernetics National Academy of Sciences of Ukraine Kiev Ukraine

The problem of mathematical modeling of the spread of contamination from point sources in the air has been considered. An approach that uses the idea of splitting and organization of computation with explicit difference schemes of a traveling wave has been proposed for the numerical solution of multi-dimensional convection-diffusion equations. Problems of construction of splitting difference schemes, approximation and stability by the initial data have been investigated. parallel algorithms for GPU and cluster systems have been proposed. © 2014 by Begell House Inc.

关键词： parallel algorithms

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