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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Tianshu Yang Daniel Kuhn Gabriela Hug Risk Analytics and Optimization Laboratory EPF Lausanne Lausanne Switzerland EEH - Power Systems Laboratory ETH Zürich Zürich Switzerland

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

Ensuring reliable operation in power grids given the complexity of modern electricity networks with increasing demands, varying generation and flows present significant computational challenges. Addressing these challenges, we develop efficient methods for solving multi-period optimal power flow (MPOPF) problems. The class of MPOPF problems is NP-hard due to the non-convexity of power flow equations and due to the temporal coupling of the decision variables introduced by ramp constraints. To comprehensively address these challenges, we extend a conic relaxation, originally developed for single-period optimal power flow problems, to convexify the MPOPF problem, and develop a customized alternating direction method of multipliers to solve it in parallel. The proposed method is guaranteed to converge and provides a tighter lower bound than standard second-order cone relaxations. We assess the performance of our method on standard systems.

关键词： Europe Performance gain Power system reliability Computational efficiency Complexity theory Smart grids Reliability parallel algorithms Standards Load flow

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Efficient Tree-based parallel algorithms for N-Body Simulations Using C++ Standard parallelism

Efficient Tree-based Parallel Algorithms for N-Body Simulati...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Thomas Lane Cassell Tom Deakin Aksel Alpay Vincent Heuveline Gonzalo Brito Gadeschi School of Computer Science University of Bristol Bristol UK Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany GPU Architecture NVIDIA Munich Germany

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

The Barnes-Hut approximation for N-body simulations reduces the time complexity of the naive all-pairs approach from O(N 2 ) to O(N log N) by hierarchically aggregating nearby particles into single entities using a tree data structure. This inherently irregular algorithm poses substantial challenges for performance portable implementations on multi-core CPUs and GPUs. We introduce two portable fully-parallel Barnes-Hut implementation strategies that trade-off different levels of GPU support for performance: an unbalanced concurrent octree, and a balanced bounding volume hierarchy sorted by a Hilbert spacefilling curve. We implement these algorithms in portable ISO C++ using parallel algorithms and concurrency primitives like atomics. The results demonstrate competitive performance on a range of CPUs and GPUs. Additionally, they highlight the effectiveness of the par execution policy for highly concurrent irregular algorithms, outperforming par_unseq on CPUs and GPUs with Independent Thread Scheduling.

关键词： Performance evaluation ISO Standards Software algorithms Octrees Graphics processing units C++ languages Scheduling Hardware parallel algorithms Standards

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Fast Distributed Memory parallel algorithms for Finding Connected Components in Large Graphs

Fast Distributed Memory Parallel Algorithms for Finding Conn...

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IEEE International Conference on Big Data

作者： Maleq Khan Sharon Boddu Department of Electrical Engineering and Computer Science Texas A&M University-Kingsville

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Finding the connected components in a graph is a fundamental problem in graph theory and network science. A connected component in a graph is a maximal set of vertices such that there is a path between any two vertices in the component. In a serial setting, connected components can be efficiently computed by running a breadth-first search (BFS). However, BFS-based algorithms do not lead to good parallelization. At the early stage, parallel algorithms for the connected component problem were developed for the PRAM model and shared-memory systems. With the availability of big data and big graphs and the development of a new area called network science, there is a renewed interest in developing parallel algorithms for this problem. In recent years, a number of distributed-memory parallel algorithms have been developed. These algorithms are based on mainly Shiloach and Vishkin’s PRAM algorithm, called the SV algorithm, or some variants of SV. In this paper, we present a PRAM algorithm, called SELP, which is elegant and significantly simpler than the SV algorithm. Based on SELP, we develop a distributed-memory parallel algorithm, called DSELP. In addition, we incorporate some novel techniques to minimize communications among the processors. Experimental results show that our algorithms outperform the recent state-of-the-art distributed-memory algorithms significantly on a wide variety of graphs, and scale very well to a large number of processors.

关键词： Program processors Scalability Computational modeling Memory management Clustering algorithms Optimization methods Big Data Phase change random access memory Graph theory parallel algorithms

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Shared-Memory parallel algorithms for Community Detection in Dynamic Graphs

Shared-Memory Parallel Algorithms for Community Detection in...

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IEEE International Symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Subhajit Sahu Kishore Kothapalli Dip Sankar Banerjee International Institute of Information Technology Hyderabad India Hyderabad India Indian Institute of Technology Jodhpur India Karwar Rajasthan India

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Community detection is the problem of identifying natural divisions in networks. A relevant challenge in this problem is to find communities on rapidly evolving graphs. In this paper, we present our parallel Dynamic Frontier (DF) approach. Given a batch update of edge deletions or insertions, this approach incrementally identifies an approximate set of affected vertices in the graph with minimal overhead. We apply this approach to both Louvain, a high quality, and Label Propagation Algorithm (LPA), a fast static community detection algorithm. Our approach achieves a mean speedup of 7.3 × and 6.7 ×, when applied to Louvain and LPA respectively, compared to our parallel and optimized implementation of Δ-screening, a recently proposed state-of-the-art approach. Finally, we show how to combine Louvain and LPA with the DF approach to arrive at a hybrid algorithm. This algorithm produces high-quality communities while providing a speedup of 2.0 x on top of DF -based Louvain.

关键词： Distributed processing Heuristic algorithms Image edge detection Conferences Approximation algorithms Detection algorithms parallel algorithms

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Efficient parallel algorithms For Exact SimRank Computations

Efficient Parallel Algorithms For Exact SimRank Computations

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Aditya Mundhara Prajjwal Nijhara Dip Sankar Banerjee Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

SimRank is a popular measure for evaluating node similarities in graphs, but its high computational cost limits scalability for large graphs. The ExactSim [1] algorithm achieves precise single-source SimRank similarities but suffers from significant computation time. In this work, we parallelize key components of ExactSim — Personalized PageRank (PPR) computations and SimRank calculations on a CPU-based shared memory multi-core system. By optimizing these modules, we significantly reduce runtime, making ExactSim more practical for large-scale applications. Our results (on graph sizes ranging from 0.1 million edges to 100 million edges) demonstrate the effectiveness of parallelization in improving both efficiency and scalability.

关键词： Runtime Scalability High performance computing Conferences Distance measurement Computational efficiency parallel algorithms

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Enhancing Large Scale Brain Simulation with Optimized parallel algorithms on Fugaku Supercomputer

Enhancing Large Scale Brain Simulation with Optimized Parall...

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IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Tianxiang Lyu Mitsuhisa Sato Shigeki Aoki Ryutaro Himeno Zhe Sun Graduate School of Medicine Juntendo University Japan Faculty of Health Data Science Graduate School of Medicine Juntendo University RIKEN Center of Computational Science Japan Faculty of Health Data Science Graduate School of Medicine Juntendo University Japan Faculty of Health Data Science Graduate School of Medicine Juntendo University RIKEN Center for Advanced Photonics Japan

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

The quest to understand the brain has progressed from experimental and theoretical phases to the burgeoning field of simulation neuroscience [1]. Driven by big data generated at multiple levels of brain organization, simulation neuroscience seems to be the only methodology for systematically investigation on multi-scale brain and the interactions within and across all these levels. However, simulating the whole human brain is one of the most ambitious scientific challenges in the 21st century [2], impeded by issues of scale and complexity. Current spiking neural networks based brain simulator, like the NEST simulator [3], face several operational challenges on high-performance computing systems, including low computing intensity, high memory consumption and so on. Addressing these challenges, we introduce an innovative framework optimized for the Fugaku computing system, demonstrating enhanced performance compared to the NEST simulator

关键词： Neuroscience Conferences High performance computing Memory management Spiking neural networks Organizations Supercomputers Complexity theory parallel algorithms Faces

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parallel Greedy algorithms for Steiner Forest

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2025年第6期36卷 1311-1325页

作者： Ghalami, Laleh Grosu, Daniel Wayne State Univ Detroit MI 48202 USA

The Steiner Forest Problem is a fundamental combinatorial optimization problem in operations research and computer science. Given an undirected graph with non-negative weights for edges and a set of pairs of vertices called terminals, the Steiner Forest Problem is to find the minimum cost subgraph that connects each of the terminal pairs together. We design a family of parallel greedy algorithms based on a sequential heuristic greedy algorithm called Paired Greedy, which iteratively connects the terminal pairs that have the minimum distance. The family of parallel algorithms consists of a set of algorithms exhibiting various degrees of parallelism determined by the number of pairs that are connected in parallel in each iteration of the algorithms. We implement and run the algorithms on a multi-core system and perform an extensive experimental analysis. We analyzed the performance of the algorithms on a rich library of Steiner Forest instances with various underlying graph types. The results show that our proposed parallel algorithms achieve significant speedup with respect to the sequential Paired Greedy algorithm and provide solutions with costs that are very close to those of the solutions obtained by the sequential Paired Greedy algorithm. We provide recommendation on selecting the type of parallel algorithm and its parameters in order to achieve the most efficient results for each class of instances.

关键词： Forestry Greedy algorithms Costs parallel algorithms Steiner trees Approximation algorithms Training Partitioning algorithms Optimization Libraries Steiner forest parallel algorithms multi-core

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Distributed-Memory parallel algorithms for Sparse Times Tall-Skinny-Dense Matrix Multiplication 21

Distributed-Memory Parallel Algorithms for Sparse Times Tall...

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35th ACM International Conference on Supercomputing (ICS)

作者： Selvitopi, Oguz Brock, Benjamin Nisa, Israt Tripathy, Alok Yelick, Katherine Buluc, Aydin Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450383356

Sparse times dense matrix multiplication (SpMM) finds its applications in well-established fields such as computational linear algebra as well as emerging fields such as graph neural networks. In this study, we evaluate the performance of various techniques for performing SpMM as a distributed computation across many nodes by focusing on GPU accelerators. We examine how the actual local computational performance of state-of-the-art SpMM implementations affect computational efficiency as dimensions change when we scale to large numbers of nodes, which proves to be an unexpectedly important bottleneck. We consider various distribution strategies, including A-Stationary, B-Stationary, and C-Stationary algorithms, 1.5D and 2D algorithms, and RDMA-based and bulk synchronous methods of data transfer. Our results show that the best choice of algorithm and implementation technique depends not only on the cost of communication for particular matrix sizes and dimensions, but also on the performance of local SpMM operations. Our evaluations reveal that with the involvement of GPU accelerators, the best design choices for SpMM differ from the conventional algorithms that are known to perform well for dense matrix-matrix or sparse matrix-sparse matrix multiplies.

关键词： Sparse linear algebra Sparse matrices Graphics accelerators parallel algorithms RDMA

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Fast parallel algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering 21

Fast Parallel Algorithms for Euclidean Minimum Spanning Tree...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Wang, Yiqiu Yu, Shangdi Gu, Yan Shun, Julian MIT CSAIL Cambridge MA 02139 USA UC Riverside Riverside CA USA

ISBN: (纸本)9781450383431

This paper presents new parallel algorithms for generating Euclidean minimum spanning trees and spatial clustering hierarchies (known as HDBSCAN*). Our approach is based on generating a well-separated pair decomposition followed by using Kruskal's minimum spanning tree algorithm and bichromatic closest pair computations. We introduce a new notion of well-separation to reduce the work and space of our algorithm for HDBSCAN*. We also give a new parallel divide-and-conquer algorithm for computing the dendrogram and reachability plots, which are used in visualizing clusters of different scale that arise for both EMST and HDBSCAN*. We show that our algorithms are theoretically efficient: they have work (number of operations) matching their sequential counterparts, and polylogarithmic depth (parallel time). We implement our algorithms and propose a memory optimization that requires only a subset of well-separated pairs to be computed and materialized, leading to savings in both space (up to 10x) and time (up to 8x). Our experiments on large real-world and synthetic data sets using a 48-core machine show that our fastest algorithms outperform the best serial algorithms for the problems by 11.13-55.89x, and existing parallel algorithms by at least an order of magnitude.

关键词： parallel algorithms shared memory algorithms clustering

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parallel algorithms for Sparse Matrix Multiplication and Join-Aggregate Queries 20

Parallel Algorithms for Sparse Matrix Multiplication and Joi...

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39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS)

作者： Hu, Xiao Yi, Ke Duke Univ Durham NC 27706 USA HKUST Hong Kong Peoples R China

ISBN: (纸本)9781450371087

In this paper, we design massively parallel algorithms for sparse matrix multiplication, as well as more general join-aggregate queries, where the join hypergraph is a tree with arbitrary output attributes. For each case, we obtain asymptotic improvement over existing algorithms. In particular, our matrix multiplication algorithm is shown to be optimal in the semiring model.

关键词： parallel algorithms sparse matrix multiplication join-aggregate query

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