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检索条件"主题词=Parallel graph algorithms"

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Enabling Exploratory Large Scale graph Analytics through Arkouda

Enabling Exploratory Large Scale Graph Analytics through Ark...

引用

IEEE High Performance Extreme Computing Conference (HPEC)

作者： Du, Zhihui Rodriguez, Oliver Alvarado Bader, David A. New Jersey Inst Technol Dept Data Sci Newark NJ 07102 USA

ISBN: (纸本)9781665423694

Exploratory graph analytics helps maximize the informational value from a graph. However, increasing graph sizes makes it impossible for existing popular exploratory data analysis tools to handle dozens of terabytes or even larger data sets in the memory of a common laptop/personal computer. Arkouda is a framework under early development that brings together the productivity of Python at the user-side with the high performance of Chapel at the server-side. In this paper, we present our initial work on overcoming the memory limit and high-performance computing coding roadblocks for high-level Python users to perform large graph analyses. Based on a simple and succinct graph data structure, a high-level Chapel-based graph algorithm, Breadth-First Search (BFS), is presented to show the scalable and parallel graph algorithm development method in a productive way through Arkouda. The reverse Cuthill-McKee (RCM) algorithm is implemented in Chapel to relabel the vertices of a graph as a preprocessing step to improve the performance of BFS and one low-level BFS algorithm is also developed to compare with the performance of high-level method. Both synthetic graphs and typical graph benchmarks are used to evaluate the performance of the provided graph algorithms. The experimental results show that, based on the proposed high-level algorithm framework, the performance of BFS can be improved significantly and easily by simply selecting suitable Chapel high-level data structures and parallel constructs. Our code is open source and available from GitHub (https://***/Bader-Research/arkouda).

关键词： Exploratory graph analysis parallel graph algorithms Breadth-First Search High-Performance Computing

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A coarse grained parallel algorithm for closest larger ancestors in trees with applications to single link clustering

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1st International on High Performance Computing and Communications (HPCC 2005)

作者： Chan, A Gao, CM Rau-Chaplin, A Fayetteville State Univ Dept Math & Comp Sci Fayetteville NC 28301 USA Dalhousie Univ Fac Comp Sci Halifax NS B3J 2X4 Canada

ISBN: (纸本)3540290311

Hierarchical clustering methods are important in many data mining and pattern recognition tasks. In this paper we present an efficient coarse grained parallel algorithm for Single Link Clustering;a standard inter-cluster linkage metric. Our approach is to first describe algorithms for the Prefix Larger Integer Set and the Closest Larger Ancestor problems and then to show how these can be applied to solve the Single Link Clustering problem. In an extensive performance analysis an implementation of these algorithms on a Linux-based cluster has shown to scale well, exhibiting near linear relative speedup.

关键词： single link clustering closest larger ancestor parallel graph algorithms coarse grained multicomputer hierarchical agglomerative clustering

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Toward an Efficient, Highly Scalable Maximum Clique Solver for Massive graphs 2

Toward an Efficient, Highly Scalable Maximum Clique Solver f...

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IEEE International Conference on Big Data

作者： Hagan, Ronald D. Phillips, Charles A. Wang, Kai Rogers, Gary L. Langston, Michael A. Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Univ Tennessee Natl Inst Computat Sci Oak Ridge TN USA

ISBN: (纸本)9781479956661

As the size of available data sets grows, so too does the demand for efficient parallel algorithms that will yield the solution to complex combinatorial problems on graphs that may be too large to fit entirely in memory. In previous work, we have provided a set of out-of-core algorithms to solve one of the central examples of such a problem, maximum clique. In this paper, we review the algorithms and report on our ongoing work to use them as a starting point for an optimized, highly scalable implementation of a maximum clique solver.

关键词： big data parallel graph algorithms out-of-core maximum clique

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Degree-Aware Kernels for Computing Jaccard Weights on GPUs 36

Degree-Aware Kernels for Computing Jaccard Weights on GPUs

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36th IEEE International parallel and Distributed Processing Symposium (IEEE IPDPS)

作者： Aljundi, Amro Alabsi Akyildiz, Taha Atahan Kaya, Kamer Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9781665481069

graphs provide the ability to extract valuable metrics from the structural properties of the underlying data they represent. One such metric is the Jaccard Weight of an edge, which is the ratio of the number of common neighbors of the edge's endpoints to the union of the endpoints' neighborhood. A naive implementation of Jaccard Weights computation has a complexity that scales with the number of edges in the graph times the square of the maximum degree. Recently, GPU-based parallel algorithms have been proposed for this problem. However, these algorithms cannot overcome the structural variance within a graph, i.e., the sparsity pattern and degree imbalance, which directly translates to unbalanced work distribution across threads. In this work, we propose an optimized GPU-based algorithm with an ML-based work distribution model that mitigates the unbalanced work distribution. Our algorithm is shown to be up to 35x and on average 12x faster than the state of the art in practice while using less memory. In fact, we show that by manually tweaking the load distribution, a state-of-the-art implementation can be 5x faster. In addition, we propose a multi-core, shared-memory algorithm that applies a traditional but effective technique to improve the computation asymptotically and perform comparably to the GPU algorithms. Our code is available at https://***/SU- HPC/Jaccard-ML

关键词： Jaccard Weights parallel graph algorithms GPU work distribution

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A Fast and Scalable graph Coloring Algorithm for Multi-core and Many-core Architectures 21st

A Fast and Scalable Graph Coloring Algorithm for Multi-core ...

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21st International Conference on parallel and Distributed Computing (Euro-Par)

作者： Rokos, Georgios Gorman, Gerard Kelly, Paul H. J. Univ London Imperial Coll Sci Technol & Med Dept Comp Software Performance Optimisat Grp London SW7 2AZ England Univ London Imperial Coll Sci Technol & Med Dept Earth Sci & Engn Appl Modelling & Computat Grp London SW7 2AZ England

ISBN: (纸本)9783662480960;9783662480953

Irregular computations on unstructured data are an important class of problems for parallel programming. graph coloring is often an important preprocessing step, e.g. as a way to perform dependency analysis for safe parallel execution. The total run time of a coloring algorithm adds to the overall parallel overhead of the application whereas the number of colors used determines the amount of exposed parallelism. A fast and scalable coloring algorithm using as few colors as possible is vital for the overall parallel performance and scalability of many irregular applications that depend upon runtime dependency analysis. Catalyurek et al. have proposed a graph coloring algorithm which relies on speculative, local assignment of colors. In this paper we present an improved version which runs even more optimistically with less thread synchronization and reduced number of conflicts compared to Catalyurek et al.'s algorithm. We show that the new technique scales better on multicore and many-core systems and performs up to 1.5x faster than its predecessor on graphs with high-degree vertices, while keeping the number of colors at the same near-optimal levels.

关键词： graph coloring Greedy coloring First-fit coloring Irregular data parallel graph algorithms Shared-memory parallelism Optimistic execution Many-core architectures Intel (R) Xeon Phi (TM)

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An Empirical Comparison of k-Shortest Simple Path algorithms on Multicores 18

An Empirical Comparison of <i>k</i>-Shortest Simple Path Alg...

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47th International Conference on parallel Processing (ICPP)

作者： Ajwani, Deepak Duriakova, Erika Hurley, Neil Meyer, Ulrich Schickedanz, Alexander Nokia Bell Labs Dublin Ireland Univ Coll Dublin Sch Comp Sci Insight Ctr Data Analyt Dublin Ireland Goethe Univ Frankfurt Germany

ISBN: (纸本)9781450365109

We consider the loop less k-shortest path (KSP) problem. Although this problem has been studied in the sequential setting for at least the last two decades, no good parallel implementations are known. In this paper, we provide (i) a first systematic empirical comparison of various KSP algorithms and heuristic optimisations, (ii) carefully engineer various parallel implementations of these sequential algorithms and (iii) perform an extensive study of these parallel implementations on a range of graph classes and multicore architectures to determine the best algorithm and parallelization strategy for different graph classes. We find that even though the worst-case complexity of the best undirected KSP algorithm O (k (m + n log n)) is significantly better than that of the popular and considerably simpler directed KSP algorithm O(kn(m + n log n)), the two algorithms are fairly competitive in terms of their empirical performance on small diameter graphs. Furthermore, we show that a few simple optimisations help to bridge the gap between these KSP algorithms even more. However, on moderate to large diameter graphs, the undirected KSP algorithm is considerably faster than the directed algorithms, both in sequential and parallel settings. In terms of the parallelisation strategy, simply replacing the shortest path subroutine by parallel A-stepping algorithm can provide a good speed-up for many KSP algorithms on random graphs. In contrast, for graphs with skewed degree distribution, a more complex strategy of parallelizing the different deviations and then parallelizing the shortest path computation inside the deviations with the remaining threads, provides a better performance.

关键词： Algorithm Engineering parallel k-Shortest Path parallel graph algorithms parallel Single Source Shortest Path

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Counting Triangles in Large graphs on GPU 30

Counting Triangles in Large Graphs on GPU

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30th IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Polak, Adam Jagiellonian Univ Fac Math & Comp Sci Dept Theoret Comp Sci Krakow Poland

ISBN: (纸本)9781509036820

The clustering coefficient and the transitivity ratio are concepts often used in network analysis, which creates a need for fast practical algorithms for counting triangles in large graphs. Previous research in this area focused on sequential algorithms, MapReduce parallelization, and fast approximations. In this paper we propose a parallel triangle counting algorithm for CUDA GPU. We describe the implementation details necessary to achieve high performance and present the experimental evaluation of our approach. The algorithm achieves 15 to 35 times speedup over our CPU implementation, and is capable of finding 8.8 billion triangles in a 180 million edges graph in 12 seconds on the Nvidia GeForce GTX 980 GPU.

关键词： GPU CUDA parallel graph algorithms triangles clustering coefficient

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A Brief Survey of algorithms, Architectures, and Challenges toward Extreme-scale graph Analytics 22

A Brief Survey of Algorithms, Architectures, and Challenges ...

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22nd Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Kalyanaraman, Ananth Pande, Partha Pratim Washington State Univ Sch EECS Pullman WA 99164 USA

ISBN: (纸本)9783981926323

The notion of networks is inherent in the structure, function and behavior of the natural and engineered world that surround us. Consequently, graph models and methods have assumed a prominent role to play in this modern era of Big Data, and are taking a center stage in the discovery pipelines of various data-driven scientific domains. In this paper, we present a brief review of the state-of-the-art in parallel graph analytics, particularly focusing on iterative graph algorithms and their implementation on modern day multicore/manycore architectures. The class of iterative graph algorithms covers a broad class of graph operations of varying complexities, from simpler routines such as Breadth-First Search (BFS), to polynomially-solvable problems such as shortest path computations, to NP-Hard problems such as community detection and graph coloring. We cover a set of common algorithmic abstractions used in implementing such iterative graph algorithms, state the challenges around parallelization on contemporary parallel platforms (including commodity multicores and emerging manycore platforms), and describe a set of approaches that have led to efficient implementations. We also report on advances in manycore architectural frameworks that have found application in parallel graph analytics. We conclude the paper identifying potential research directions, opportunities, and challenges that lay ahead in the path toward enabling graph analytics at exascale.

关键词： parallel graph algorithms parallel architectures irregular applications extreme-scale computing

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SISA: Set-Centric Instruction Set Architecture for graph Mining on Processing-in-Memory Systems 21

SISA: Set-Centric Instruction Set Architecture for Graph Min...

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54th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Besta, Maciej Kanakagiri, Raghavendra Kwasniewski, Grzegorz Ausavarungnirun, Rachata Beranek, Jakub Kanellopoulos, Konstantinos Janda, Kacper Vonarburg-Shmaria, Zur Gianinazzi, Lukas Stefan, Ioana Gomez-Luna, Juan Copik, Marcin Kapp-Schwoerer, Lukas Di Girolamo, Salvatore Blach, Nils Konieczny, Marek Mutlu, Onur Hoefler, Torsten Swiss Fed Inst Technol Zurich Switzerland IIT Tirupati Tirupati Andhra Pradesh India King Mongkuts Univ Technol North Bangkok Bangkok Thailand Tech Univ Ostrava Ostrava Czech Republic AGH Univ Sci & Technol Lublin Poland

ISBN: (纸本)9781450385572

Simple graph algorithms such as PageRank have been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such as clustering or maximal clique listing. These algorithms are memory-bound and thus could be accelerated by hardware techniques such as Processing-in-Memory (PIM). However, they also come with non-straightforward parallelism and complicated memory access patterns. In this work, we address this problem with a simple yet surprisingly powerful observation: operations on sets of vertices, such as intersection or union, form a large part of many complex graph mining algorithms, and can offer rich and simple parallelism at multiple levels. This observation drives our cross-layer design, in which we (1) expose set operations using a novel programming paradigm, (2) express and execute these operations efficiently with carefully designed set-centric ISA extensions called SISA, and (3) use PIM to accelerate SISA instructions. The key design idea is to alleviate the bandwidth needs of SISA instructions by mapping set operations to two types of PIM: in-DRAM bulk bitwise computing for bitvectors representing high-degree vertices, and near-memory logic layers for integer arrays representing low-degree vertices. Set-centric SISA-enhanced algorithms are efficient and outperform hand-tuned baselines, offering more than 10x speedup over the established Bron-Kerbosch algorithm for listing maximal cliques. We deliver more than 10 SISA set-centric algorithm formulations, illustrating SISA's wide applicability.

关键词： graph Mining graph Pattern Matching graph Learning Clique Mining Clique Listing Clique Enumeration Subgraph Isomorphism parallel graph algorithms Processing In Memory Processing Near Memory graph Accelerators Instruction Set Architecture

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FuseIM: Fusing Probabilistic Traversals for Influence Maximization on Exascale Systems 24

FuseIM: Fusing Probabilistic Traversals for Influence Maximi...

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38th ACM International Conference on Supercomputing (ACM ICS)

作者： Neff, Reece Zarch, Mostafa Eghbali Minutoli, Marco Halappanavar, Mahantesh Tumeo, Antonino Kalyanaraman, Ananth Becchi, Michela North Carolina State Univ Raleigh NC 27695 USA Pacific Northwest Natl Lab Richland WA USA Washington State Univ Pullman WA USA

ISBN: (纸本)9798400706103

Probabilistic breadth-first traversals (BPTs) are used in many network science and graph machine learning applications. In this paper, we are motivated by the application of BPTs in stochastic diffusion-based graph problems such as influence maximization. These applications heavily rely on BPTs to implement a Monte-Carlo sampling step for their approximations. Given the large sampling complexity, stochasticity of the diffusion process, and the inherent irregularity in real-world graph topologies, efficiently parallelizing these BPTs remains significantly challenging. In this paper, we present a new algorithm to fuse a massive number of concurrently executing BPTs with random starts on the input graph. Our algorithm is designed to fuse BPTs by combining separate probabilistic traversals into a unified frontier. To show the general applicability of the fused BPT technique, we have incorporated it into two state-of-the-art influence maximization parallel implementations (gIM and Ripples). Our experiments on up to 4K nodes of the OLCF Frontier supercomputer (32, 768 GPUs and 196K CPU cores) show strong scaling behavior, and that fused BPTs can improve the performance of these implementations up to 182.13x (avg. 75.15x) and 359.86x (avg. 135.17x) for gIM and Ripples, respectively.

关键词： parallel graph algorithms Influence Maximization

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