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检索条件"主题词=Parallel graph algorithms"

共 72 条记录，以下是51-60 订阅

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A Shared-Memory Algorithm for Updating Single-Source Shortest Paths in Large Weighted Dynamic Networks 25

A Shared-Memory Algorithm for Updating Single-Source Shortes...

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25th IEEE International Conference on High Performance Computing, Data and Analytics (HiPC)

作者： Srinivasan, Sriram Riazi, Sara Norris, Boyana Das, Sajal K. Bhowmick, Sanjukta Univ Nebraska Omaha NE 68106 USA Univ Oregon Eugene OR 97403 USA Missouri Univ Sci & Technol Rolla MO 65409 USA Univ North Texas Denton TX 76203 USA

ISBN: (纸本)9781538683866

Computing the single-source shortest path (SSSP) is one of the fundamental graph algorithms, and is used in many applications. Here, we focus on computing SSSP on large dynamic graphs, i.e. graphs whose structure evolves with time. We posit that instead of recomputing the SSSP for each set of changes on the dynamic graphs, it is more efficient to update the results based only on the region of change. To this end, we present a novel two step shared-memory algorithm for updating SSSP on weighted large-scale graphs. The key idea of our algorithm is to identify changes, such as vertex/edge addition and deletion, that affect the shortest path computations and update only the parts of the graphs affected by the change. We provide the proof of correctness of our proposed algorithm. Our experiments on real and synthetic networks demonstrate that our algorithm is as much as 4X faster compared to computing SSSP with Galois, a state-of-the-art parallel graph analysis software for shared memory architectures. We also demonstrate how increasing the asynchrony can lead to even faster updates. To the best of our knowledge, this is one of the first practical parallel algorithms for updating networks on shared-memory systems, that is also scalable to large networks.

关键词： dynamic networks parallel graph algorithms single-source shortest path

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parallel Heuristics for Scalable Community Detection 28

Parallel Heuristics for Scalable Community Detection

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28th IEEE International parallel & Distributed Processing Symposium Workshops (IPDPSW)

作者： Lu, Hao Kalyanaraman, Ananth Halappanavar, Mahantesh Choudhury, Sutanay Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Pacific Northwest Natl Lab Computat Sci & Math Div Richland WA 99352 USA

ISBN: (纸本)9781479941162

Community detection has become a fundamental operation in numerous graph-theoretic applications. It is used to reveal natural divisions that exist within real world networks without imposing prior size or cardinality constraints on the set of communities. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to the irregular and inherently sequential nature of the underlying heuristics. In this paper, we present parallelization heuristics for fast community detection using the Louvain method as the serial template. The Louvain method is an iterative heuristic for modularity optimization. Originally developed by Blondel et al. in 2008, the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. However, the method is also inherently sequential, thereby limiting its scalability. Here, we observe certain key properties of this method that present challenges for its parallelization, and consequently propose heuristics that are designed to break the sequential barrier. For evaluation purposes, we implemented our heuristics using OpenMP multithreading, and tested them over real world graphs derived from multiple application domains (e.g., internet, citation, biological). Compared to the serial Louvain implementation, our parallel implementation is able to produce community outputs with a higher modularity for most of the inputs tested, in comparable number of iterations, while providing real speedups of up to 8x using 32 threads. In addition, our parallel implementation was able to exhibit weak scaling properties on up to 32 threads.

关键词： Community detection graph coloring Louvain method parallel graph algorithms parallel heuristics

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GOSH: Embedding Big graphs on Small Hardware 20

GOSH: Embedding Big Graphs on Small Hardware

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49th International Conference on parallel Processing (ICPP)

作者： Akyildiz, Taha Atahan Aljundi, Amro Alabsi Kaya, Kamer Sabanci Univ Istanbul Turkey

ISBN: (纸本)9781450388160

In graph embedding, the connectivity information of a graph is used to represent each vertex as a point in a d-dimensional space. Unlike the original, irregular structural information, such a representation can be used for a multitude of machine learning tasks. Although the process is extremely useful in practice, it is indeed expensive and unfortunately, the graphs are becoming larger and harder to embed. Attempts at scaling up the process to larger graphs have been successful but often at a steep price in hardware requirements. We present Gosh, an approach for embedding graphs of arbitrary sizes on a single GPU with minimum constraints. Gosh utilizes a novel graph coarsening approach to compress the graph and minimize the work required for embedding, delivering high-quality embeddings at a fraction of the time compared to the state-of-the-art. In addition to this, it incorporates a decomposition schema that enables any arbitrarily large graph to be embedded using a single GPU with minimum constraints on the memory size. With these techniques, Gosh is able to embed a graph with over 65 million vertices and 1.8 billion edges in less than an hour on a single GPU and obtains a 93% AUCROC for link-prediction which can be increased to 95% by running the tool for 80 minutes.

关键词： graph embedding GPU parallel graph algorithms graph coarsening link prediction

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Low-Latency graph Streaming using Compressed Purely-Functional Trees 2019

Low-Latency Graph Streaming using Compressed Purely-Function...

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40th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) part of ACM's Federated Computing Research Conference (FCRC)

作者： Dhulipala, Laxman Blelloch, Guy E. Shun, Julian Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9781450367127

There has been a growing interest in the graph-streaming setting where a continuous stream of graph updates is mixed with graph queries. In principle, purely-functional trees are an ideal fit for this setting as they enable safe parallelism, lightweight snapshots, and strict serializability for queries. However, directly using them for graph processing leads to significant space overhead and poor cache locality. This paper presents C-trees, a compressed purely-functional search tree data structure that significantly improves on the space usage and locality of purely-functional trees. We design theoretically-efficient and practical algorithms for performing batch updates to C-trees, and also show that we can store massive dynamic real-world graphs using only a few bytes per edge, thereby achieving space usage close to that of the best static graph processing frameworks. To study the applicability of our data structure, we designed Aspen, a graph-streaming framework that extends the interface of Ligra with operations for updating graphs. We show that Aspen is faster than two state-of-the-art graph-streaming systems, Stinger and LLAMA, while requiring less memory, and is competitive in performance with the state-of-the-art static graph frameworks, Galois, GAP, and Ligra+. With Aspen, we are able to efficiently process the largest publicly-available graph with over two hundred billion edges in the graph-streaming setting using a single commodity multicore server with 1TB of memory.

关键词： streaming graph processing purely-functional data structures parallel graph algorithms

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Scaling graph Community Detection on the Tilera Many-core Architecture 21

Scaling Graph Community Detection on the Tilera Many-core Ar...

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21st International Conference on High Performance Computing (HiPC)

作者： Chavarria-Miranda, Daniel Halappanavar, Mahantesh Kalyanaraman, Ananth Pacific Northwest Natl Lab High Performance Comp Richland WA 99352 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA

ISBN: (纸本)9781479959754

In an era when power constraints and data movement are proving to be significant barriers for the application of high-end computing, the Tilera many-core architecture offers a low-power platform exhibiting many important characteristics of future systems, including a large number of simple cores, a sophisticated network-on-chip, and fine-grained control over memory and caching policies. While this emerging architecture has been previously studied for structured compute-intensive kernels, benchmarking the platform for data-bound, irregular applications present significant challenges that have remained unexplored. Community detection is an advanced prototypical graph-theoretic operation with applications in numerous scientific domains including life sciences, cyber security, and power systems. In this work, we explore multiple design strategies toward developing a scalable tool for community detection on the Tilera platform. Using several memory layout and work scheduling techniques we demonstrate speedups of up to 47x on 36 cores of the Tilera TileGX36 platform over the best serial implementation, and also show results that have comparable quality and performance to mainstream x86 platforms. To the best of our knowledge this is the first work addressing graph algorithms on the Tilera platform. This study demonstrates that through careful design space exploration, low-power many-core platforms like Tilera can be effectively exploited for graph algorithms that embody all the essential characteristics of an irregular application.

关键词： Tilera community detection many-core parallel graph algorithms

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Performance Comparison of graph BFS Implemented in MapReduce and PGAS Programming Models 12th

Performance Comparison of Graph BFS Implemented in MapReduce...

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12th International Conference on parallel Processing and Applied Mathematics (PPAM)

作者： Ryczkowska, Magdalena Nowicki, Marek Univ Warsaw Interdisciplinary Ctr Math & Computat Modeling Pawinskiego 5a PL-02106 Warsaw Poland Nicolaus Copernicus Univ Fac Math & Comp Sci Chopina 12-18 PL-87100 Torun Poland

ISBN: (纸本)9783319780542;9783319780535

Computations based on graphs are very common problems but complexity, increasing size of analyzed graphs and a huge amount of communication make this analysis a challenging task. In this paper, we present a comparison of two parallel BFS (Breath- First Search) implementations: MapReduce run on Hadoop infrastructure and in PGAS (Partitioned Global Address Space) model. The latter implementation has been developed with the help of the PCJ (parallel Computations in Java) - a library for parallel and distributed computations in Java. Both implementations realize the level synchronous strategy - Hadoop algorithm assumes iterative MapReduce jobs, whereas PCJ uses explicit synchronization after each level. The scalability of both solutions is similar. However, the PCJ implementation is much faster (about 100 times) than the MapReduce Hadoop solution.

关键词： High performance computing Hadoop MapReduce PGAS parallel and distributed computation Performance evaluation parallel graph algorithms Java

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Towards a graphBLAS Library in Chapel 31

Towards a GraphBLAS Library in Chapel

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31st IEEE International parallel and Distributed Processing Symposium Workshops (IPDPS)

作者： Azad, Ariful Buluc, Aydin Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

ISBN: (纸本)9780769561493

The adoption of a programming language is positively influenced by the breadth of its software libraries. Chapel is a modern and relatively young parallel programming language. Consequently, not many domain-specific software libraries exists that are written for Chapel. graph processing is an important domain with many applications in cyber security, energy, social networking, and health. Implementing graph algorithms in the language of linear algebra enables many advantages including rapid development, flexibility, high-performance, and scalability. graphBLAS initiative aims to standardize an interface for linear-algebraic primitives for graph computations. This paper presents initial experiences and findings of implementing a subset of important graphBLAS operations in Chapel. We analyzed the bottlenecks in both shared and distributed memory. We also provided alternative implementations whenever the default implementation lacked performance or scaling.

关键词： Chapel graphBLAS parallel graph algorithms PGAS

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Accelerating CUDA graph algorithms at Maximum Warp 11

Accelerating CUDA Graph Algorithms at Maximum Warp

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16th ACM Symposium on Principles and Practice of parallel Programming

作者： Hong, Sungpack Kim, Sang Kyun Oguntebi, Tayo Olukotun, Kunle Stanford Univ Comp Syst Lab Stanford CA 94305 USA

ISBN: (纸本)9781450301190

graphs are powerful data representations favored in many computational domains. Modern GPUs have recently shown promising results in accelerating computationally challenging graph problems but their performance suffers heavily when the graph structure is highly irregular, as most real-world graphs tend to be. In this study, we first observe that the poor performance is caused by work imbalance and is an artifact of a discrepancy between the GPU programming model and the underlying GPU architecture. We then propose a novel virtual warp-centric programming method that exposes the traits of underlying GPU architectures to users. Our method significantly improves the performance of applications with heavily imbalanced workloads, and enables trade-offs between workload imbalance and ALU underutilization for fine-tuning the performance. Our evaluation reveals that our method exhibits up to 9x speedup over previous GPU algorithms and 12x over single thread CPU execution on irregular graphs. When properly configured, it also yields up to 30% improvement over previous GPU algorithms on regular graphs. In addition to performance gains on graph algorithms, our programming method achieves 1.3x to 15.1x speedup on a set of GPU benchmark applications. Our study also confirms that the performance gap between GPUs and other multi-threaded CPU graph implementations is primarily due to the large difference in memory bandwidth.

关键词： algorithms Performance parallel graph algorithms CUDA GPGPU

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Scalable parallel Minimum Spanning Forest Computation

Scalable Parallel Minimum Spanning Forest Computation

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17th ACM SIGPLAN Symposium on Principles and Practice of parallel Programming

作者： Nobari, Sadegh Cao, Thanh-Tung Karras, Panagiotis Bressan, Stephane Natl Univ Singapore Singapore Singapore Rutgers State Univ Piscataway NJ 08855 USA

The proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graph's minimum spanning forest (MSF). Past research has proposed several parallel algorithms for this problem, yet none of them scales to large, high-density graphs. In this paper we propose a novel, scalable, parallel MSF algorithm for undirected weighted graphs. Our algorithm leverages Prim's algorithm in a parallel fashion, concurrently expanding several subsets of the computed MSF. Our effort focuses on minimizing the communication among different processors without constraining the local growth of a processor's computed subtree. In effect, we achieve a scalability that previous approaches lacked. We implement our algorithm in CUDA, running on a GPU and study its performance using real and synthetic, sparse as well as dense, structured and unstructured graph data. Our experimental study demonstrates that our algorithm outperforms the previous state-of-the-art GPU-based MSF algorithm, while being several order of magnitude faster than sequential CPU-based algorithms.

关键词： algorithms Experimentation Performance parallel graph algorithms Minimum Spanning Forest GPU

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Substream-Centric Maximum Matchings on FPGA 19

Substream-Centric Maximum Matchings on FPGA

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ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA)

作者： Besta, Maciej Fischer, Marc Ben-Nun, Tal Licht, Johannes De Fine Hoefler, Torsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781450361378

Developing high-performance and energy-efficient algorithms for maximum matchings is becoming increasingly important in social network analysis, computational sciences, scheduling, and others. In this work, we propose the first maximum matching algorithm designed for FPGAs;it is energy-efficient and has provable guarantees on accuracy, performance, and storage utilization. To achieve this, we forego popular graph processing paradigms, such as vertex-centric programming, that often entail large communication costs. Instead, we propose a substream-centric approach, in which the input stream of data is divided into substreams processed independently to enable more parallelism while lowering communication costs. We base our work on the theory of streaming graph algorithms and analyze 14 models and 28 algorithms. We use this analysis to provide theoretical underpinning that matches the physical constraints of FPGA platforms. Our algorithm delivers high performance (more than 4x speedup over tuned parallel CPU variants), low memory, high accuracy, and effective usage of FPGA resources. The substream-centric approach could easily be extended to other algorithms to offer low-power and high-performance graph processing on FPGAs.

关键词： matching graph streaming parallel graph algorithms maximum matching fpga

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