Medusa: Simplified Graph Processing on GPUs

Zhong, Jianlong; He, Bingsheng

doi:10.1109/tpds.2013.111

Cited by 196 publications

(17 citation statements)

References 22 publications

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“…We first evaluate its single-node performance by comparing it with Gunrock (March 11, 2018 commit), the state-of-the-art single-node multi-GPU graph analytics system, for rmat26 and twitter40 (Gunrock runs out-of-memory for rmat28 or larger graphs) on a platform with four GPUs sharing a physical node. Like other existing multi-GPU graph analytical systems [8,74], Gunrock can handle only outgoing edge-cuts 5 . We evaluated D-IrGL with the partitioning policies described in Section 5.2.…”

Section: Strong Scaling Of Distributed Gpu Systemmentioning

confidence: 99%

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

et al. 2018

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This paper introduces a new approach to building distributedmemory graph analytics systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies, and programming models. The key to this approach is Gluon, a communication-optimizing substrate. Programmers write applications in a shared-memory programming system of their choice and interface these applications with Gluon using a lightweight API. Gluon enables these programs to run on heterogeneous clusters and optimizes communication in a novel way by exploiting structural and temporal invariants of graph partitioning policies. To demonstrate Gluon's ability to support different programming models, we interfaced Gluon with the Galois and Ligra shared-memory graph analytics systems to produce distributed-memory versions of these systems named D-Galois and D-Ligra, respectively. To demonstrate Gluon's ability to support heterogeneous processors, we interfaced Gluon with IrGL, a state-of-the-art single-GPU system for * Both authors contributed equally.

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Section: Strong Scaling Of Distributed Gpu Systemmentioning

confidence: 99%

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

et al. 2018

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“…In this section, we examine the WolfPath's ability to process large graphs which cannot fit into GPU memory. To the best of our knowledge, the state-of-art GPU-based graph processing frameworks [6,16,47] assume that the input graphs can fit in the GPU memory. Therefore, in this work, we compare WolfPath (WP) with two CPU-based, out-of-memory graph processing framework: GraphChi (GC) [17] and X-Stream (XS) [33].…”

Section: Comparison With Gpu Out-of-memory Frameworkmentioning

confidence: 99%

“…Medusa [47] is a GPU-based graph processing framework that focuses on abstractions for easy programming. MapGraph [6] implements the runtime-based optimisation to deliver good performance.…”

Section: Effect Of Optimisation Techniquesmentioning

confidence: 99%

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WolfPath: Accelerating Iterative Traversing-Based Graph Processing Algorithms on GPU

Zhu

et al. 2017

Int J Parallel Prog

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There is the significant interest nowadays in developing the frameworks of parallelizing the processing for the large graphs such as social networks, Web graphs, etc. Most parallel graph processing frameworks employ iterative processing model. However, by benchmarking the state-of-art GPU-based graph processing frameworks, we observed that the performance of iterative traversing-based graph algorithms (such as Bread First Search, Single Source Shortest Path and so on) on GPU is limited by the frequent data exchange between host and GPU. In order to tackle the problem, we develop a GPU-based graph framework called WolfPath to accelerate the processing of iterative traversing-based graph processing algorithms. In WolfPath, the iterative process is guided by the graph diameter to eliminate the frequent data exchange between host and GPU. To accomplish this goal, WolfPath proposes a data structure called Layered Edge list to represent the graph, from which the graph diameter is known before the start of graph processing. In order to enhance the applicability of our WolfPath 123Int J Parallel Prog framework, a graph preprocessing algorithm is also developed in this work to convert any graph into the format of the Layered Edge list. We conducted extensive experiments to verify the effectiveness of WolfPath. The experimental results show that WolfPath achieves significant speedup over the state-of-art GPU-based in-memory and out-of-memory graph processing frameworks.

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“…An example of the former is GraphX [30], which incurs some overhead while gaining fault tolerance and an example of the latter is Ligra [27], a framework requiring a shared-memory architecture. Additionally, there is a growing number of systems designed to run on GPUs [31].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Parallel Graph Processing Implementations

Pollard

Norris

2017

2017 IEEE International Conference on Cluster Computing (CLUSTER)

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The rapidly growing number of large network analysis problems has led to the emergence of many parallel and distributed graph processing systems-one survey in 2014 identified over 80. Since then, the landscape has evolved; some packages have become inactive while more are being developed. Determining the best approach for a given problem is infeasible for most developers. To enable easy, rigorous, and repeatable comparison of the capabilities of such systems, we present an approach and associated software for analyzing the performance and scalability of parallel, open-source graph libraries. We demonstrate our approach on five graph processing packages: Graph-Mat, the Graph500, the Graph Algorithm Platform Benchmark Suite, GraphBIG, and PowerGraph using synthetic and real-world datasets. We examine previously overlooked aspects of parallel graph processing performance such as phases of execution and energy usage for three algorithms: breadth first search, single source shortest paths, and PageRank and compare our results to Graphalytics.

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Medusa: Simplified Graph Processing on GPUs

Cited by 196 publications

References 22 publications

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

WolfPath: Accelerating Iterative Traversing-Based Graph Processing Algorithms on GPU

A Comparison of Parallel Graph Processing Implementations

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