WGB: Towards a Universal Graph Benchmark

Ammar, Khaled; Özsu, M. Tamer

doi:10.1007/978-3-319-10596-3_6

Cited by 11 publications

(14 citation statements)

References 22 publications

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“…[B] Cyclone [113] [B] GBBS [46] + Ligra [106] [B] GraphBIG [94] [B] GAPBS [13] [B] LDBC [23] ⇤ ⇤ ⇤ ⇤ ⇤ [B] WGB [9] [B] PBBS [19] [B] Graph500 [93] [B] CRONO [6] [B] GARDENIA [126] [B] GMS Table 2: Related work analysis, part 2: GMS vs. graph benchmarks ("[B]") and graph pattern matching frameworks ("[F]"), focusing on supported functionalities important for developing fast and simple graph mining algorithms. New alg?…”

Section: Reference / Infrastructurementioning

confidence: 99%

GraphMineSuite

et al. 2021

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We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by >2X), maximal clique listing (by >9×), k -clique listing (by up to 1.1×), and subgraph isomorphism (by 2.5×), also obtaining better theoretical performance bounds.

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Section: Reference / Infrastructurementioning

confidence: 99%

GraphMineSuite

et al. 2021

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“…All studies consider power-law real and synthetic datasets. Many systems use graph data generators, such as Data-Gen [20], WGB [9], RTG [8], LUBM [25], and gMark [12] to create large datasets. The main objective of these graph generators is to represent real graphs and allow the creation of graph datasets with different sizes.…”

Section: Related Workmentioning

confidence: 99%

“…This issue can happen when MPI library is used to aggregate a large number of data items. The issue is known to the MPI community 9 and is a problem with MPI rather than Blogel.…”

Section: Blogel: the Overall Winnermentioning

confidence: 99%

Experimental analysis of distributed graph systems

Ammar

Özsu

2018

Proc. VLDB Endow.

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This paper evaluates eight parallel graph processing systems: Hadoop, HaLoop, Vertica, Giraph, GraphLab (Pow-erGraph), Blogel, Flink Gelly, and GraphX (SPARK) over four very large datasets (Twitter, World Road Network, UK 200705, and ClueWeb) using four workloads (PageRank, WCC, SSSP and K-hop). The main objective is to perform an independent scale-out study by experimentally analyzing the performance, usability, and scalability (using up to 128 machines) of these systems. In addition to performance results, we discuss our experiences in using these systems and suggest some system tuning heuristics that lead to better performance.

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“…Reference drivers can be provided by platform vendors or obtained from public repository. The Graphalytics team also offers the drivers for a number of platforms (7).…”

Section: Design Of the Graphalytics Architecturementioning

confidence: 99%

LDBC graphalytics

et al. 2016

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In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective comparison of graph analysis platforms. Its test harness produces deep metrics that quantify multiple kinds of system scalability, such as horizontal/vertical and weak/strong, and of robustness, such as failures and performance variability. The benchmark comes with open-source software for generating data and monitoring performance. We describe and analyze six implementations of the benchmark (three from the community, three from the industry), providing insights into the strengths and weaknesses of the platforms. Key to our contribution, vendors perform the tuning and benchmarking of their platforms.

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WGB: Towards a Universal Graph Benchmark

Cited by 11 publications

References 22 publications

GraphMineSuite

GraphMineSuite

Experimental analysis of distributed graph systems

LDBC graphalytics

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