Design and Implementation of the HPCS Graph Analysis Benchmark on Symmetric Multiprocessors

Bader, David A.; Madduri, Kamesh

doi:10.1007/11602569_48

Cited by 121 publications

(62 citation statements)

References 15 publications

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“…Request permissions from permissions@acm.org. Recent graph benchmarking initiatives focus on three key areas: (1) transactional workloads consisting of interactive read and update queries [4,6,12], (2) graph analysis algorithms [5,11,19,23], and (3) inferencing/matching on semantic data [1,17,22,30,32].…”

Section: Introductionmentioning

confidence: 99%

An early look at the LDBC social network benchmark's business intelligence workload

Szárnyas

Prat-Pérez²,

Averbuch

et al. 2018

Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &Amp; Systems (GRADES) and

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In this short paper, we provide an early look at the LDBC Social Network Benchmark's Business Intelligence (BI) workload which tests graph data management systems on a graph business analytics workload. Its queries involve complex aggregations and navigations (joins) that touch large data volumes, which is typical in BI workloads, yet they depend heavily on graph functionality such as connectivity tests and path finding. We outline the motivation for this new benchmark, which we derived from many interactions with the graph database industry and its users, and situate it in a scenario of social network analysis. The workload was designed by taking into account technical "chokepoints" identified by database system architects from academia and industry, which we also describe and map to the queries. We present reference implementations in openCypher, PGQL, SPARQL, and SQL, and preliminary results of SNB BI on a number of graph data management systems.

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Section: Introductionmentioning

confidence: 99%

An early look at the LDBC social network benchmark's business intelligence workload

Szárnyas

Prat-Pérez²,

Averbuch

et al. 2018

Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &Amp; Systems (GRADES) and

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“…The evaluation on real datasets can show the efficiency of the proposed algorithms in real applications, while the evaluation on synthetic datasets can easily demonstrate their sensitivity to varying graph characteristics. Two synthetic datasets are generated by the SSCA#2 generator [2] and the power-law generator R-MAT [3] respectively. A SSCA#2 graph is directed, and made up of random-sized cliques, with a hierarchical interclique distribution of edges based on a distance metric.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Efficient Maximal Clique Enumeration Over Graph Data

et al. 2016

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In a wide variety of emerging data-intensive applications, such as social network analysis, Web document clustering, entity resolution, and detection of consistently coexpressed genes in systems biology, the detection of dense subgraphs (cliques) is an essential component. Unfortunately, this problem is NP-Complete and thus computationally intensive at scale-hence there is a need for efficient processing, as well as the techniques for distributing the computation across multiple machines such that the computation, which is too time-consuming on a single machine, can be efficiently performed on a machine cluster given that it is large enough. In this paper, we propose a new algorithm (called GP) for maximal clique enumeration. It identifies cliques by the operation of binary graph partitioning, which iteratively divides a graph until each task is sufficiently small to be processed in parallel. Given a connected graph G ¼ ðV; EÞ, the GP algorithm has a space complexity of O(|E|) and a time complexity of OðjEjlðGÞÞ, where lðGÞ represents the number of different cliques existing in G. We also present a hybrid algorithm, which can effectively leverage the advantages of both the GP algorithm and the classical Bron-and-Kerbosch (BK) algorithm. Then, we develop corresponding parallel solutions based on the GP and hybrid algorithms. Finally, we evaluate the performance of the proposed solutions on real and synthetic graph data. Our extensive experiments show that in both centralized and parallel setting, our proposed GP and hybrid approaches achieve considerably better performance than the state-ofthe-art BK approach. Our parallel solutions are implemented and evaluated on MapReduce, a popular shared-nothing parallel framework, but can easily generalize to other sharednothing or shared-memory parallel frameworks.

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“…Our first graph is an artificial R-MAT [32], [33] graph derived by sampling from a perturbed Kronecker product. R-MAT graphs are scale-free and reflect many properties of real social networks but are known not to possess significant community structure [36].…”

Section: B Test Graphsmentioning

confidence: 99%

Scalable Multi-threaded Community Detection in Social Networks

Riedy

Bader

Meyerhenke

2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

Self Cite

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Abstract-The volume of existing graphstructured data requires improved parallel tools and algorithms. Finding communities, smaller subgraphs densely connected within the subgraph than to the rest of the graph, plays a role both in developing new parallel algorithms as well as opening smaller portions of the data to current analysis tools. We improve performance of our parallel community detection algorithm by 20% on the massively multithreaded Cray XMT, evaluate its performance on the next-generation Cray XMT2, and extend its reach to Intel-based platforms with OpenMP. To our knowledge, not only is this the first massively parallel community detection algorithm but also the only such algorithm that achieves excellent performance and good parallel scalability across all these platforms. Our implementation analyzes a moderate sized graph with 105 million vertices and 3.3 billion edges in around 500 seconds on a four processor, 80-logical-core Intel-based system and 1100 seconds on a 64-processor Cray XMT2.

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Design and Implementation of the HPCS Graph Analysis Benchmark on Symmetric Multiprocessors

Cited by 121 publications

References 15 publications

An early look at the LDBC social network benchmark's business intelligence workload

An early look at the LDBC social network benchmark's business intelligence workload

Efficient Maximal Clique Enumeration Over Graph Data

Scalable Multi-threaded Community Detection in Social Networks

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