Answering billion-scale label-constrained reachability queries within microsecond

Peng, You; Zhang, Ying; Lin, Xuemin; Qin, Lu; Zhang, Wenjie

doi:10.14778/3380750.3380753

Cited by 50 publications

(22 citation statements)

References 58 publications

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“…This time, we suppose that the second DFS follows the right child of each node before moving along the left child. In this case, we can see that we have label inclusion on the first label pair, that is, [1,5] ⊆ [1,8], but not on the second label pair, that is, [1,5]…”

Section: The Grail Approachmentioning

confidence: 99%

“…In these fields data are normally organized into a directed graph (or digraph) and the ancestor-descendant relationship of nodes is often required. Thus, given a directed graph finding whether there is a simple path leading from a vertex v to another vertex u is known as the reachability problem [1][2][3][4][5][6][7][8][9]. Reachability plays an important role in several modern algorithms since it is the underlying steps of many other applications.…”

Section: Introductionmentioning

confidence: 99%

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Graph Reachability on Parallel Many-Core Architectures

Quer

Calabrese

2020

Computation

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Many modern applications are modeled using graphs of some kind. Given a graph, reachability, that is, discovering whether there is a path between two given nodes, is a fundamental problem as well as one of the most important steps of many other algorithms. The rapid accumulation of very large graphs (up to tens of millions of vertices and edges) from a diversity of disciplines demand efficient and scalable solutions to the reachability problem. General-purpose computing has been successfully used on Graphics Processing Units (GPUs) to parallelize algorithms that present a high degree of regularity. In this paper, we extend the applicability of GPU processing to graph-based manipulation, by re-designing a simple but efficient state-of-the-art graph-labeling method, namely the GRAIL (Graph Reachability Indexing via RAndomized Interval) algorithm, to many-core CUDA-based GPUs. This algorithm firstly generates a label for each vertex of the graph, then it exploits these labels to answer reachability queries. Unfortunately, the original algorithm executes a sequence of depth-first visits which are intrinsically recursive and cannot be efficiently implemented on parallel systems. For that reason, we design an alternative approach in which a sequence of breadth-first visits substitute the original depth-first traversal to generate the labeling, and in which a high number of concurrent visits is exploited during query evaluation. The paper describes our strategy to re-design these steps, the difficulties we encountered to implement them, and the solutions adopted to overcome the main inefficiencies. To prove the validity of our approach, we compare (in terms of time and memory requirements) our GPU-based approach with the original sequential CPU-based tool. Finally, we report some hints on how to conduct further research in the area.

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Section: The Grail Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Reachability on Parallel Many-Core Architectures

Quer

Calabrese

2020

Computation

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“…This problem has been extensively studied [Valstar et al 2017;Zou et al 2014]. Recently, Peng et al [2020] proposed an indexing scheme to answer billion-scale LCR queries. Hassan et al [2016] and Rice and Tsotras [2010] proposed approaches to finding the shortest path for LCR problems.…”

Section: Indexing Schemes and Their Potential Use In Program Analysismentioning

confidence: 99%

Indexing Context-Sensitive Reachability

Shi,

Wang,

Zhang

2021

Preprint

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Many context-sensitive data flow analyses can be formulated as a variant of the all-pairs Dyck-CFL reachability problem, which, in general, is of sub-cubic time complexity and quadratic space complexity. Such high complexity significantly limits the scalability of context-sensitive data flow analysis and is not affordable for analyzing large-scale software. This paper presents Flare, a reduction from the CFL reachability problem to the conventional graph reachability problem for context-sensitive data flow analysis. This reduction allows us to benefit from recent advances in reachability indexing schemes, which often consume almost linear space for answering reachability queries in almost constant time. We have applied our reduction to a context-sensitive alias analysis and a context-sensitive information-flow analysis for C/C++ programs. Experimental results on standard benchmarks and open-source software demonstrate that we can achieve orders of magnitude speedup at the cost of only moderate space to store the indexes. The implementation of our approach is publicly available. CCS Concepts: • Mathematics of computing → Graph algorithms; • Theory of computation → Program analysis; • Software and its engineering → Automated static analysis.

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“…Next, we introduce the notations of path and subgraph on attributed graphs. [51,54,94,151,169,185,243] Shortest-path query [10,11,18,40,75,118,157,228] Regular path query [4, 5, 19, 39, 79, 103, 119, 138-140, 148, 149, 186, 192, 199, 229] Subgraph pattern query Y Exact match query [15,20,37,73,156,162,181,200,204] Approximate match query [32, 33, 46, 52, 53, 63, 64, 80, 100, 101, 111, 122, 136, 161, 175-177, 191, 197, 205, 208, 225, 232, 236, 238, 241] Extended match query [45, 55, 67, 88-90, 144, 150, 153, 155, 163, 210, 221, 226, 227, 233, 234] Aggregate query Y Graph OLAP [13,25,154,193,214,231] Egocentric aggregation [49,143,203] Similarity…”

Section: Preliminarymentioning

confidence: 99%

A survey of typical attributed graph queries

Wang

Fan

et al. 2020

World Wide Web

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Graphs are commonly used for representing complex structures such as social relationships, biological interactions, and knowledge bases. In many scenarios, graphs not only represent topological relationships but also store the attributes that denote the semantics associated with their vertices and edges, known as attributed graphs. Attributed graphs can meet demands for a wide range of applications, and thus a variety of queries on attributed graphs have been proposed. However, these diverse types of attributed graph queries have not been systematically investigated yet. In this paper, we provide an extensive survey of several typical types of attributed graph queries. We propose a taxonomy of attributed graph queries based on query inputs and outputs. We summarize the definitions of queries that fall into each category and present a fine-grained classification of queries within each category by analyzing the semantics and algorithmic motivations behind these queries. Moreover, we discuss the insights of how existing studies address the technical challenges of query processing and outline several promising future research directions.

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Answering billion-scale label-constrained reachability queries within microsecond

Cited by 50 publications

References 58 publications

Graph Reachability on Parallel Many-Core Architectures

Graph Reachability on Parallel Many-Core Architectures

Indexing Context-Sensitive Reachability

A survey of typical attributed graph queries

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