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1. SimGQ: Simultaneously Evaluating Iterative Graph Queries

   https://doi.org/10.1109/HiPC50609.2020.00014  

   Xu, Chengshuo; Mazloumi, Abbas; Jiang, Xiaolin; Gupta, Rajiv (December 2020, IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC))

   null (Ed.)

   Graph processing frameworks are typically designed to optimize the evaluation of a single graph query. However, in practice, we often need to respond to multiple graph queries, either from different users or from a single user performing a complex analytics task. Therefore in this paper we develop SimGQ, a system that optimizes simultaneous evaluation of a group of vertex queries that originate at different source vertices (e.g., multiple shortest path queries originating at different source vertices) and delivers substantial speedups over a conventional framework that evaluates and responds to queries one by one. The performance benefits are achieved via batching and sharing. Batching fully utilizes system resources to evaluate a batch of queries and amortizes runtime overheads incurred due to fetching vertices and edge lists, synchronizing threads, and maintaining computation frontiers. Sharing dynamically identifies shared queries that substantially represent subcomputations in the evaluation of different queries in a batch, evaluates the shared queries, and then uses their results to accelerate the evaluation of all queries in the batch. With four input power-law graphs and four graph algorithms SimGQ achieves speedups of up to 45.67 × with batch sizes of up to 512 queries over the baseline implementation that evaluates the queries one by one using the state of the art Ligra system. Moreover, both batching and sharing contribute substantially to the speedups. 

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2. Ranked enumeration of join queries with projections

   https://doi.org/10.14778/3510397.3510401  

   Deep, Shaleen; Hu, Xiao; Koutris, Paraschos (January 2022, Proceedings of the VLDB Endowment)

   Join query evaluation with ordering is a fundamental data processing task in relational database management systems. SQL and custom graph query languages such as Cypher offer this functionality by allowing users to specify the order via the ORDER BY clause. In many scenarios, the users also want to see the first k results quickly (expressed by the LIMIT clause), but the value of k is not predetermined as user queries are arriving in an online fashion. Recent work has made considerable progress in identifying optimal algorithms for ranked enumeration of join queries that do not contain any projections. In this paper, we initiate the study of the problem of enumerating results in ranked order for queries with projections. Our main result shows that for any acyclic query, it is possible to obtain a near-linear (in the size of the database) delay algorithm after only a linear time preprocessing step for two important ranking functions: sum and lexicographic ordering. For a practical subset of acyclic queries known as star queries, we show an even stronger result that allows a user to obtain a smooth tradeoff between faster answering time guarantees using more preprocessing time. Our results are also extensible to queries containing cycles and unions. We also perform a comprehensive experimental evaluation to demonstrate that our algorithms, which are simple to implement, improve up to three orders of magnitude in the running time over state-of-the-art algorithms implemented within open-source RDBMS and specialized graph databases. 

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3. Low latency RNN inference with cellular batching

   https://doi.org/10.1145/3190508.3190541  

   Gao, Pin; Yu, Lingfan; Wu, Yongwei; Li, Jinyang (April 2018, EuroSys '18: Proceedings of the Thirteenth EuroSys Conference)

   Performing inference on pre-trained neural network models must meet the requirement of low-latency, which is often at odds with achieving high throughput. Existing deep learning systems use batching to improve throughput, which do not perform well when serving Recurrent Neural Networks with dynamic dataflow graphs. We propose the technique of cellular batching, which improves both the latency and throughput of RNN inference. Unlike existing systems that batch a fixed set of dataflow graphs, cellular batching makes batching decisions at the granularity of an RNN "cell" (a subgraph with shared weights) and dynamically assembles a batched cell for execution as requests join and leave the system. We implemented our approach in a system called BatchMaker. Experiments show that BatchMaker achieves much lower latency and also higher throughput than existing systems. 

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4. Learning Mixed Membership from Adjacency Graph Via Systematic Edge Query: Identifiability and Algorithm

   https://doi.org/10.1109/ICASSP39728.2021.9413541  

   Ibrahim, Shahana; Fu, Xiao (June 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   null (Ed.)

   Graph clustering is a core technique for network analysis problems, e.g., community detection. This work puts forth a node clustering approach for largely incomplete adjacency graphs. Under the considered scenario, instead of having access to the complete graph, only a small amount of queries about the graph edges can be made for node clustering. This task is well-motivated in many large-scale network analysis problems, where complete graph acquisition is prohibitively costly. Prior work tackles this problem under the setting that the nodes only admit single membership and the clusters are disjoint, yet multiple membership nodes and overlapping clusters often arise in practice. Existing approaches also rely on random edge query patterns and convex optimization-based formulations, which give rise to a number of implementation and scalability challenges. This work offers a framework that provably learns the mixed membership of nodes from overlapping clusters using limited edge information. Our method is equipped with a systematic edge query pattern, which is arguably easier to implement relative to the random counterparts in certain applications, e.g., field survey based graph analysis. A lightweight scalable algorithm is proposed, and its performance characterizations are presented. Numerical experiments are used to showcase the effectiveness of our method 

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5. Tripoline: generalized incremental graph processing via graph triangle inequality

   https://doi.org/10.1145/3447786.3456226  

   Jiang, Xiaolin; Xu, Chengshuo; Yin, Xizhe; Zhao, Zhijia; Gupta, Rajiv (April 2021, EuroSys '21: Proceedings of the Sixteenth European Conference on Computer Systems)

   null (Ed.)

   For compute-intensive iterative queries over a streaming graph, it is critical to evaluate the queries continuously and incrementally for best efficiency. However, the existing incremental graph processing requires a priori knowledge of the query (e.g., the source vertex of a vertex-specific query); otherwise, it has to fall back to the expensive full evaluation that starts from scratch. To alleviate this restriction, this work presents a principled solution to generalizing the incremental graph processing, such that queries, without their a priori knowledge, can also be evaluated incrementally. The solution centers around the concept of graph triangle inequalities, an idea inspired by the classical triangle inequality principle in the Euclidean space. Interestingly, similar principles can also be derived for many vertex-specific graph problems. These principles can help establish rigorous constraints between the evaluation of one graph query and the results of another, thus enabling reusing the latter to accelerate the former. Based on this finding, a novel streaming graph system, called Tripoline, is built which enables incremental evaluation of queries without their a priori knowledge. Built on top of a state-of-the-art shared-memory streaming graph engine (Aspen), Tripoline natively supports high-throughput low-cost graph updates. A systematic evaluation with a set of eight vertex-specific graph problems and four real-world large graphs confirms both the effectiveness of the proposed techniques and the efficiency of Tripoline. 

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