Scalable Graph Convolutional Network Training on Distributed-Memory Systems

Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts. In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs.Comment: To appear in PVLDB'2

Partitioner Selection with EASE to Optimize Distributed Graph Processing

For distributed graph processing on massive graphs, a graph is partitioned into multiple equally-sized parts which are distributed among machines in a compute cluster. In the last decade, many partitioning algorithms have been developed which differ from each other with respect to the partitioning quality, the run-time of the partitioning and the type of graph for which they work best. The plethora of graph partitioning algorithms makes it a challenging task to select a partitioner for a given scenario. Different studies exist that provide qualitative insights into the characteristics of graph partitioning algorithms that support a selection. However, in order to enable automatic selection, a quantitative prediction of the partitioning quality, the partitioning run-time and the run-time of subsequent graph processing jobs is needed. In this paper, we propose a machine learning-based approach to provide such a quantitative prediction for different types of edge partitioning algorithms and graph processing workloads. We show that training based on generated graphs achieves high accuracy, which can be further improved when using real-world data. Based on the predictions, the automatic selection reduces the end-to-end run-time on average by 11.1% compared to a random selection, by 17.4% compared to selecting the partitioner that yields the lowest cut size, and by 29.1% compared to the worst strategy, respectively. Furthermore, in 35.7% of the cases, the best strategy was selected.Comment: To appear at IEEE International Conference on Data Engineering (ICDE 2023

Optimal partitioning of directed acyclic graphs with dependent costs between clusters

Many statistical inference contexts, including Bayesian Networks (BNs), Markov processes and Hidden Markov Models (HMMS) could be supported by partitioning (i.e.~mapping) the underlying Directed Acyclic Graph (DAG) into clusters. However, optimal partitioning is challenging, especially in statistical inference as the cost to be optimised is dependent on both nodes within a cluster, and the mapping of clusters connected via parent and/or child nodes, which we call dependent clusters. We propose a novel algorithm called DCMAP for optimal cluster mapping with dependent clusters. Given an arbitrarily defined, positive cost function based on the DAG and cluster mappings, we show that DCMAP converges to find all optimal clusters, and returns near-optimal solutions along the way. Empirically, we find that the algorithm is time-efficient for a DBN model of a seagrass complex system using a computation cost function. For a 25 and 50-node DBN, the search space size was $9.91\times 10^9$ and $1.51\times10^{21}$ possible cluster mappings, respectively, but near-optimal solutions with 88\% and 72\% similarity to the optimal solution were found at iterations 170 and 865, respectively. The first optimal solution was found at iteration 934 $(\text{95\% CI } 926,971)$, and 2256 $(2150,2271)$ with a cost that was 4\% and 0.2\% of the naive heuristic cost, respectively

Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning

In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. The primary goal is to minimize communication overhead and runtime cost. However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consider is memory overhead. Real-world graphs often reach colossal sizes, making it impractical and economically unviable to load the entire graph into memory for partitioning. This is also a fundamental premise in distributed graph processing, where accommodating a graph with non-distributed systems is unattainable. Currently, existing streaming partitioning algorithms exhibit a skew-oblivious nature, yielding satisfactory partitioning results exclusively for specific graph types. In this paper, we propose a novel streaming partitioning algorithm, the Skewness-aware Vertex-cut Partitioner S5P, designed to leverage the skewness characteristics of real graphs for achieving high-quality partitioning. S5P offers high partitioning quality by segregating the graph's edge set into two subsets, head and tail sets. Following processing by a skewness-aware clustering algorithm, these two subsets subsequently undergo a Stackelberg graph game. Our extensive evaluations conducted on substantial real-world and synthetic graphs demonstrate that, in all instances, the partitioning quality of S5P surpasses that of existing streaming partitioning algorithms, operating within the same load balance constraints. For example, S5P can bring up to a 51% improvement in partitioning quality compared to the top partitioner among the baselines. Lastly, we showcase that the implementation of S5P results in up to an 81% reduction in communication cost and a 130% increase in runtime efficiency for distributed graph processing tasks on PowerGraph.Comment: This paper has been accepted by SIGMOD 202

DKWS: A Distributed System for Keyword Search on Massive Graphs (Complete Version)

Due to the unstructuredness and the lack of schemas of graphs, such as knowledge graphs, social networks, and RDF graphs, keyword search for querying such graphs has been proposed. As graphs have become voluminous, large-scale distributed processing has attracted much interest from the database research community. While there have been several distributed systems, distributed querying techniques for keyword search are still limited. This paper proposes a novel distributed keyword search system called \DKWS. First, we \revise{present} a {\em monotonic} property with keyword search algorithms that guarantees correct parallelization. Second, we present a keyword search algorithm as monotonic backward and forward search phases. Moreover, we propose new tight bounds for pruning nodes being searched. Third, we propose a {\em notify-push} paradigm and \PINE {\em programming model} of \DKWS. The notify-push paradigm allows {\em asynchronously} exchanging the upper bounds of matches across the workers and the coordinator in \DKWS. The \PINE programming model naturally fits keyword search algorithms, as they have distinguished phases, to allow {\em preemptive} searches to mitigate staleness in a distributed system. Finally, we investigate the performance and effectiveness of \DKWS through experiments using real-world datasets. We find that \DKWS is up to two orders of magnitude faster than related techniques, and its communication costs are $7.6$ times smaller than those of other techniques

The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey

Graph Neural Networks (GNNs) are an emerging research field. This specialized Deep Neural Network (DNN) architecture is capable of processing graph structured data and bridges the gap between graph processing and Deep Learning (DL). As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology and chemistry. With the rapid growing size of real world graphs, the need for efficient and scalable GNN training solutions has come. Consequently, many works proposing GNN systems have emerged throughout the past few years. However, there is an acute lack of overview, categorization and comparison of such systems. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions. In addition, we establish connections between GNN systems, graph processing systems and DL systems.Comment: Accepted at ACM Computing Survey

Distributed Graph Neural Network Training: A Survey

Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is able to provide abundant computing resources. However, the dependency of graph structure increases the difficulty of achieving high-efficiency distributed GNN training, which suffers from the massive communication and workload imbalance. In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed. Yet, there is a lack of systematic review on the optimization techniques for the distributed execution of GNN training. In this survey, we analyze three major challenges in distributed GNN training that are massive feature communication, the loss of model accuracy and workload imbalance. Then we introduce a new taxonomy for the optimization techniques in distributed GNN training that address the above challenges. The new taxonomy classifies existing techniques into four categories that are GNN data partition, GNN batch generation, GNN execution model, and GNN communication protocol. We carefully discuss the techniques in each category. In the end, we summarize existing distributed GNN systems for multi-GPUs, GPU-clusters and CPU-clusters, respectively, and give a discussion about the future direction on distributed GNN training

Scalable graph convolutional network training on distributed-memory systems

Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts. In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs

Efficient Path Enumeration and Structural Clustering on Massive Graphs

Graph analysis plays a crucial role in understanding the relationships and structures within complex systems. This thesis focuses on addressing fundamental problems in graph analysis, including hop-constrained s-t simple path (HC-s-t path) enumeration, batch HC-s-t path query processing, and graph structural clustering (SCAN). The objective is to develop efficient and scalable distributed algorithms to tackle these challenges, particularly in the context of billion-scale graphs. We first explore the problem of HC-s-t path enumeration. Existing solutions for this problem often suffer from inefficiency and scalability limitations, especially when dealing with billion-scale graphs. To overcome these drawbacks, we propose a novel hybrid search paradigm specifically tailored for HC-s-t path enumeration. This paradigm combines different search strategies to effectively explore the solution space. Building upon this paradigm, we devise a distributed enumeration algorithm that follows a divide-and-conquer strategy, incorporates fruitless exploration pruning, and optimizes memory consumption. Experimental evaluations on various datasets demonstrate that our algorithm achieves a significant speedup compared to existing solutions, even on datasets where they encounter out-of-memory issues. Secondly, we address the problem of batch HC-s-t path query processing. In real-world scenarios, it is common to issue multiple HC-s-t path queries simultaneously and process them as a batch. However, existing solutions often focus on optimizing the processing performance of individual queries, disregarding the benefits of processing queries concurrently. To bridge this gap, we propose the concept of HC-s path queries, which captures the common computation among different queries. We design a two-phase HC-s path query detection algorithm to identify the shared computation for a given set of HC-s-t path queries. Based on the detected HC-s path queries, we develop an efficient HC-s-t path enumeration algorithm that effectively shares the common computation. Extensive experiments on diverse datasets validate the efficiency and scalability of our algorithm for processing multiple HC-s-t path queries concurrently. Thirdly, we investigate the problem of graph structural clustering (SCAN) in billion-scale graphs. Existing distributed solutions for SCAN often lack efficiency or suffer from high memory consumption, making them impractical for large-scale graphs. To overcome these challenges, we propose a fine-grained clustering framework specifically tailored for SCAN. This framework enables effective identification of cohesive subgroups within a graph. Building upon this framework, we devise a distributed SCAN algorithm that minimizes communication overhead and reduces memory consumption throughout the execution. We also incorporate an effective workload balance mechanism that dynamically adjusts to handle skewed workloads. Experimental evaluations on real-world graphs demonstrate the efficiency and scalability of our proposed algorithm. Overall, this thesis contributes novel distributed algorithms for HC-s-t path enumeration, batch HC-s-t path query processing, and graph structural clustering. The proposed algorithms address the efficiency and scalability challenges in graph analysis, particularly on billion-scale graphs. Extensive experimental evaluations validate the superiority of our algorithms compared to existing solutions, enabling efficient and scalable graph analysis in complex systems