An Initial Evaluation of Distributed Graph Algorithms using NWGraph and HPX

2026-01-26

AI Summary

This work addresses the scalability challenges of large-scale graph processing, which is often constrained by limited single-node memory and computational resources, while existing distributed frameworks struggle with high communication latency, irregular memory access patterns, and synchronization overhead. To overcome these limitations, the authors propose a task-driven distributed graph processing approach that integrates the NWGraph graph algorithm library with the HPX asynchronous task-based runtime system. By leveraging fine-grained asynchronous execution, the method reduces synchronization costs and improves load balancing. Experimental evaluation on representative algorithms such as BFS and PageRank demonstrates that the proposed approach outperforms the distributed Boost Graph Library in BFS performance, while indicating room for further optimization in PageRank. Overall, the results validate the potential and effectiveness of asynchronous task-based models for scalable graph analytics.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Graphs are central to modeling relationships in scientific computing, data analysis, and AI/ML, but their growing scale can exceed the memory and compute capacity of single nodes, requiring distributed solutions. Existing distributed graph framework, however, face fundamental challenges: graph algorithms are latency-bound, suffer from irregular memory access, and often impose synchronization costs that limit scalability and efficiency. In this work, we present a distributed implementation of the NWGraph library integrated with the HPX runtime system. By leveraging HPX's asynchronous many-task model, our approach aims to reduce synchronization overhead, improve load balance, and provide a foundation for distributed graph analytics. We evaluate this approach using two representative algorithms: Breadth-First-Search (BFS) and (PageRank). Our initial results show that BFS achieves better performance than the distributed Boost Graph Library (BGL), while PageRank remains more challenging, with current implementation not yet outperforming BGL. These findings highlight both the promise and the open challenges of applying asynchronous task-based runtimes to graph processing, and point to opportunities for future optimizations and extensions.

Problem

• distributed graph algorithms
• latency-bound
• irregular memory access
• synchronization overhead
• scalability

Innovation

• distributed graph processing
• asynchronous many-task runtime
• NWGraph
• HPX
• load balancing