A Unified Graph Framework for Storage-Compute Coupled Cluster and High-Density Computing Cluster

In this paper, we present a novel unified framework that seamlessly integrates distributed computing and high-density graph computing. Our approach leverages a hybrid architecture that combines the strengths of both paradigms, enabling efficient graph traversal and computation while ensuring scalability and flexibility. The key contributions of our work include: 1. A distributed graph storage and partitioning strategy that maximizes data locality and minimizes cross-machine communication overhead. 2. A high-density graph computing cluster optimized for memory-efficient operations and parallel execution. 3. Query optimization, include partition pruning, network optimization, parallel processing, and predicate pushdown. 4. Two approaches for implementing Pregel for graph-wide algorithms: a shard-based SCC (Storage-Compute Coupling) implementation and an HDC (High-Density Computing) based implementation. Our unified framework addresses the critical challenges faced by contemporary graph databases and analytics frameworks, providing a robust and versatile solution tailored to the diverse requirements of modern graph-based applications.