Scalability and performance in distributed graph databases

Resource-Aware Scheduling of Graph Partitions for Distributed Graph Computing in Heterogeneous Clusters

This work proposes Resource-Aware Scheduling of Graph Partitions (RASGP) for heterogeneous clusters, and designs a greedy resource-aware partition scheduling method that achieves more balanced workload distribution by matching partition loads with the resource capacities of different workers.

A Survey of Large-Scale Out-of-Core Graph Processing

This survey will help researchers better understand and gain useful insights into the large and complex design space of out-of-core graph processing, including graph preprocessing, graph algorithm execution, utilization of emerging storage devices, and miscellaneous optimizations.

GRBench: A Comprehensive Benchmark Evaluation for Graph-relational Data Management

GRBench constructs a linked graph-relational schema from the real-world SciSciNet-v2 dataset and derives scalable instances through consistency-preserving subset extraction and evaluates representative system architectures through a unified, multidimensional methodology.

Sparsity-aware Fine-Grained Parallelization for Set Intersection Computations in Graph Datasets

Many real-world applications such as bioinformatics, network analysis, and recommendation systems require computing intersections of vertex neighborhood sets in graph datasets. However, variability in graph sparsity patterns makes it challenging to design a single GPU optimization strategy for such workloads that perfo...

Distributed Graph Generation Using JDM Replication

I/O-Efficient Graph Analytics on SSD via Activity-Aware Preprocessing

SSD-based graph processing systems provide a cost-efficient solution for handling ever-growing large graphs that cannot fit into the memory of a single machine. However, the mismatch between the coarse SSD access granularity (e.g., 4 KB) and the fine-grained graph vertex data causes severe read amplification and poor I...