Presentation
Bubble: Towards Scalable Evolving Graph Processing via Mini-Batch Sorting
DescriptionEvolving graph processing has become a critical component in various applications and is gaining increasing attention. However, existing evolving graph systems suffer from cache contention and workload imbalance between threads, which leads to poor scalability and performance degradation on modern multi-core computers.
In this paper, we introduce Bubble, a high-performance evolving graph processing engine designed with high scalability. By employing a novel graph format based on mini-batch sorting, Bubble utilizes the private caches of modern processor cores, achieving near-linear scalability in graph ingestion, while maintaining high performance for graph analytics. Compared with state-of-the-art systems, including LSGraph, GraphOne, and XPGraph, Bubble achieves 2.46×-8.86× higher throughput in graph ingestion and 0.77×-3.29× speedups when running common graph algorithms.
In this paper, we introduce Bubble, a high-performance evolving graph processing engine designed with high scalability. By employing a novel graph format based on mini-batch sorting, Bubble utilizes the private caches of modern processor cores, achieving near-linear scalability in graph ingestion, while maintaining high performance for graph analytics. Compared with state-of-the-art systems, including LSGraph, GraphOne, and XPGraph, Bubble achieves 2.46×-8.86× higher throughput in graph ingestion and 0.77×-3.29× speedups when running common graph algorithms.
Event Type
Paper
TimeWednesday, 19 November 20254:37pm - 5:00pm CST
Location275
Applications