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DGAP: Efficient Dynamic Graph Analysis on Persistent Memory
SessionGraph Analytics
DescriptionDynamic graphs have grown in importance for numerous real-world applications. To accommodate this, graph frameworks, particularly their internal data structures, must support both persistent graph updates and rapid graph analysis simultaneously. Emerging persistent memory technologies, such as Optane DCPMM, offer a promising choice to simplify the designs by providing data persistence, low latency, and high IOPS together. We propose DGAP, a framework for efficient dynamic graph analysis on persistent memory. DGAP utilizes mutable Compressed Sparse Row (CSR) with new designs for persistent memory to construct the framework. Specifically, DGAP introduces a per-section edge log to reduce write amplification; a per-thread undo log to enable high-performance, crash-consistent rebalancing operations; and a data placement schema to minimize in-place updates. Our extensive evaluation results demonstrate that DGAP can achieve up to 3.2x better graph update performance and up to 3.77x better graph analysis performance compared to state-of-the-art dynamic graph frameworks for persistent memory.
Event Type
Paper
TimeThursday, 16 November 20234:30pm - 5pm MST
Location403-404
Architecture and Networks
Data Movement and Memory
Graph Algorithms and Frameworks
Performance Measurement, Modeling, and Tools
Programming Frameworks and System Software
TP
