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1
Intro
2
Large-Scale Graph Processing Challenges
3
Fine-Grained Access in External Graph Processing
4
Programming Model
5
Prior External Graph Processing -- Graf Boost
6
Scalability Issue
7
Partitioning Graph Data
8
Instead, We Propose a Partitioning for Vertex Data
9
Execution Flow
10
Updating Vertex Mirrors on Different Partitions
11
Experimental Setup
12
Performance Evaluation
13
Execution Time Breakdown
14
Concluding Remarks
Description:
Explore a conference talk from FAST '19 on optimizing large-scale graph processing for emerging storage devices. Learn about the challenges of graph processing on huge datasets and how traditional solutions become inefficient with modern storage technologies like SSDs. Discover a new graph partitioning and processing framework designed to leverage the capabilities of these devices, offering up to 2X performance improvement over state-of-the-art solutions. Gain insights into fine-grained access in external graph processing, partitioning strategies for vertex data, and experimental results demonstrating the effectiveness of this approach. Understand the potential impact on applications analyzing massive datasets and the future of graph processing architectures.

Large-Scale Graph Processing on Emerging Storage Devices

USENIX
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