LaGRange: Locality-aware Graph with Range Maintenance

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姓名

徐裕翔(Yu-Hsiang Hsu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(LaGRange: Locality-aware Graph with Range Maintenance)

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至系統瀏覽論文 (2027-6-1以後開放)

摘要(中)

隨著圖神經網絡（GNNs）的興起與其強大的解釋能力，對於高效存取圖
結構資料的需求重新變的重要。然而圖結構資料不規則且複雜的特性加劇了
寫入放大和讀取的延遲。儘管如此，現階段的針對圖儲存相關的研究主要集
中在靜態圖上。雖然一些研究涉及動態圖，它們討論的是在不同的時間戳下
保持該時間點下的圖，而非真正的去維護一個會隨時間而變動的單一圖資料
結構。
因此，本項研究提出了一個關注單一圖結構資料隨時間演變的局部感知
圖快閃記憶體－LaGRange的全面設計。它能夠更有效地管理圖的更新，同時
減輕寫入放大和讀取延遲。

摘要(英)

With the rise of Graph Neural Networks (GNNs), the need for efficient graph storage
has resurfaced. While retrieving data from external storage, the irregular pattern of
graph data exacerbates both write amplification and read latency. Despite this, current
research on graphs predominantly focuses on static graphs. Although some studies
address dynamic graphs, it maintains multiple versions of a graph at different
timestamps rather than a single graph evolving over time.
The study proposes Locality-aware Graph SSD with Range(LaGRange) maintenance
which focuses on a single graph that evolves over time. It’s a comprehensive design to
manage graph updates effectively, mitigating write amplification and read latency
simultaneously.

關鍵字(中)

★ 圖存取
★ 圖神經網路
★ 快閃記憶體
★ 寫入放大

關鍵字(英)

★ Graph storage
★ GNNs
★ Flash memory
★ Write amplification

論文目次

Contents
1 Introduction 1
2 Technical Background & Motivation 3
2.1 Graph Data Representation 3
2.2 Flash Translation Layer(FTL) 4
2.3 Related Work 5
2.4 Motivation 6
3 Locality-aware Graph with Range Maintenance 8
3.1 LaGRange Overview 8
3.2 Detailed Components 10
3.2.1 Update Buffer 10
3.2.2 Locality-aware Page Table 11
3.2.3 Mapping Merger 13
3.2.4 Garbage Collector 14
3.3 Overhead Analysis 15
4 Experiments 16
4.1 Environmental Settings 16
4.1.1 Graph Update 17
4.2 Evaluation Results 17
4.2.1 Write Amplification 17
4.2.2 Read Amplification 18
5 Concluding Remarks 21
Bibliography 22

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指導教授

陳增益(Tseng-Yi Chen)

審核日期

2024-7-26

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