GraphSSD Article Swipe
Kiran Kumar Matam
,
Gunjae Koo
,
Haipeng Zha
,
Hung‐Wei Tseng
,
Murali Annavaram
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1145/3307650.3322275
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1145/3307650.3322275
Graph analytics play a key role in a number of applications such as social networks, drug discovery, and recommendation systems. Given the large size of graphs that may exceed the capacity of the main memory, application performance is bounded by storage access time. Out-of-core graph processing frameworks try to tackle this storage access bottleneck through techniques such as graph sharding, and sub-graph partitioning. Even with these techniques, the need to access data across different graph shards or sub-graphs causes storage systems to become a significant performance hurdle. In this paper, we propose a graph semantic aware solid state drive (SSD) framework, called GraphSSD, which is a full system solution for storing, accessing, and performing graph analytics on SSDs. Rather than treating storage as a collection of blocks, GraphSSD considers graph structure while deciding on graph layout, access, and update mechanisms. GraphSSD replaces the conventional logical to physical page mapping mechanism in an SSD with a novel vertex-to-page mapping scheme and exploits the detailed knowledge of the flash properties to minimize page accesses. GraphSSD also supports efficient graph updates (vertex and edge modifications) by minimizing unnecessary page movement overheads. GraphSSD provides a simple programming interface that enables application developers to access graphs as native data in their applications, thereby simplifying the code development. It also augments the NVMe (non-volatile memory express) interface with a minimal set of changes to map the graph access APIs to appropriate storage access mechanisms.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3307650.3322275
- https://dl.acm.org/doi/pdf/10.1145/3307650.3322275
- OA Status
- gold
- Cited By
- 56
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2952348083
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2952348083Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3307650.3322275Digital Object Identifier
- Title
-
GraphSSDWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-06-14Full publication date if available
- Authors
-
Kiran Kumar Matam, Gunjae Koo, Haipeng Zha, Hung‐Wei Tseng, Murali AnnavaramList of authors in order
- Landing page
-
https://doi.org/10.1145/3307650.3322275Publisher landing page
- PDF URL
-
https://dl.acm.org/doi/pdf/10.1145/3307650.3322275Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://dl.acm.org/doi/pdf/10.1145/3307650.3322275Direct OA link when available
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Computer science, Bottleneck, Graph, Data access, Graph database, Analytics, Distributed computing, Parallel computing, Theoretical computer science, Database, Embedded systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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56Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 14, 2023: 9, 2022: 8, 2021: 11Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works_count | 40 |
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