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Databases in the Era of Memory-Centric Computing

Summary: Argues processor-centric DB designs hit a Memory Wall due to growing core vs. memory‑bandwidth imbalance and costly, underutilized memory; advocates memory‑centric computing with disaggregated memory pools for cost‑effective scaling. Demonstrates how DB operators and execution models can be reengineered to exploit memory‑centric architectures on current hardware, enabling more efficient, scalable cloud data management. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
565
Venue
CIDR
Year
2025
Pagerank
4.2897489e-05
Overall Rank
9,743 | 32.22%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
8,513 CXL Memory Performance for In-Memory Data Processing 2025 VLDB 4.4947795e-05
10,756 Selective Late Materialization in Modern Analytical Databases 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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