5 papers
The Case for TPU-Accelerated Analytical Query Processing
Yeounoh Chung, Jaehyun Ha, Helena Caminal +4
Large-scale analytical query processing requires computing substrates capable of immense distributed scale-out. Tensor Processing Units (TPUs) perfectly embody this extreme-scale a…
VADER: Filtered Vector Search with Declarative Recall
Manos Chatzakis, Duo Lu, Helena Caminal +7
Approximate filtered vector search (FVS), a core operation in many data management tasks that combine structured data with vector embeddings, exhibits increased complexity due to t…
AI Query Compilation for Unified and Optimized Execution
Yeounoh Chung, Helena Caminal, Fatma Ozcan
In this vision paper, we propose a novel architectural paradigm for accelerated AI query execution via a unified compiled execution strategy. By compiling the hybrid AI Query as a…
An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments and Analysis]
Duo Lu, Helena Caminal, Manos Chatzakis +4
Filtered Vector Search (FVS) is critical for supporting semantic search and GenAI applications in modern database systems. However, existing research most often evaluates algorithm…
Membrane: Accelerating Database Analytics with Bank-Level DRAM-PIM Filtering
Akhil Shekar, Kevin Gaffney, Martin Prammer +9
In-memory database query processing frequently involves substantial data transfers between the CPU and memory, leading to inefficiencies due to Von Neumann bottleneck. Processing-i…