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GORIO: GPU-Centered Remote I/O for Graph ANNS over NVMe-oF

Gen Zhang, Wenhao Gu 2026-07-07
GPU Search Data × Design Cache Scheduling Workload

GORIO addresses the problem that graph-based approximate nearest neighbor search (ANNS) vector indexes often exceed GPU memory, and existing CPU-centered remote I/O over NVMe-oF is poorly matched to GPU graph traversal. The method keeps all query evolution, page-miss generation, and resume decisions on the GPU, using the CPU only as an NVMe-oF transport proxy, with a two-layer design for GPU-direct remote I/O and ANNS-specific scheduling. On a SIFT1M DiskANN-style workload over RDMA NVMe-oF, GORIO achieves 1.31× speedup over the state-of-the-art remote-I/O reference and 3.73× over the direct remote page-cache path. This matters because it provides a concrete GPU-centered remote I/O substrate that significantly accelerates graph ANNS for vector databases and retrieval-augmented generation services.

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Efficient Discovery of Conditional Dependencies with Desbordante

Ivan Kozhukov, Dmitry Fedoseev, Maksim Emelyanov, Artem Smola 2026-07-07
Data ×

The problem is the computationally demanding discovery of conditional functional dependencies (CFDs) from data. The method builds on the CFDFinder algorithm with algorithmic and engineering improvements, including parallelization, to produce ParCFDFinder integrated into the Desbordante data profiler. Experimental results show speedups of up to 318× (118× average) and memory reductions of up to 23× (14× average) compared to the Java-based Metanome implementation. This matters because it enables convenient CFD discovery on datasets with hundreds of thousands of rows on a commodity machine within reasonable time.

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