cs.AI · 2606.01840 Copy arXiv ID · Jun 1, 2026 Save Evaluation of Baseline Methods for IDD-based SSD External Memory Search Authors: Yuki Suzuki , Alex Fukunaga
Organizations: The University of Tokyo
Abstract Many difficult search problems cannot be solved by algorithms such as A* using only RAM. Search algorithms which use external memory such as SSDs and HDDs with much higher capacity than RAM have been proposed in previous work, but previous work has focused on delayed duplicate detection approaches, as well as complex immediate duplicate detection (IDD) methods, and relatively simple methods for IDD have not been systematically studied. In addition, the effect of OS-level mechanisms for managing and speeding up accesses to external memory, such as page caches, has not been studied. This paper addresses these gaps in the literature by evaluating and analyzing the performance of simple baseline approaches for IDD-based A*.
Explore similar work Apr 22, 2026 · Deevashwer Rathee, Jean-Luc Watson, Zirui Neil Zhao +2 Approximate Nearest-Neighbor Search Search Algorithms
May 27, 2026 · Yonatan Vernik, Alexander Tuisov, Alexander Shleyfman Planning Methods Rescue
Apr 22, 2026 · cs.CR J/K move · Enter open · S save
Deevashwer Rathee, Jean-Luc Watson, Zirui Neil Zhao, G. Edward Suh +1
UC Berkeley, NVIDIA · NVIDIA · UT Austin, NVIDIA · UC Berkeley
Approximate nearest neighbor (ANN) search in AI systems increasingly handles sensitive data on third-party infrastructure. Trusted execution environments (TEEs) offer protection, but cost-efficient deployments must rely on external SSDs, which leaks user queries through disk access patterns to the host. Oblivious RAM (ORAM) can hide these access patterns but at a high cost; when paired with existing disk-based ANN search techniques, it makes poor use of SSD resources, yielding high latency and poor cost-efficiency. The core challenge for efficient oblivious ANN search over SSDs is balancing both bandwidth and access count. The state-of-the-art ORAM-ANN design minimizes access count at the ANN level and bandwidth at the ORAM level, each trading-off the other, leaving the combined system with both resources overutilized. We propose inverting this design, minimizing bandwidth consumption in the ANN layer and access count in the ORAM layer, since each component is better suited for its new role: ANN's inherent approximation allows for more bandwidth efficiency, while ORAM has no fundamental lower bounds on access count (as opposed to bandwidth). To this end, we propose a cost-efficient approach, Onyx, with two new co-designed components: Onyx-ANNS introduces a compact intermediate representation that proactively prunes the majority of bandwidth-intensive accesses without hurting recall, and Onyx-ORAM proposes a locality-aware shallow tree design that reduces access count while remaining compatible with bandwidth-efficient ORAM techniques. Compared to the state-of-the-art oblivious ANN search system, Onyx achieves
1.7 − 9.9 × 1.7-9.9\times 1.7 − 9.9 × lower cost and
2.3 − 12.3 × 2.3-12.3\times 2.3 − 12.3 × lower latency.