← 研踪 PaperTrailarXiv:2610.34595 · v1
Cross-Modal Data Co-Locality for Heterogeneous Storage Systems
首次提交 2026-10-26 05:52
版本更新 2026-10-26 05:52
雷达观测 2026-10-29 00:36
摘要
Distributed vector stores increasingly serve multimodal workloads whose representations are jointly accessed but independently partitioned and placed. As a result, cross-modal physical locality remains largely unoptimized. Multimodal queries can incur remote accesses and cross-tier data movement even when each modality's ANN search remains efficient. We present Coloc, a storage-management layer that makes cross-modal access locality a first-class data-layout signal. A Cross-Modal Access Graph combines semantic affinity, co-access, and temporal behavior. A movement-aware optimizer places frequently co-accessed vectors under capacity and topology constraints. A migration manager adapts placement as workloads shift by weighing locality gains against migration cost. A confidence-gated prefetcher hides remaining slow-tier latency. Coloc operates above existing ANN indexes without modifying embeddings, search, or retrieval semantics. We deploy Coloc on a multi-tier AWS testbed spanning GPU, NUMA-DRAM, NVMe, and S3. Coloc substantially reduces data movement and query latency while leaving ANN retrieval unchanged. Under a scheduled workload shift, its migration manager matches the locality of periodic global reorganization while moving 5.9x less data and completing 3.9x faster.
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v1 · 2026-10-26 05:52 · 最新观测版本
Cross-Modal Data Co-Locality for Heterogeneous Storage Systems
Distributed vector stores increasingly serve multimodal workloads whose representations are jointly accessed but independently partitioned and placed. As a result, cross-modal physical locality remains largely unoptimized. Multimodal queries can incur remote accesses and cross-tier data movement even when each modality's ANN search remains efficient. We present Coloc, a storage-management layer that makes cross-modal access locality a first-class data-layout signal. A Cross-Modal Access Graph combines semantic affinity, co-access, and temporal behavior. A movement-aware optimizer places frequently co-accessed vectors under capacity and topology constraints. A migration manager adapts placement as workloads shift by weighing locality gains against migration cost. A confidence-gated prefetcher hides remaining slow-tier latency. Coloc operates above existing ANN indexes without modifying embeddings, search, or retrieval semantics. We deploy Coloc on a multi-tier AWS testbed spanning GPU, NUMA-DRAM, NVMe, and S3. Coloc substantially reduces data movement and query latency while leaving ANN retrieval unchanged. Under a scheduled workload shift, its migration manager matches the locality of periodic global reorganization while moving 5.9x less data and completing 3.9x faster.
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