对比云上CPU性能，找出向量搜索性价比最高的配置。
Bang for the Buck: Vector Search on Cloud CPUs
- 测试不同云CPU在向量搜索中的表现差异。
- Graviton3在多数场景下每美元查询数最高，性价比最优。
- 不同索引类型（IVF/HNSW）对CPU选择影响显著。
向量数据库已成为支持高维向量高效查询的新一代系统，许多厂商将其作为云服务提供。然而，云中可用的CPU种类繁多，且缺乏跨CPU的向量搜索基准测试，导致用户难以选择。本研究发现，云上不同微架构的CPU在向量搜索场景中表现差异显著：例如，在float32向量的IVF索引下，AMD Zen4的查询率（QPS）接近Intel Sapphire Rapids的3倍；但在HNSW索引下，结果相反。当以每美元查询数（QP$）为衡量标准时，Graviton3在多数索引和量化设置下均优于Graviton4（表1）。本工作旨在帮助用户在部署向量搜索系统时获得最佳性价比。
原文摘要 · Abstract (English)
Vector databases have emerged as a new type of systems that support efficient querying of high-dimensional vectors. Many of these offer their database as a service in the cloud. However, the variety of available CPUs and the lack of vector search benchmarks across CPUs make it difficult for users to choose one. In this study, we show that CPU microarchitectures available in the cloud perform significantly differently across vector search scenarios. For instance, in an IVF index on float32 vectors, AMD's Zen4 gives almost 3x more queries per second (QPS) compared to Intel's Sapphire Rapids, but for HNSW indexes, the tables turn. However, when looking at the number of queries per dollar (QP$), Graviton3 is the best option for most indexes and quantization settings, even over Graviton4 (Table 1). With this work, we hope to guide users in getting the best "bang for the buck" when deploying vector search systems.
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