I’m planning a vector search project with implementations of exact search, HNSW, IVF, and product quantization. My benchmark plan is to use SIFT1M and GloVe, compare against exact nearest neighbors and FAISS, and measure: Recall@10 Queries per second and p50/p95 latency Index build time Memory usage I also want to sweep parameters like HNSW’s efSearch and IVF’s nprobe so the results show the tradeoffs across configurations. I expect a Python implementation to be slower than FAISS. Understanding where that gap comes from is part of the project. What benchmarking mistakes should I watch out for, especially when comparing my implementation against an optimized library?   submitted by   /u/Reasonable_Action608 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：从零构建向量搜索引擎的基准测试讨论
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/7 03:07:31