From 106c9e6ca62390975b4a312cc516b0758815b199 Mon Sep 17 00:00:00 2001 From: ccyyy1023 <2637034749@qq.com> Date: Sun, 20 Sep 2026 16:29:43 +0800 Subject: [PATCH] feat: add CUDA GPU vector retrieval project --- 09_vector_retrieval/ccyyy1023/.gitignore | 5 + 09_vector_retrieval/ccyyy1023/CMakeLists.txt | 39 +++ ...3\347\273\223\346\212\245\345\221\212.pdf" | Bin 0 -> 246794 bytes 09_vector_retrieval/ccyyy1023/README.md | 155 +++++++++++ 09_vector_retrieval/ccyyy1023/REPORT.md | 127 +++++++++ .../ccyyy1023/configs/exact.conf | 6 + .../ccyyy1023/configs/ivf_flat.conf | 6 + .../ccyyy1023/include/gvs/io.hpp | 34 +++ .../ccyyy1023/include/gvs/search.hpp | 78 ++++++ .../ccyyy1023/results/SMOKE_SUMMARY.md | 5 + .../ccyyy1023/results/SUMMARY.md | 14 + .../ccyyy1023/results/batch_1.json | 8 + .../ccyyy1023/results/batch_32.json | 8 + .../ccyyy1023/results/environment.txt | 7 + .../ccyyy1023/results/index_build.txt | 1 + .../ccyyy1023/results/k_1.json | 8 + .../ccyyy1023/results/k_10.json | 8 + .../ccyyy1023/results/k_100.json | 8 + .../ccyyy1023/results/k_50.json | 8 + .../ccyyy1023/results/nprobe_1.json | 8 + .../ccyyy1023/results/nprobe_4.json | 8 + .../ccyyy1023/results/nprobe_64.json | 8 + .../ccyyy1023/results/smoke.json | 8 + .../ccyyy1023/scripts/check_submission.py | 50 ++++ .../scripts/run_required_benchmarks.sh | 50 ++++ .../ccyyy1023/scripts/run_smoke.sh | 26 ++ .../ccyyy1023/scripts/summarize_results.py | 59 ++++ .../ccyyy1023/src/cpu_search.cpp | 73 +++++ 09_vector_retrieval/ccyyy1023/src/file_io.cpp | 127 +++++++++ .../ccyyy1023/src/gpu_search.cu | 227 +++++++++++++++ .../ccyyy1023/src/ivf_flat.cpp | 214 +++++++++++++++ 09_vector_retrieval/ccyyy1023/src/main.cpp | 259 ++++++++++++++++++ .../ccyyy1023/tests/test_search.cpp | 72 +++++ 33 files changed, 1714 insertions(+) create mode 100644 09_vector_retrieval/ccyyy1023/.gitignore create mode 100644 09_vector_retrieval/ccyyy1023/CMakeLists.txt create mode 100644 "09_vector_retrieval/ccyyy1023/GPU\345\220\221\351\207\217\346\243\200\347\264\242\345\274\225\346\223\216_\346\200\273\347\273\223\346\212\245\345\221\212.pdf" create mode 100644 09_vector_retrieval/ccyyy1023/README.md create mode 100644 09_vector_retrieval/ccyyy1023/REPORT.md create mode 100644 09_vector_retrieval/ccyyy1023/configs/exact.conf create mode 100644 09_vector_retrieval/ccyyy1023/configs/ivf_flat.conf create mode 100644 09_vector_retrieval/ccyyy1023/include/gvs/io.hpp create mode 100644 09_vector_retrieval/ccyyy1023/include/gvs/search.hpp create mode 100644 09_vector_retrieval/ccyyy1023/results/SMOKE_SUMMARY.md create mode 100644 09_vector_retrieval/ccyyy1023/results/SUMMARY.md create mode 100644 09_vector_retrieval/ccyyy1023/results/batch_1.json create mode 100644 09_vector_retrieval/ccyyy1023/results/batch_32.json create mode 100644 09_vector_retrieval/ccyyy1023/results/environment.txt create mode 100644 09_vector_retrieval/ccyyy1023/results/index_build.txt create mode 100644 09_vector_retrieval/ccyyy1023/results/k_1.json create mode 100644 09_vector_retrieval/ccyyy1023/results/k_10.json create mode 100644 09_vector_retrieval/ccyyy1023/results/k_100.json create mode 100644 09_vector_retrieval/ccyyy1023/results/k_50.json create mode 100644 09_vector_retrieval/ccyyy1023/results/nprobe_1.json create mode 100644 09_vector_retrieval/ccyyy1023/results/nprobe_4.json create mode 100644 09_vector_retrieval/ccyyy1023/results/nprobe_64.json create mode 100644 09_vector_retrieval/ccyyy1023/results/smoke.json create mode 100644 09_vector_retrieval/ccyyy1023/scripts/check_submission.py create mode 100644 09_vector_retrieval/ccyyy1023/scripts/run_required_benchmarks.sh create mode 100644 09_vector_retrieval/ccyyy1023/scripts/run_smoke.sh create mode 100644 09_vector_retrieval/ccyyy1023/scripts/summarize_results.py create mode 100644 09_vector_retrieval/ccyyy1023/src/cpu_search.cpp create mode 100644 09_vector_retrieval/ccyyy1023/src/file_io.cpp create mode 100644 09_vector_retrieval/ccyyy1023/src/gpu_search.cu create mode 100644 09_vector_retrieval/ccyyy1023/src/ivf_flat.cpp create mode 100644 09_vector_retrieval/ccyyy1023/src/main.cpp create mode 100644 09_vector_retrieval/ccyyy1023/tests/test_search.cpp diff --git a/09_vector_retrieval/ccyyy1023/.gitignore b/09_vector_retrieval/ccyyy1023/.gitignore new file mode 100644 index 00000000..e862c7fb --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/.gitignore @@ -0,0 +1,5 @@ +/build/ +/data/ +gvs_test_index.bin +*.tmp + diff --git a/09_vector_retrieval/ccyyy1023/CMakeLists.txt b/09_vector_retrieval/ccyyy1023/CMakeLists.txt new file mode 100644 index 00000000..bd874bac --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/CMakeLists.txt @@ -0,0 +1,39 @@ +cmake_minimum_required(VERSION 3.20) +project(gpu_vector_retrieval LANGUAGES CXX CUDA) + +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_CUDA_STANDARD 17) +set(CMAKE_CUDA_STANDARD_REQUIRED ON) + +option(GVS_BUILD_TESTS "Build correctness tests" ON) + +find_package(CUDAToolkit REQUIRED) +find_package(OpenMP) + +add_library(gvs + src/cpu_search.cpp + src/file_io.cpp + src/ivf_flat.cpp + src/gpu_search.cu +) +target_include_directories(gvs PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include) +target_link_libraries(gvs PUBLIC CUDA::cudart) +if(OpenMP_CXX_FOUND) + target_link_libraries(gvs PUBLIC OpenMP::OpenMP_CXX) +endif() +set_target_properties(gvs PROPERTIES CUDA_SEPARABLE_COMPILATION ON) +target_compile_options(gvs PRIVATE + $<$:-O3> + $<$:-O3;--use_fast_math> +) + +add_executable(vector_search src/main.cpp) +target_link_libraries(vector_search PRIVATE gvs) + +if(GVS_BUILD_TESTS) + enable_testing() + add_executable(gvs_tests tests/test_search.cpp) + target_link_libraries(gvs_tests PRIVATE gvs) + add_test(NAME gvs_correctness COMMAND gvs_tests) +endif() diff --git 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z$QS&7YC-(e`z3!%I$N!O_|HP=QV2i(pC$zS>n}uk1!*B|D8T&oFJzmh-+%m5F;jfm gJ^%OZ?@sn023gXod5s; literal 0 HcmV?d00001 diff --git a/09_vector_retrieval/ccyyy1023/README.md b/09_vector_retrieval/ccyyy1023/README.md new file mode 100644 index 00000000..2f6ea01f --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/README.md @@ -0,0 +1,155 @@ +# GPU 向量检索引擎（CUDA） + +本项目实现了一个可独立构建的简化 GPU 向量检索引擎，面向训练营第九题。当前实现包含 CPU 精确检索参考、CUDA 批量精确检索、L2/内积、排序后的 Top-K、IVF-Flat、索引持久化、正确性测试，以及 QPS、P50/P99、recall@K、平均分数误差、显存工作区和 CPU/GPU 加速比的采集框架。 + +> 真实性声明：`results/*.json` 来自 2026-09-20 在 RTX 4090 24GB、CUDA 11.8 上的真实运行。百万规模正式矩阵覆盖 1,000,000 条 128 维向量和 1000 个查询；原始 JSON、环境记录和汇总表均随项目提交。 + +## 1. 已完成范围 + +- CPU FP32 精确检索 baseline。 +- CUDA FP32 精确检索，支持 `l2` 和 `inner_product`。 +- 批量查询，`batch_size` 可配置。 +- 任意正整数 `top_k`，已提供 K=1/10/50/100 的基准脚本。 +- Top-K 按 L2 从小到大或内积从大到小输出。 +- IVF-Flat：CPU mini-batch Lloyd 聚类、倒排桶、`nprobe` 探测、数据库常驻 GPU、按候选 ID 计算距离。 +- IVF 索引保存/加载，查询时不需要重新聚类。 +- 固定版本的二进制向量、查询、结果和 IVF 索引格式。 +- CPU/GPU 一致性测试、索引 round-trip 测试。 +- JSON 性能/质量日志与汇总脚本。 + +实现边界：基础版本仅支持 FP32；IVF 聚类、coarse search 和最终 Top-K 归并在 CPU 上完成，主要距离计算在 GPU 上完成。这个版本不声称实现了 GPU Top-K、FP16、IVF-PQ 或国产 GPU 适配。 + +## 2. 环境 + +- NVIDIA GPU +- CUDA Toolkit 11.0 或更新版本（需要 `nvcc`） +- CMake 3.20+ +- 支持 C++17 的编译器 +- Python 3.8+（仅用于日志汇总和包检查） + +推荐 Ubuntu 22.04 + CUDA 12.x。Windows 也可使用 Visual Studio 2022 的 C++ 工具链构建。 + +## 3. 构建与测试 + +```bash +cmake -S . -B build -DCMAKE_BUILD_TYPE=Release +cmake --build build -j +ctest --test-dir build --output-on-failure +``` + +测试同时覆盖 L2、inner product、批量精确 Top-K、CPU/GPU 一致性和 IVF 索引保存/加载。 + +## 4. 五分钟 smoke test + +```bash +bash scripts/run_smoke.sh +``` + +等价的手动命令： + +```bash +mkdir -p data results +./build/vector_search generate \ + --db data/small_db.bin --queries data/small_queries.bin \ + --n 10000 --nq 100 --dim 128 --metric l2 --seed 42 + +./build/vector_search build \ + --db data/small_db.bin --index data/small_ivf.bin \ + --nlist 64 --iterations 8 --seed 42 + +./build/vector_search benchmark \ + --db data/small_db.bin --queries data/small_queries.bin \ + --index data/small_ivf.bin --log results/smoke.json \ + --k 10 --batch 32 --nlist 64 --nprobe 8 \ + --cpu-query-limit 20 --latency-query-limit 50 +``` + +## 5. 题目要求规模测试 + +以下脚本生成 `1,000,000 x 128` 向量库和 1000 个查询，测试 K=1/10/50/100、多个 batch size 和多个 nprobe。数据库约 488 MiB，索引还会额外保存一份向量数据，请预留至少 2 GiB 磁盘空间。 + +```bash +bash scripts/run_required_benchmarks.sh +python3 scripts/summarize_results.py results results/SUMMARY.md +``` + +正式测试前请关闭其他 GPU 任务，并记录：GPU 型号、驱动、CUDA 版本、CPU 型号和编译命令。脚本不会伪造或预填任何数字。 + +## 6. 文件模式 + +`configs/exact.conf` 和 `configs/ivf_flat.conf` 遵循题面参数形式： + +```text +top_k = 10 +search_mode = "ivf_flat" +batch_size = 128 +nlist = 256 +nprobe = 16 +``` + +构建一次索引并从文件加载查询： + +```bash +./build/vector_search build --db data/db.bin --index data/index.bin --nlist 256 +./build/vector_search search \ + --db data/db.bin --queries data/queries.bin \ + --params configs/ivf_flat.conf --index data/index.bin \ + --output results/search_results.bin +``` + +精确检索不使用 `--index`，但命令行解析允许保留该参数： + +```bash +./build/vector_search search \ + --db data/db.bin --queries data/queries.bin \ + --params configs/exact.conf --output results/exact_results.bin +``` + +## 7. 二进制格式 + +所有整数均为 little-endian，向量按行主序存储。 + +- 数据库：8 字节 magic `GVSDB01\0`，`uint64 n`，`uint32 dim`，`uint32 dtype`，`uint32 metric`，随后 FP32 数据。`dtype=1`，`metric=0/1` 分别表示 L2/inner product。 +- 查询：8 字节 magic `GVSQR01\0`，`uint64 nq`，`uint32 dim`，`uint32 dtype`，随后 FP32 数据。 +- 结果：8 字节 magic `GVSRS01\0`，`uint64 nq`，`uint32 k`，随后每个结果的 `int64 id + float score`。 +- IVF 索引：版本化 magic、形状/度量、聚类中心、原始 FP32 向量和倒排表。加载时会验证 header、总 ID 数和 ID 范围。 + +## 8. 指标定义 + +- `QPS = 查询数 / 查询时间`。精确检索计时包含 query H2D、CUDA 距离计算、distance D2H 和 CPU Top-K，不包含数据库首次上传；IVF 计时还包含 CPU coarse search、候选 ID 构造和一次数据库 H2D，索引文件加载不计入。 +- P50/P99：逐 query、batch=1 的端到端延迟分位数。 +- `recall@K`：近似结果与 GPU 精确结果 Top-K ID 集合的平均交集比例。 +- `mean_rank_score_error`：近似结果和精确结果在相同排名位置的距离/相似度绝对误差平均值。 +- `workspace_mib`：程序显式分配的数据库、query 和 distance buffer 总量估计，不包含 CUDA context/驱动开销。 +- CPU/GPU speedup：CPU 单 query 平均时间除以 GPU 批量查询的单 query 平均时间。CPU 默认只测前 20 个 query，并在日志中明确记录样本数。 + +## 9. 设计说明 + +精确检索 kernel 使用二维 grid：x 维覆盖数据库向量，y 维覆盖 batch 内 query。每个线程顺序归约一个向量的 128 个维度，行主序布局使相邻线程访问相邻向量。L2 使用 `fmaf(diff, diff, acc)`，内积内部取负数以复用升序 Top-K。 + +IVF-Flat 用 mini-batch Lloyd 方法训练聚类中心，随后将每条向量分配到最近中心。查询先计算 coarse centroid 距离，选择 `nprobe` 个桶，只打包这些桶中的 FP32 原向量并调用同一 CUDA 精确距离路径。索引文件保存中心、向量与倒排表，因此查询过程无需重新训练或分桶。 + +当前 Top-K 在 CPU 使用 `std::partial_sort`，这样实现简单且可作为后续 GPU Top-K 的稳定参照。性能报告应如实讨论 D2H 距离传输和 CPU 归并的开销。 + +## 10. 目录 + +```text +. +├── CMakeLists.txt +├── README.md +├── REPORT.md +├── configs/ +├── include/gvs/ +├── results/ +├── scripts/ +├── src/ +└── tests/ +``` + +## 11. 提交 + +1. Fork `InfiniTensor/Learning-CUDA`。 +2. 基于 `2026-summer-project` 分支工作。 +3. 将本目录完整复制到 `09_vector_retrieval/ccyyy1023/`（若实际 ID 不同，只改这一层目录名）。 +4. 保留 `results/` 中的真实日志；如换硬件复测，请用新日志替换并同步更新 `REPORT.md`。 +5. Commit、push，然后向上游 `2026-summer-project` 发起 PR；官网提交 fork 仓库链接和 commit 链接。 diff --git a/09_vector_retrieval/ccyyy1023/REPORT.md b/09_vector_retrieval/ccyyy1023/REPORT.md new file mode 100644 index 00000000..b283b592 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/REPORT.md @@ -0,0 +1,127 @@ +# GPU 向量检索引擎（CUDA）总结报告 + +> 状态：已在 NVIDIA GeForce RTX 4090 24GB 上完成 Release 构建、正确性测试和百万规模性能矩阵。表中数字均来自 `results/*.json`，未使用估算或示例值。 + +## 1. 问题描述 + +本项目面向 RAG、推荐系统和多模态检索中的 Top-K 向量搜索。给定 N 条 D 维数据库向量和一批查询向量，系统返回 L2 距离最小或内积最大的 K 条记录。精确搜索复杂度为 O(ND)，可以作为正确性基准；IVF-Flat 通过 coarse quantization 只访问部分倒排桶，以召回率换取吞吐和延迟。 + +## 2. 完成内容 + +项目实现 CPU 精确 baseline、CUDA 精确距离计算、批量查询、排序 Top-K、L2/内积、IVF-Flat 索引、索引保存与加载、二进制输入输出、正确性测试和统一指标日志。支持任意正整数 K，正式脚本覆盖 K=1、10、50、100。 + +基础实现使用 FP32。IVF 聚类、coarse search 和最终 Top-K 在 CPU 完成，数据库常驻 GPU，候选 ID 上传后由 CUDA kernel 读取原始向量并计算距离。本项目未实现 IVF-PQ、GPU Top-K、FP16 或国产 GPU 适配，不将这些未完成项写成已完成。 + +## 3. 数据布局与文件格式 + +数据库和查询采用 row-major 连续 FP32 数组。相邻 CUDA 线程处理相邻向量；固定维度循环中，相邻线程的全局内存访问存在跨向量步长。此布局与题目文件定义一致，容易验证和落盘，但不是所有维度下最理想的 coalescing 布局。后续可尝试转置或 tiled layout。 + +文件带 8 字节 magic 和版本号，并固定整数字段宽度。IVF 索引文件保存聚类中心、原始向量和倒排列表；加载时校验形状、总 ID 数和 ID 范围，避免每次查询重新训练。 + +## 4. 精确检索算法 + +CPU 参考实现逐 query 计算所有距离，并用 `std::partial_sort` 得到排序 Top-K。CUDA kernel 使用二维 grid，一维覆盖 N 条候选，一维覆盖 batch。一个线程累加一个 query-vector pair 的 D 个维度，L2 和 inner product 均使用 `fmaf`。内积取负后复用升序排序逻辑，输出时恢复真实相似度。 + +GPU 结果将距离复制回主机，在 CPU 完成 Top-K。这是当前版本最明确的性能边界：距离阶段在 GPU，但 distance D2H 与 CPU partial sort 仍可能成为瓶颈。 + +## 5. IVF-Flat + +### 5.1 建索引 + +1. 从数据库随机选取初始中心。 +2. 使用有固定上限的 mini-batch Lloyd 迭代更新中心，避免百万规模训练遍历成本失控。 +3. 将全部向量分配给最近中心，建立倒排桶。 +4. 保存中心、数据库向量和每个桶的原始 ID。 + +### 5.2 查询 + +1. 在 CPU 计算 query 与所有 coarse centroid 的距离。 +2. 选取最近的 `nprobe` 个桶。 +3. 仅把候选 ID 传入 GPU，数据库在一次 IVF 查询过程中常驻显存。 +4. CUDA kernel 按 ID 读取原始向量并计算精确距离。 +5. CPU Top-K 归并并映射回原始 ID。 + +`nprobe` 越大，通常 recall@K 越高，但候选数、延迟和工作区也更大。正式脚本测试 1、4、16、64 四档。 + +## 6. 正确性验证 + +自动测试覆盖非 2 的幂维度 D=33，以避免只在整齐 shape 下通过；分别验证 L2 和 inner product。精确 GPU 与 CPU 的 Top-K ID 集合要求完全一致，分数误差容限为 2e-3。另有 clustered synthetic data 的 IVF 索引 round-trip 测试，要求 recall@10 不低于 0.90。 + +| 检查项 | 当前结果 | +|---|---| +| CMake Release 构建 | 通过 | +| `ctest` | 1/1 通过（0.34 s） | +| CPU vs GPU exact recall@K | 1.000000 | +| L2 Top-K/分数测试 | 通过（容限 2e-3） | +| Inner product Top-K/分数测试 | 通过（容限 2e-3） | +| IVF save/load | 通过 | + +## 7. 实验设置 + +| 项目 | 值 | +|---|---| +| GPU | NVIDIA GeForce RTX 4090 | +| GPU 显存 | 24,564 MiB | +| CPU | Intel Xeon Platinum 8352V @ 2.10 GHz，16 vCPU | +| OS | Ubuntu 22.04 / Linux 5.15.0-91-generic | +| NVIDIA Driver | 580.95.05 | +| CUDA Toolkit / nvcc | 11.8 / V11.8.89 | +| 编译器 / CMake | G++ 11.3.0 / CMake 3.22.1 | +| 编译类型 | Release (`-O3`, CUDA `--use_fast_math`) | +| 数据规模 | 1,000,000 x 128，1000 queries | +| 随机种子 | 42 | + +正式计时关闭了其他 GPU 任务。精确 QPS 包含 query H2D、CUDA 距离、distance D2H 和 CPU Top-K，不包含数据库首次上传；IVF QPS 还包含 CPU coarse search、候选 ID 构造和一次数据库 H2D。两者都不包含索引文件加载。P50/P99 用 batch=1 的逐 query 精确查询时间。显存字段是程序显式 buffer 大小，不包含 CUDA context。 + +## 8. 性能与质量结果 + +### 8.1 精确检索与 K + +| K | Batch | QPS | P50 (ms) | P99 (ms) | CPU/GPU 加速比 | CPU/GPU recall@K | 工作区 MiB | +|---:|---:|---:|---:|---:|---:|---:|---:| +| 1 | 128 | 263.571 | 4.622 | 6.801 | 42.444x | 1.000000 | 976.625 | +| 10 | 128 | 261.783 | 4.650 | 6.038 | 42.262x | 1.000000 | 976.625 | +| 50 | 128 | 274.503 | 4.281 | 5.897 | 42.709x | 1.000000 | 976.625 | +| 100 | 128 | 255.570 | 4.402 | 5.988 | 40.381x | 1.000000 | 976.625 | + +### 8.2 Batch size + +| Batch | QPS | P50 (ms) | P99 (ms) | 工作区 MiB | +|---:|---:|---:|---:|---:| +| 1 | 231.165 | 4.391 | 6.144 | 492.096 | +| 32 | 276.272 | 4.874 | 6.491 | 610.367 | +| 128 | 261.783 | 4.650 | 6.038 | 976.625 | + +### 8.3 IVF nprobe + +| nlist | nprobe | QPS | recall@10 | 平均排名分数误差 | 工作区 MiB | +|---:|---:|---:|---:|---:|---:| +| 256 | 1 | 3509.281 | 0.059600 | 14.431664 | 488.390 | +| 256 | 4 | 1986.191 | 0.182900 | 8.026410 | 488.680 | +| 256 | 16 | 595.972 | 0.472900 | 3.293365 | 489.774 | +| 256 | 64 | 153.481 | 0.937800 | 0.272549 | 493.920 | + +## 9. 结果分析 + +batch=32 在本次运行中取得最高精确检索吞吐 276.272 QPS，比 batch=1 的 231.165 QPS 高约 19.5%；batch=128 未继续提升，说明更大的 distance buffer 和 CPU Top-K 并未带来额外收益。P50/P99 都在约 4.3-6.5 ms 范围内，差异接近单次运行波动。 + +K 从 1 增加到 100 后，精确 QPS 仍处于 255-275 区间，未呈严格单调下降；GPU 距离计算和 1000x1000000 距离传输仍是主要固定成本，CPU `partial_sort` 的额外开销没有压倒该成本。所有 K 配置的 CPU/GPU exact recall 均为 1.0。 + +IVF 展示了清晰的速度-质量权衡：nprobe=1 达到 3509.281 QPS，但 recall@10 仅 0.0596；nprobe=16 为 595.972 QPS、recall@10=0.4729；nprobe=64 达到 0.9378 recall@10，但吞吐降至 153.481 QPS，低于精确检索 261.783 QPS。若重视质量，本实现的 nprobe=64 已没有速度优势；这说明 coarse quality、索引布局和 GPU Top-K 仍需优化。 + +索引构建耗时 4391.59 ms。工作区指标为程序显式 buffer 估算：精确 batch=128 为 976.625 MiB，IVF 随 nprobe 从约 488.390 MiB 增至 493.920 MiB；它不包含 CUDA context 与驱动开销。 + +## 10. 优化历程与已知瓶颈 + +初版选择统一的“距离越小越好”内部表示，使 L2 和内积复用相同 Top-K 逻辑；同时采用 batch distance matrix，避免逐 query 启动 kernel。第一版 IVF 会在 CPU 按 ID 打包候选向量并逐 query 上传，端到端 nprobe=16 仅约 11 QPS。定位后改为数据库一次上传并常驻 GPU、每次只传候选 ID，最终 nprobe=16 达到 595.972 QPS，召回率保持 0.4729。这一修改约提升 52 倍，并保留版本化索引加载。 + +当前主要瓶颈包括：每个线程独立扫描维度、精确检索 distance matrix 的 D2H 传输、CPU `partial_sort`、IVF 的 CPU coarse search，以及候选 ID 的随机访存。服务器没有安装 `ncu`/`nsys`，因此报告不虚构 profiler 结论。 + +## 11. 后续工作 + +- 使用 CUB block radix sort 或分层 heap 实现 GPU Top-K 与跨 block merge。 +- 将数据库转为 tiled/转置布局，改善 warp 访存合并。 +- 用 CUDA kernel 完成 IVF assignment 和 coarse search。 +- 数据库与索引常驻 GPU，并用 pinned memory、stream 和 double buffering 重叠传输。 +- 支持 FP16、cosine、IVF-PQ 和多 GPU shard。 +- 使用 `ncu` 检查 memory throughput、occupancy、warp stall；使用 `nsys` 分析 H2D/kernel/D2H/CPU merge 时间线。 diff --git a/09_vector_retrieval/ccyyy1023/configs/exact.conf b/09_vector_retrieval/ccyyy1023/configs/exact.conf new file mode 100644 index 00000000..23898f96 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/configs/exact.conf @@ -0,0 +1,6 @@ +top_k = 10 +search_mode = "exact" +batch_size = 128 +nlist = 256 +nprobe = 16 + diff --git a/09_vector_retrieval/ccyyy1023/configs/ivf_flat.conf b/09_vector_retrieval/ccyyy1023/configs/ivf_flat.conf new file mode 100644 index 00000000..a5e4b718 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/configs/ivf_flat.conf @@ -0,0 +1,6 @@ +top_k = 10 +search_mode = "ivf_flat" +batch_size = 128 +nlist = 256 +nprobe = 16 + diff --git a/09_vector_retrieval/ccyyy1023/include/gvs/io.hpp b/09_vector_retrieval/ccyyy1023/include/gvs/io.hpp new file mode 100644 index 00000000..2b5618b2 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/include/gvs/io.hpp @@ -0,0 +1,34 @@ +#pragma once + +#include "gvs/search.hpp" + +#include +#include +#include +#include + +namespace gvs { + +struct VectorFile { + std::size_t count = 0; + std::size_t dim = 0; + Metric metric = Metric::L2; + std::vector values; +}; + +struct Parameters { + std::size_t top_k = 10; + std::string search_mode = "exact"; + std::size_t batch_size = 128; + std::size_t nlist = 4096; + std::size_t nprobe = 16; +}; + +void write_vector_file(const std::string& path, const VectorFile& data, + bool include_metric); +VectorFile read_vector_file(const std::string& path, bool expect_metric); +void write_search_result(const std::string& path, const SearchResult& result); +Parameters read_parameters(const std::string& path); + +} // namespace gvs + diff --git a/09_vector_retrieval/ccyyy1023/include/gvs/search.hpp b/09_vector_retrieval/ccyyy1023/include/gvs/search.hpp new file mode 100644 index 00000000..d1becb62 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/include/gvs/search.hpp @@ -0,0 +1,78 @@ +#pragma once + +#include +#include +#include +#include + +namespace gvs { + +enum class Metric { L2, InnerProduct }; + +struct SearchConfig { + std::size_t k = 10; + std::size_t batch_size = 16; +}; + +struct SearchResult { + std::size_t queries = 0; + std::size_t k = 0; + std::vector ids; + std::vector scores; +}; + +struct GpuStats { + double elapsed_ms = 0.0; + std::size_t peak_workspace_bytes = 0; + std::string device_name; +}; + +SearchResult cpu_exact_search(const std::vector& database, + const std::vector& queries, + std::size_t n, std::size_t nq, + std::size_t dim, Metric metric, + std::size_t k); + +SearchResult gpu_exact_search(const std::vector& database, + const std::vector& queries, + std::size_t n, std::size_t nq, + std::size_t dim, Metric metric, + const SearchConfig& config, + GpuStats* stats = nullptr); + +SearchResult gpu_indexed_search(const std::vector& database, + const std::vector& queries, + const std::vector>& candidate_ids, + std::size_t n, std::size_t nq, + std::size_t dim, Metric metric, + const SearchConfig& config, + GpuStats* stats = nullptr); + +double recall_at_k(const SearchResult& truth, const SearchResult& candidate); + +class IvfFlatIndex { +public: + IvfFlatIndex(std::size_t dim, std::size_t nlist, Metric metric); + + void train(const std::vector& database, std::size_t n, + std::size_t iterations = 12, std::uint64_t seed = 42); + void add(const std::vector& database, std::size_t n); + SearchResult search(const std::vector& queries, std::size_t nq, + std::size_t nprobe, const SearchConfig& config, + GpuStats* stats = nullptr) const; + + void save(const std::string& path) const; + static IvfFlatIndex load(const std::string& path); + + std::size_t nlist() const { return nlist_; } + +private: + std::size_t dim_; + std::size_t nlist_; + Metric metric_; + std::vector centroids_; + std::vector database_; + std::vector> lists_; +}; + +} // namespace gvs diff --git a/09_vector_retrieval/ccyyy1023/results/SMOKE_SUMMARY.md b/09_vector_retrieval/ccyyy1023/results/SMOKE_SUMMARY.md new file mode 100644 index 00000000..3cd570ae --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/SMOKE_SUMMARY.md @@ -0,0 +1,5 @@ +# Measured benchmark summary + +| log | GPU | N | NQ | D | K | exact QPS | P50 ms | P99 ms | exact recall | speedup | nprobe | IVF QPS | IVF recall | mean rank score error | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| smoke.json | NVIDIA GeForce RTX 4090 | 10000 | 100 | 128 | 10 | 16118.163 | 0.081 | 0.084 | 1.000000 | 21.021 | 8 | 21150.489 | 0.371000 | 6.098108 | diff --git a/09_vector_retrieval/ccyyy1023/results/SUMMARY.md b/09_vector_retrieval/ccyyy1023/results/SUMMARY.md new file mode 100644 index 00000000..96e659da --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/SUMMARY.md @@ -0,0 +1,14 @@ +# Measured benchmark summary + +| log | GPU | N | NQ | D | K | exact QPS | P50 ms | P99 ms | exact recall | speedup | nprobe | IVF QPS | IVF recall | mean rank score error | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| batch_1.json | NVIDIA GeForce RTX 4090 | 1000000 | 1000 | 128 | 10 | 231.165 | 4.391 | 6.144 | 1.000000 | 36.857 | 16 | 585.657 | 0.472900 | 3.293365 | +| batch_32.json | NVIDIA GeForce RTX 4090 | 1000000 | 1000 | 128 | 10 | 276.272 | 4.874 | 6.491 | 1.000000 | 43.956 | 16 | 590.492 | 0.472900 | 3.293365 | +| k_1.json | NVIDIA GeForce RTX 4090 | 1000000 | 1000 | 128 | 1 | 263.571 | 4.622 | 6.801 | 1.000000 | 42.444 | 16 | 562.764 | 0.507000 | 2.678481 | +| k_10.json | NVIDIA GeForce RTX 4090 | 1000000 | 1000 | 128 | 10 | 261.783 | 4.650 | 6.038 | 1.000000 | 42.262 | 16 | 595.972 | 0.472900 | 3.293365 | +| k_100.json | NVIDIA GeForce RTX 4090 | 1000000 | 1000 | 128 | 100 | 255.570 | 4.402 | 5.988 | 1.000000 | 40.381 | 16 | 545.980 | 0.435290 | 4.288385 | +| k_50.json | NVIDIA GeForce RTX 4090 | 1000000 | 1000 | 128 | 50 | 274.503 | 4.281 | 5.897 | 1.000000 | 42.709 | 16 | 550.537 | 0.449600 | 3.925203 | +| nprobe_1.json | NVIDIA GeForce RTX 4090 | 1000000 | 1000 | 128 | 10 | 233.112 | 4.454 | 6.053 | 1.000000 | 43.759 | 1 | 3509.281 | 0.059600 | 14.431664 | +| nprobe_4.json | NVIDIA GeForce RTX 4090 | 1000000 | 1000 | 128 | 10 | 244.722 | 4.357 | 6.802 | 1.000000 | 38.676 | 4 | 1986.191 | 0.182900 | 8.026410 | +| nprobe_64.json | NVIDIA GeForce RTX 4090 | 1000000 | 1000 | 128 | 10 | 259.653 | 4.138 | 5.862 | 1.000000 | 42.469 | 64 | 153.481 | 0.937800 | 0.272549 | +| smoke.json | NVIDIA GeForce RTX 4090 | 10000 | 100 | 128 | 10 | 16118.163 | 0.081 | 0.084 | 1.000000 | 21.021 | 8 | 21150.489 | 0.371000 | 6.098108 | diff --git a/09_vector_retrieval/ccyyy1023/results/batch_1.json b/09_vector_retrieval/ccyyy1023/results/batch_1.json new file mode 100644 index 00000000..546e24ea --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/batch_1.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 1000000, "nq": 1000, "dim": 128, "k": 10}, + "exact": {"query_ms": 4325.907430, "qps": 231.165372, "p50_ms": 4.391250, "p99_ms": 6.144444, "workspace_mib": 492.096436, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 36.857411}, + "cpu": {"queries_measured": 20, "query_ms": 3188.834950}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 1707.485259, "qps": 585.656593, "nlist": 256, "nprobe": 16, "recall_at_k": 0.472900, "mean_rank_score_error": 3.293365, "workspace_mib": 489.773682} +} diff --git a/09_vector_retrieval/ccyyy1023/results/batch_32.json b/09_vector_retrieval/ccyyy1023/results/batch_32.json new file mode 100644 index 00000000..de774f01 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/batch_32.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 1000000, "nq": 1000, "dim": 128, "k": 10}, + "exact": {"query_ms": 3619.621729, "qps": 276.271963, "p50_ms": 4.873504, "p99_ms": 6.491380, "workspace_mib": 610.367188, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 43.956197}, + "cpu": {"queries_measured": 20, "query_ms": 3182.096116}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 1693.504089, "qps": 590.491636, "nlist": 256, "nprobe": 16, "recall_at_k": 0.472900, "mean_rank_score_error": 3.293365, "workspace_mib": 489.773682} +} diff --git a/09_vector_retrieval/ccyyy1023/results/environment.txt b/09_vector_retrieval/ccyyy1023/results/environment.txt new file mode 100644 index 00000000..92569c74 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/environment.txt @@ -0,0 +1,7 @@ +Measured: 2026-09-20 Asia/Shanghai +Model name: Intel(R) Xeon(R) Platinum 8352V CPU @ 2.10GHz +NVIDIA GeForce RTX 4090, 24564 MiB, 580.95.05 +Build cuda_11.8.r11.8/compiler.31833905_0 +cmake version 3.22.1 +g++ (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0 +Linux 5.15.0-91-generic diff --git a/09_vector_retrieval/ccyyy1023/results/index_build.txt b/09_vector_retrieval/ccyyy1023/results/index_build.txt new file mode 100644 index 00000000..cbdd0605 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/index_build.txt @@ -0,0 +1 @@ +index_build_ms=4391.59 diff --git a/09_vector_retrieval/ccyyy1023/results/k_1.json b/09_vector_retrieval/ccyyy1023/results/k_1.json new file mode 100644 index 00000000..7cd813e2 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/k_1.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 1000000, "nq": 1000, "dim": 128, "k": 1}, + "exact": {"query_ms": 3794.049961, "qps": 263.570594, "p50_ms": 4.621565, "p99_ms": 6.800636, "workspace_mib": 976.625000, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 42.443584}, + "cpu": {"queries_measured": 20, "query_ms": 3220.661527}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 1776.944915, "qps": 562.763647, "nlist": 256, "nprobe": 16, "recall_at_k": 0.507000, "mean_rank_score_error": 2.678481, "workspace_mib": 489.773682} +} diff --git a/09_vector_retrieval/ccyyy1023/results/k_10.json b/09_vector_retrieval/ccyyy1023/results/k_10.json new file mode 100644 index 00000000..0590c67e --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/k_10.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 1000000, "nq": 1000, "dim": 128, "k": 10}, + "exact": {"query_ms": 3819.959462, "qps": 261.782883, "p50_ms": 4.650455, "p99_ms": 6.037778, "workspace_mib": 976.625000, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 42.261559}, + "cpu": {"queries_measured": 20, "query_ms": 3228.748869}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 1677.930929, "qps": 595.972088, "nlist": 256, "nprobe": 16, "recall_at_k": 0.472900, "mean_rank_score_error": 3.293365, "workspace_mib": 489.773682} +} diff --git a/09_vector_retrieval/ccyyy1023/results/k_100.json b/09_vector_retrieval/ccyyy1023/results/k_100.json new file mode 100644 index 00000000..7602d850 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/k_100.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 1000000, "nq": 1000, "dim": 128, "k": 100}, + "exact": {"query_ms": 3912.828840, "qps": 255.569574, "p50_ms": 4.402323, "p99_ms": 5.987935, "workspace_mib": 976.625000, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 40.380501}, + "cpu": {"queries_measured": 20, "query_ms": 3160.039780}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 1831.570234, "qps": 545.979609, "nlist": 256, "nprobe": 16, "recall_at_k": 0.435290, "mean_rank_score_error": 4.288385, "workspace_mib": 489.773682} +} diff --git a/09_vector_retrieval/ccyyy1023/results/k_50.json b/09_vector_retrieval/ccyyy1023/results/k_50.json new file mode 100644 index 00000000..37725b99 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/k_50.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 1000000, "nq": 1000, "dim": 128, "k": 50}, + "exact": {"query_ms": 3642.950502, "qps": 274.502769, "p50_ms": 4.280817, "p99_ms": 5.896796, "workspace_mib": 976.625000, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 42.708571}, + "cpu": {"queries_measured": 20, "query_ms": 3111.704184}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 1816.408478, "qps": 550.536959, "nlist": 256, "nprobe": 16, "recall_at_k": 0.449600, "mean_rank_score_error": 3.925203, "workspace_mib": 489.773682} +} diff --git a/09_vector_retrieval/ccyyy1023/results/nprobe_1.json b/09_vector_retrieval/ccyyy1023/results/nprobe_1.json new file mode 100644 index 00000000..8ee126a1 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/nprobe_1.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 1000000, "nq": 1000, "dim": 128, "k": 10}, + "exact": {"query_ms": 4289.785030, "qps": 233.111914, "p50_ms": 4.453786, "p99_ms": 6.053339, "workspace_mib": 976.625000, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 43.759387}, + "cpu": {"queries_measured": 20, "query_ms": 3754.367282}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 284.958634, "qps": 3509.281280, "nlist": 256, "nprobe": 1, "recall_at_k": 0.059600, "mean_rank_score_error": 14.431664, "workspace_mib": 488.388565} +} diff --git a/09_vector_retrieval/ccyyy1023/results/nprobe_4.json b/09_vector_retrieval/ccyyy1023/results/nprobe_4.json new file mode 100644 index 00000000..0e4875b0 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/nprobe_4.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 1000000, "nq": 1000, "dim": 128, "k": 10}, + "exact": {"query_ms": 4086.269341, "qps": 244.721999, "p50_ms": 4.357081, "p99_ms": 6.801751, "workspace_mib": 976.625000, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 38.676046}, + "cpu": {"queries_measured": 20, "query_ms": 3160.814839}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 503.476214, "qps": 1986.191149, "nlist": 256, "nprobe": 4, "recall_at_k": 0.182900, "mean_rank_score_error": 8.026410, "workspace_mib": 488.679054} +} diff --git a/09_vector_retrieval/ccyyy1023/results/nprobe_64.json b/09_vector_retrieval/ccyyy1023/results/nprobe_64.json new file mode 100644 index 00000000..aebd6520 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/nprobe_64.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 1000000, "nq": 1000, "dim": 128, "k": 10}, + "exact": {"query_ms": 3851.290919, "qps": 259.653197, "p50_ms": 4.137849, "p99_ms": 5.861579, "workspace_mib": 976.625000, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 42.469173}, + "cpu": {"queries_measured": 20, "query_ms": 3271.222801}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 6515.475806, "qps": 153.480733, "nlist": 256, "nprobe": 64, "recall_at_k": 0.937800, "mean_rank_score_error": 0.272549, "workspace_mib": 493.922852} +} diff --git a/09_vector_retrieval/ccyyy1023/results/smoke.json b/09_vector_retrieval/ccyyy1023/results/smoke.json new file mode 100644 index 00000000..b9bea0d1 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/results/smoke.json @@ -0,0 +1,8 @@ +{ + "status": "measured", + "device": "NVIDIA GeForce RTX 4090", + "dataset": {"n": 10000, "nq": 100, "dim": 128, "k": 10}, + "exact": {"query_ms": 6.204181, "qps": 16118.162897, "p50_ms": 0.081473, "p99_ms": 0.084174, "workspace_mib": 6.119141, "cpu_gpu_recall_at_k": 1.000000, "speedup_vs_cpu": 21.021299}, + "cpu": {"queries_measured": 20, "query_ms": 26.083989}, + "ivf_flat": {"index_build_ms": 0.000000, "query_ms": 4.728023, "qps": 21150.489327, "nlist": 64, "nprobe": 8, "recall_at_k": 0.371000, "mean_rank_score_error": 6.098108, "workspace_mib": 0.998459} +} diff --git a/09_vector_retrieval/ccyyy1023/scripts/check_submission.py b/09_vector_retrieval/ccyyy1023/scripts/check_submission.py new file mode 100644 index 00000000..3c40c03b --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/scripts/check_submission.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 +"""Static submission check that does not require CUDA.""" + +import json +import pathlib +import sys + + +REQUIRED = [ + "CMakeLists.txt", + "README.md", + "REPORT.md", + "include/gvs/search.hpp", + "include/gvs/io.hpp", + "src/cpu_search.cpp", + "src/gpu_search.cu", + "src/ivf_flat.cpp", + "src/file_io.cpp", + "src/main.cpp", + "tests/test_search.cpp", + "configs/exact.conf", + "configs/ivf_flat.conf", + "results/SUMMARY.md", + "results/environment.txt", +] + + +def main() -> int: + root = pathlib.Path(__file__).resolve().parents[1] + missing = [name for name in REQUIRED if not (root / name).is_file()] + if missing: + print("missing files:", *missing, sep="\n- ", file=sys.stderr) + return 1 + logs = [json.loads(path.read_text(encoding="utf-8")) + for path in (root / "results").glob("*.json")] + if not logs or not all(item.get("status") == "measured" for item in logs): + print("all submitted JSON logs must be real measured runs", file=sys.stderr) + return 1 + forbidden = ["TODO: fake", "RTX 4090: 999", "speedup: 100x"] + text = "\n".join((root / name).read_text(encoding="utf-8", errors="replace") + for name in REQUIRED if name.endswith((".md", ".cpp", ".cu", ".hpp"))) + if any(token in text for token in forbidden): + print("found forbidden fabricated marker", file=sys.stderr) + return 1 + print(f"submission structure OK ({len(REQUIRED)} required files)") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/09_vector_retrieval/ccyyy1023/scripts/run_required_benchmarks.sh b/09_vector_retrieval/ccyyy1023/scripts/run_required_benchmarks.sh new file mode 100644 index 00000000..bf17803b --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/scripts/run_required_benchmarks.sh @@ -0,0 +1,50 @@ +#!/usr/bin/env bash +set -euo pipefail + +project_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$project_dir" +mkdir -p data results + +cmake -S . -B build -DCMAKE_BUILD_TYPE=Release +cmake --build build -j"$(nproc)" +ctest --test-dir build --output-on-failure + +if [[ ! -f data/required_db.bin || ! -f data/required_queries.bin ]]; then + ./build/vector_search generate \ + --db data/required_db.bin --queries data/required_queries.bin \ + --n 1000000 --nq 1000 --dim 128 --metric l2 --seed 42 +fi + +if [[ ! -f data/required_ivf.bin ]]; then + ./build/vector_search build \ + --db data/required_db.bin --index data/required_ivf.bin \ + --nlist 256 --iterations 8 --seed 42 | tee results/index_build.txt +fi + +for k in 1 10 50 100; do + ./build/vector_search benchmark \ + --db data/required_db.bin --queries data/required_queries.bin \ + --index data/required_ivf.bin --log "results/k_${k}.json" \ + --k "$k" --batch 128 --nlist 256 --nprobe 16 \ + --cpu-query-limit 20 --latency-query-limit 100 +done + +for batch in 1 32; do + ./build/vector_search benchmark \ + --db data/required_db.bin --queries data/required_queries.bin \ + --index data/required_ivf.bin --log "results/batch_${batch}.json" \ + --k 10 --batch "$batch" --nlist 256 --nprobe 16 \ + --cpu-query-limit 20 --latency-query-limit 100 +done + +for nprobe in 1 4 64; do + ./build/vector_search benchmark \ + --db data/required_db.bin --queries data/required_queries.bin \ + --index data/required_ivf.bin --log "results/nprobe_${nprobe}.json" \ + --k 10 --batch 128 --nlist 256 --nprobe "$nprobe" \ + --cpu-query-limit 20 --latency-query-limit 100 +done + +python3 scripts/summarize_results.py results results/SUMMARY.md +echo "Required benchmark complete: results/SUMMARY.md" + diff --git a/09_vector_retrieval/ccyyy1023/scripts/run_smoke.sh b/09_vector_retrieval/ccyyy1023/scripts/run_smoke.sh new file mode 100644 index 00000000..1cb2e1e9 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/scripts/run_smoke.sh @@ -0,0 +1,26 @@ +#!/usr/bin/env bash +set -euo pipefail + +project_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$project_dir" + +cmake -S . -B build -DCMAKE_BUILD_TYPE=Release +cmake --build build -j"$(nproc)" +ctest --test-dir build --output-on-failure + +mkdir -p data results +./build/vector_search generate \ + --db data/small_db.bin --queries data/small_queries.bin \ + --n 10000 --nq 100 --dim 128 --metric l2 --seed 42 +./build/vector_search build \ + --db data/small_db.bin --index data/small_ivf.bin \ + --nlist 64 --iterations 8 --seed 42 +./build/vector_search benchmark \ + --db data/small_db.bin --queries data/small_queries.bin \ + --index data/small_ivf.bin --log results/smoke.json \ + --k 10 --batch 32 --nlist 64 --nprobe 8 \ + --cpu-query-limit 20 --latency-query-limit 50 + +python3 scripts/summarize_results.py results results/SMOKE_SUMMARY.md +echo "Smoke test complete: results/SMOKE_SUMMARY.md" + diff --git a/09_vector_retrieval/ccyyy1023/scripts/summarize_results.py b/09_vector_retrieval/ccyyy1023/scripts/summarize_results.py new file mode 100644 index 00000000..95462aa1 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/scripts/summarize_results.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +"""Collect measured benchmark JSON files into a compact Markdown table.""" + +import json +import pathlib +import sys + + +def main() -> int: + if len(sys.argv) != 3: + print("usage: summarize_results.py RESULTS_DIR OUTPUT.md", file=sys.stderr) + return 2 + results_dir = pathlib.Path(sys.argv[1]) + output = pathlib.Path(sys.argv[2]) + rows = [] + for path in sorted(results_dir.glob("*.json")): + data = json.loads(path.read_text(encoding="utf-8")) + if data.get("status") != "measured": + continue + ds = data["dataset"] + exact = data["exact"] + ivf = data["ivf_flat"] + rows.append( + [ + path.name, + data.get("device", ""), + ds["n"], + ds["nq"], + ds["dim"], + ds["k"], + f"{exact['qps']:.3f}", + f"{exact['p50_ms']:.3f}", + f"{exact['p99_ms']:.3f}", + f"{exact['cpu_gpu_recall_at_k']:.6f}", + f"{exact['speedup_vs_cpu']:.3f}", + ivf["nprobe"], + f"{ivf['qps']:.3f}", + f"{ivf['recall_at_k']:.6f}", + f"{ivf['mean_rank_score_error']:.6f}", + ] + ) + header = [ + "log", "GPU", "N", "NQ", "D", "K", "exact QPS", "P50 ms", + "P99 ms", "exact recall", "speedup", "nprobe", "IVF QPS", + "IVF recall", "mean rank score error", + ] + lines = ["# Measured benchmark summary", "", "| " + " | ".join(header) + " |", + "|" + "|".join(["---"] * len(header)) + "|"] + lines.extend("| " + " | ".join(map(str, row)) + " |" for row in rows) + if not rows: + lines.extend(["", "No measured benchmark JSON files were found."]) + output.write_text("\n".join(lines) + "\n", encoding="utf-8") + print(f"wrote {output} with {len(rows)} measured rows") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) + diff --git a/09_vector_retrieval/ccyyy1023/src/cpu_search.cpp b/09_vector_retrieval/ccyyy1023/src/cpu_search.cpp new file mode 100644 index 00000000..69f6185e --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/src/cpu_search.cpp @@ -0,0 +1,73 @@ +#include "gvs/search.hpp" + +#include +#include +#include +#include +#include +#include + +namespace gvs { +namespace { + +float distance(const float* a, const float* b, std::size_t dim, Metric metric) { + float value = 0.0f; + if (metric == Metric::L2) { + for (std::size_t d = 0; d < dim; ++d) { + const float diff = a[d] - b[d]; + value += diff * diff; + } + return value; + } + for (std::size_t d = 0; d < dim; ++d) value += a[d] * b[d]; + return -value; // Smaller is better internally for both metrics. +} + +} // namespace + +SearchResult cpu_exact_search(const std::vector& database, + const std::vector& queries, + std::size_t n, std::size_t nq, + std::size_t dim, Metric metric, + std::size_t k) { + if (database.size() != n * dim || queries.size() != nq * dim) + throw std::invalid_argument("input shape does not match n/nq/dim"); + k = std::min(k, n); + SearchResult output{nq, k, std::vector(nq * k), + std::vector(nq * k)}; + std::vector> candidates(n); + for (std::size_t q = 0; q < nq; ++q) { + const float* query = queries.data() + q * dim; + for (std::size_t i = 0; i < n; ++i) { + candidates[i] = {distance(query, database.data() + i * dim, dim, metric), + static_cast(i)}; + } + std::partial_sort(candidates.begin(), candidates.begin() + k, + candidates.end()); + for (std::size_t j = 0; j < k; ++j) { + output.ids[q * k + j] = candidates[j].second; + output.scores[q * k + j] = + metric == Metric::InnerProduct ? -candidates[j].first + : candidates[j].first; + } + } + return output; +} + +double recall_at_k(const SearchResult& truth, const SearchResult& candidate) { + if (truth.queries != candidate.queries || truth.k != candidate.k) + throw std::invalid_argument("recall inputs have different shapes"); + std::size_t hits = 0; + for (std::size_t q = 0; q < truth.queries; ++q) { + std::unordered_set expected; + for (std::size_t j = 0; j < truth.k; ++j) + expected.insert(truth.ids[q * truth.k + j]); + for (std::size_t j = 0; j < candidate.k; ++j) + hits += expected.count(candidate.ids[q * candidate.k + j]); + } + const auto total = truth.queries * truth.k; + return total == 0 ? 1.0 : static_cast(hits) / total; +} + +} // namespace gvs + diff --git a/09_vector_retrieval/ccyyy1023/src/file_io.cpp b/09_vector_retrieval/ccyyy1023/src/file_io.cpp new file mode 100644 index 00000000..7aee9c4f --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/src/file_io.cpp @@ -0,0 +1,127 @@ +#include "gvs/io.hpp" + +#include +#include +#include +#include +#include + +namespace gvs { +namespace { + +constexpr char kDatabaseMagic[8] = {'G','V','S','D','B','0','1','\0'}; +constexpr char kQueryMagic[8] = {'G','V','S','Q','R','0','1','\0'}; +constexpr char kResultMagic[8] = {'G','V','S','R','S','0','1','\0'}; + +template +void write_scalar(std::ofstream& out, const T& value) { + out.write(reinterpret_cast(&value), sizeof(T)); + if (!out) throw std::runtime_error("failed to write binary file"); +} + +template +T read_scalar(std::ifstream& in) { + T value{}; + in.read(reinterpret_cast(&value), sizeof(T)); + if (!in) throw std::runtime_error("truncated binary file"); + return value; +} + +std::string trim(std::string value) { + auto not_space = [](unsigned char c) { return !std::isspace(c); }; + value.erase(value.begin(), std::find_if(value.begin(), value.end(), not_space)); + value.erase(std::find_if(value.rbegin(), value.rend(), not_space).base(), value.end()); + if (value.size() >= 2 && value.front() == '"' && value.back() == '"') + value = value.substr(1, value.size() - 2); + return value; +} + +} // namespace + +void write_vector_file(const std::string& path, const VectorFile& data, + bool include_metric) { + if (data.values.size() != data.count * data.dim) + throw std::invalid_argument("vector file shape mismatch"); + std::ofstream out(path, std::ios::binary); + if (!out) throw std::runtime_error("cannot open output file: " + path); + out.write(include_metric ? kDatabaseMagic : kQueryMagic, 8); + write_scalar(out, static_cast(data.count)); + write_scalar(out, static_cast(data.dim)); + write_scalar(out, static_cast(1)); // dtype: 1 = fp32 + if (include_metric) + write_scalar(out, static_cast(data.metric == Metric::L2 ? 0 : 1)); + out.write(reinterpret_cast(data.values.data()), + static_cast(data.values.size() * sizeof(float))); + if (!out) throw std::runtime_error("failed to write vectors"); +} + +VectorFile read_vector_file(const std::string& path, bool expect_metric) { + std::ifstream in(path, std::ios::binary); + if (!in) throw std::runtime_error("cannot open input file: " + path); + char magic[8]{}; + in.read(magic, 8); + const char* expected = expect_metric ? kDatabaseMagic : kQueryMagic; + if (!in || std::memcmp(magic, expected, 8) != 0) + throw std::runtime_error("invalid vector file magic/version: " + path); + VectorFile result; + result.count = static_cast(read_scalar(in)); + result.dim = static_cast(read_scalar(in)); + const auto dtype = read_scalar(in); + if (dtype != 1) throw std::runtime_error("this baseline supports fp32 files only"); + if (expect_metric) { + const auto metric = read_scalar(in); + if (metric > 1) throw std::runtime_error("invalid metric code"); + result.metric = metric == 0 ? Metric::L2 : Metric::InnerProduct; + } + if (result.count == 0 || result.dim == 0 || + result.count > static_cast(-1) / result.dim) + throw std::runtime_error("invalid or overflowing vector shape"); + result.values.resize(result.count * result.dim); + in.read(reinterpret_cast(result.values.data()), + static_cast(result.values.size() * sizeof(float))); + if (!in) throw std::runtime_error("truncated vector payload"); + return result; +} + +void write_search_result(const std::string& path, const SearchResult& result) { + if (result.ids.size() != result.queries * result.k || + result.scores.size() != result.ids.size()) + throw std::invalid_argument("result shape mismatch"); + std::ofstream out(path, std::ios::binary); + if (!out) throw std::runtime_error("cannot open result file: " + path); + out.write(kResultMagic, 8); + write_scalar(out, static_cast(result.queries)); + write_scalar(out, static_cast(result.k)); + for (std::size_t i = 0; i < result.ids.size(); ++i) { + write_scalar(out, static_cast(result.ids[i])); + write_scalar(out, result.scores[i]); + } +} + +Parameters read_parameters(const std::string& path) { + std::ifstream in(path); + if (!in) throw std::runtime_error("cannot open parameter file: " + path); + Parameters p; + std::string line; + while (std::getline(in, line)) { + const auto comment = line.find('#'); + if (comment != std::string::npos) line.erase(comment); + const auto equal = line.find('='); + if (equal == std::string::npos) continue; + const std::string key = trim(line.substr(0, equal)); + const std::string value = trim(line.substr(equal + 1)); + if (key == "top_k") p.top_k = std::stoull(value); + else if (key == "search_mode") p.search_mode = value; + else if (key == "batch_size") p.batch_size = std::stoull(value); + else if (key == "nlist") p.nlist = std::stoull(value); + else if (key == "nprobe") p.nprobe = std::stoull(value); + } + if (p.top_k == 0 || p.batch_size == 0 || p.nlist == 0 || p.nprobe == 0) + throw std::runtime_error("all numeric parameters must be positive"); + if (p.search_mode != "exact" && p.search_mode != "ivf_flat") + throw std::runtime_error("search_mode must be exact or ivf_flat"); + return p; +} + +} // namespace gvs + diff --git a/09_vector_retrieval/ccyyy1023/src/gpu_search.cu b/09_vector_retrieval/ccyyy1023/src/gpu_search.cu new file mode 100644 index 00000000..c05ab519 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/src/gpu_search.cu @@ -0,0 +1,227 @@ +#include "gvs/search.hpp" + +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace gvs { +namespace { + +#define CUDA_CHECK(call) \ + do { \ + cudaError_t error__ = (call); \ + if (error__ != cudaSuccess) \ + throw std::runtime_error(std::string("CUDA error: ") + \ + cudaGetErrorString(error__)); \ + } while (0) + +template +class DeviceBuffer { +public: + explicit DeviceBuffer(std::size_t count = 0) : count_(count) { + if (count_) CUDA_CHECK(cudaMalloc(&ptr_, count_ * sizeof(T))); + } + ~DeviceBuffer() { if (ptr_) cudaFree(ptr_); } + DeviceBuffer(const DeviceBuffer&) = delete; + DeviceBuffer& operator=(const DeviceBuffer&) = delete; + T* get() { return ptr_; } + const T* get() const { return ptr_; } +private: + T* ptr_ = nullptr; + std::size_t count_ = 0; +}; + +__global__ void distance_kernel(const float* database, const float* queries, + float* distances, std::size_t n, + std::size_t batch, std::size_t dim, + int metric) { + const std::size_t i = blockIdx.x * blockDim.x + threadIdx.x; + const std::size_t q = blockIdx.y; + if (i >= n || q >= batch) return; + const float* x = database + i * dim; + const float* query = queries + q * dim; + float value = 0.0f; + if (metric == 0) { + for (std::size_t d = 0; d < dim; ++d) { + const float diff = query[d] - x[d]; + value = fmaf(diff, diff, value); + } + } else { + for (std::size_t d = 0; d < dim; ++d) + value = fmaf(query[d], x[d], value); + value = -value; + } + distances[q * n + i] = value; +} + +__global__ void indexed_distance_kernel(const float* database, const float* query, + const int* ids, float* distances, + std::size_t candidates, std::size_t dim, + int metric) { + const std::size_t i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= candidates) return; + const float* x = database + static_cast(ids[i]) * dim; + float value = 0.0f; + if (metric == 0) { + for (std::size_t d = 0; d < dim; ++d) { + const float diff = query[d] - x[d]; + value = fmaf(diff, diff, value); + } + } else { + for (std::size_t d = 0; d < dim; ++d) + value = fmaf(query[d], x[d], value); + value = -value; + } + distances[i] = value; +} + +SearchResult finalize_on_cpu(const std::vector& distances, + std::size_t nq, std::size_t n, + std::size_t k, Metric metric) { + SearchResult output{nq, k, std::vector(nq * k), + std::vector(nq * k)}; + std::vector> row(n); + for (std::size_t q = 0; q < nq; ++q) { + for (std::size_t i = 0; i < n; ++i) + row[i] = {distances[q * n + i], static_cast(i)}; + std::partial_sort(row.begin(), row.begin() + k, row.end()); + for (std::size_t j = 0; j < k; ++j) { + output.ids[q * k + j] = row[j].second; + output.scores[q * k + j] = + metric == Metric::InnerProduct ? -row[j].first : row[j].first; + } + } + return output; +} + +} // namespace + +SearchResult gpu_exact_search(const std::vector& database, + const std::vector& queries, + std::size_t n, std::size_t nq, + std::size_t dim, Metric metric, + const SearchConfig& config, + GpuStats* stats) { + if (database.size() != n * dim || queries.size() != nq * dim) + throw std::invalid_argument("input shape does not match n/nq/dim"); + const std::size_t k = std::min(config.k, n); + const std::size_t batch_capacity = std::min(config.batch_size, nq); + + DeviceBuffer d_database(database.size()); + DeviceBuffer d_queries(batch_capacity * dim); + DeviceBuffer d_distances(batch_capacity * n); + CUDA_CHECK(cudaMemcpy(d_database.get(), database.data(), + database.size() * sizeof(float), cudaMemcpyHostToDevice)); + + SearchResult output{nq, k, std::vector(nq * k), + std::vector(nq * k)}; + std::vector host_distances(batch_capacity * n); + CUDA_CHECK(cudaDeviceSynchronize()); + const auto begin = std::chrono::steady_clock::now(); + + for (std::size_t first = 0; first < nq; first += batch_capacity) { + const std::size_t batch = std::min(batch_capacity, nq - first); + CUDA_CHECK(cudaMemcpy(d_queries.get(), queries.data() + first * dim, + batch * dim * sizeof(float), cudaMemcpyHostToDevice)); + const dim3 block(256); + const dim3 grid(static_cast((n + block.x - 1) / block.x), + static_cast(batch)); + distance_kernel<<>>(d_database.get(), d_queries.get(), + d_distances.get(), n, batch, dim, + metric == Metric::L2 ? 0 : 1); + CUDA_CHECK(cudaGetLastError()); + CUDA_CHECK(cudaMemcpy(host_distances.data(), d_distances.get(), + batch * n * sizeof(float), cudaMemcpyDeviceToHost)); + SearchResult part = finalize_on_cpu(host_distances, batch, n, k, metric); + std::copy(part.ids.begin(), part.ids.end(), output.ids.begin() + first * k); + std::copy(part.scores.begin(), part.scores.end(), + output.scores.begin() + first * k); + } + CUDA_CHECK(cudaDeviceSynchronize()); + const auto end = std::chrono::steady_clock::now(); + const double elapsed = + std::chrono::duration(end - begin).count(); + + if (stats) { + cudaDeviceProp prop{}; + CUDA_CHECK(cudaGetDeviceProperties(&prop, 0)); + stats->elapsed_ms = elapsed; + stats->peak_workspace_bytes = + (database.size() + batch_capacity * dim + batch_capacity * n) * + sizeof(float); + stats->device_name = prop.name; + } + return output; +} + +SearchResult gpu_indexed_search(const std::vector& database, + const std::vector& queries, + const std::vector>& candidate_ids, + std::size_t n, std::size_t nq, + std::size_t dim, Metric metric, + const SearchConfig& config, + GpuStats* stats) { + if (database.size() != n * dim || queries.size() != nq * dim || + candidate_ids.size() != nq) + throw std::invalid_argument("indexed search shape mismatch"); + const std::size_t k = std::min(config.k, n); + std::size_t max_candidates = 0; + for (const auto& ids : candidate_ids) max_candidates = std::max(max_candidates, ids.size()); + SearchResult output{nq, k, std::vector(nq * k, -1), + std::vector(nq * k, 0.0f)}; + if (max_candidates == 0) return output; + + DeviceBuffer d_database(database.size()); + DeviceBuffer d_query(dim); + DeviceBuffer d_ids(max_candidates); + DeviceBuffer d_distances(max_candidates); + CUDA_CHECK(cudaMemcpy(d_database.get(), database.data(), + database.size() * sizeof(float), cudaMemcpyHostToDevice)); + std::vector host_distances(max_candidates); + const auto begin = std::chrono::steady_clock::now(); + for (std::size_t q = 0; q < nq; ++q) { + const std::size_t count = candidate_ids[q].size(); + if (count == 0) continue; + CUDA_CHECK(cudaMemcpy(d_query.get(), queries.data() + q * dim, + dim * sizeof(float), cudaMemcpyHostToDevice)); + CUDA_CHECK(cudaMemcpy(d_ids.get(), candidate_ids[q].data(), + count * sizeof(int), cudaMemcpyHostToDevice)); + const dim3 block(256); + const dim3 grid(static_cast((count + block.x - 1) / block.x)); + indexed_distance_kernel<<>>( + d_database.get(), d_query.get(), d_ids.get(), d_distances.get(), + count, dim, metric == Metric::L2 ? 0 : 1); + CUDA_CHECK(cudaGetLastError()); + CUDA_CHECK(cudaMemcpy(host_distances.data(), d_distances.get(), + count * sizeof(float), cudaMemcpyDeviceToHost)); + const std::size_t part_k = std::min(k, count); + std::vector> row(count); + for (std::size_t i = 0; i < count; ++i) + row[i] = {host_distances[i], candidate_ids[q][i]}; + std::partial_sort(row.begin(), row.begin() + part_k, row.end()); + for (std::size_t j = 0; j < part_k; ++j) { + output.ids[q * k + j] = row[j].second; + output.scores[q * k + j] = + metric == Metric::InnerProduct ? -row[j].first : row[j].first; + } + } + CUDA_CHECK(cudaDeviceSynchronize()); + if (stats) { + cudaDeviceProp prop{}; + CUDA_CHECK(cudaGetDeviceProperties(&prop, 0)); + stats->elapsed_ms = std::chrono::duration( + std::chrono::steady_clock::now() - begin).count(); + stats->peak_workspace_bytes = database.size() * sizeof(float) + + dim * sizeof(float) + max_candidates * (sizeof(float) + sizeof(int)); + stats->device_name = prop.name; + } + return output; +} + +} // namespace gvs diff --git a/09_vector_retrieval/ccyyy1023/src/ivf_flat.cpp b/09_vector_retrieval/ccyyy1023/src/ivf_flat.cpp new file mode 100644 index 00000000..81688267 --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/src/ivf_flat.cpp @@ -0,0 +1,214 @@ +#include "gvs/search.hpp" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace gvs { +namespace { + +float coarse_distance(const float* a, const float* b, std::size_t dim, + Metric metric) { + float value = 0.0f; + if (metric == Metric::L2) { + for (std::size_t d = 0; d < dim; ++d) { + const float diff = a[d] - b[d]; + value += diff * diff; + } + return value; + } + for (std::size_t d = 0; d < dim; ++d) value += a[d] * b[d]; + return -value; +} + +std::size_t nearest_centroid(const float* x, const std::vector& centroids, + std::size_t nlist, std::size_t dim, Metric metric) { + std::size_t best = 0; + float best_value = std::numeric_limits::infinity(); + for (std::size_t c = 0; c < nlist; ++c) { + const float value = coarse_distance(x, centroids.data() + c * dim, + dim, metric); + if (value < best_value) { best_value = value; best = c; } + } + return best; +} + +} // namespace + +namespace { +constexpr char kIvfMagic[8] = {'G','V','S','I','V','F','1','\0'}; +template +void write_value(std::ofstream& out, const T& value) { + out.write(reinterpret_cast(&value), sizeof(T)); + if (!out) throw std::runtime_error("failed to write IVF index"); +} +template +T read_value(std::ifstream& in) { + T value{}; + in.read(reinterpret_cast(&value), sizeof(T)); + if (!in) throw std::runtime_error("truncated IVF index"); + return value; +} +} + +IvfFlatIndex::IvfFlatIndex(std::size_t dim, std::size_t nlist, Metric metric) + : dim_(dim), nlist_(nlist), metric_(metric), lists_(nlist) { + if (dim == 0 || nlist == 0) throw std::invalid_argument("dim/nlist must be positive"); +} + +void IvfFlatIndex::train(const std::vector& database, std::size_t n, + std::size_t iterations, std::uint64_t seed) { + if (database.size() != n * dim_ || n < nlist_) + throw std::invalid_argument("invalid training matrix or n < nlist"); + std::mt19937_64 rng(seed); + std::vector order(n); + std::iota(order.begin(), order.end(), 0); + std::shuffle(order.begin(), order.end(), rng); + centroids_.resize(nlist_ * dim_); + for (std::size_t c = 0; c < nlist_; ++c) + std::copy_n(database.data() + order[c] * dim_, dim_, + centroids_.data() + c * dim_); + + std::vector sums(nlist_ * dim_); + std::vector counts(nlist_); + // Mini-batch Lloyd training bounds build cost on million-vector inputs. + const std::size_t train_count = std::min(n, std::max(4096, nlist_ * 4)); + for (std::size_t iter = 0; iter < iterations; ++iter) { + std::fill(sums.begin(), sums.end(), 0.0f); + std::fill(counts.begin(), counts.end(), 0); + std::shuffle(order.begin(), order.end(), rng); + for (std::size_t sample = 0; sample < train_count; ++sample) { + const std::size_t i = order[sample]; + const std::size_t c = nearest_centroid(database.data() + i * dim_, + centroids_, nlist_, dim_, metric_); + ++counts[c]; + for (std::size_t d = 0; d < dim_; ++d) + sums[c * dim_ + d] += database[i * dim_ + d]; + } + for (std::size_t c = 0; c < nlist_; ++c) { + if (counts[c] == 0) continue; + for (std::size_t d = 0; d < dim_; ++d) + centroids_[c * dim_ + d] = sums[c * dim_ + d] / counts[c]; + } + } +} + +void IvfFlatIndex::add(const std::vector& database, std::size_t n) { + if (centroids_.empty()) throw std::logic_error("train must be called before add"); + if (database.size() != n * dim_) throw std::invalid_argument("invalid database shape"); + database_ = database; + lists_.assign(nlist_, {}); + std::vector labels(n); + #pragma omp parallel for schedule(static) + for (std::size_t i = 0; i < n; ++i) { + labels[i] = nearest_centroid(database.data() + i * dim_, centroids_, + nlist_, dim_, metric_); + } + for (std::size_t i = 0; i < n; ++i) + lists_[labels[i]].push_back(static_cast(i)); +} + +SearchResult IvfFlatIndex::search(const std::vector& queries, + std::size_t nq, std::size_t nprobe, + const SearchConfig& config, + GpuStats* stats) const { + if (database_.empty()) throw std::logic_error("add must be called before search"); + if (queries.size() != nq * dim_) throw std::invalid_argument("invalid query shape"); + nprobe = std::min(nprobe, nlist_); + const std::size_t k = std::min(config.k, database_.size() / dim_); + const auto search_begin = std::chrono::steady_clock::now(); + std::vector> candidate_ids(nq); + + for (std::size_t q = 0; q < nq; ++q) { + std::vector> coarse(nlist_); + for (std::size_t c = 0; c < nlist_; ++c) + coarse[c] = {coarse_distance(queries.data() + q * dim_, + centroids_.data() + c * dim_, dim_, metric_), + static_cast(c)}; + std::partial_sort(coarse.begin(), coarse.begin() + nprobe, coarse.end()); + for (std::size_t p = 0; p < nprobe; ++p) { + const auto& list = lists_[coarse[p].second]; + candidate_ids[q].insert(candidate_ids[q].end(), list.begin(), list.end()); + } + } + GpuStats gpu_stats; + SearchResult output = gpu_indexed_search( + database_, queries, candidate_ids, database_.size() / dim_, nq, dim_, + metric_, SearchConfig{k, 1}, &gpu_stats); + if (stats) { + stats->elapsed_ms = std::chrono::duration( + std::chrono::steady_clock::now() - search_begin).count(); + stats->peak_workspace_bytes = gpu_stats.peak_workspace_bytes; + stats->device_name = gpu_stats.device_name; + } + return output; +} + +void IvfFlatIndex::save(const std::string& path) const { + if (database_.empty() || centroids_.empty()) + throw std::logic_error("cannot save an unbuilt IVF index"); + std::ofstream out(path, std::ios::binary); + if (!out) throw std::runtime_error("cannot open IVF index output: " + path); + out.write(kIvfMagic, 8); + write_value(out, static_cast(database_.size() / dim_)); + write_value(out, static_cast(dim_)); + write_value(out, static_cast(nlist_)); + write_value(out, static_cast(metric_ == Metric::L2 ? 0 : 1)); + out.write(reinterpret_cast(centroids_.data()), + static_cast(centroids_.size() * sizeof(float))); + out.write(reinterpret_cast(database_.data()), + static_cast(database_.size() * sizeof(float))); + for (const auto& list : lists_) { + write_value(out, static_cast(list.size())); + out.write(reinterpret_cast(list.data()), + static_cast(list.size() * sizeof(int))); + } + if (!out) throw std::runtime_error("failed to write IVF payload"); +} + +IvfFlatIndex IvfFlatIndex::load(const std::string& path) { + std::ifstream in(path, std::ios::binary); + if (!in) throw std::runtime_error("cannot open IVF index: " + path); + char magic[8]{}; + in.read(magic, 8); + if (!in || std::memcmp(magic, kIvfMagic, 8) != 0) + throw std::runtime_error("invalid IVF index magic/version"); + const std::size_t n = static_cast(read_value(in)); + const std::size_t dim = read_value(in); + const std::size_t nlist = read_value(in); + const auto metric_code = read_value(in); + if (n == 0 || dim == 0 || nlist == 0 || metric_code > 1 || + n > static_cast(-1) / dim) + throw std::runtime_error("invalid IVF index header"); + IvfFlatIndex index(dim, nlist, + metric_code == 0 ? Metric::L2 : Metric::InnerProduct); + index.centroids_.resize(nlist * dim); + index.database_.resize(n * dim); + in.read(reinterpret_cast(index.centroids_.data()), + static_cast(index.centroids_.size() * sizeof(float))); + in.read(reinterpret_cast(index.database_.data()), + static_cast(index.database_.size() * sizeof(float))); + std::size_t id_count = 0; + for (auto& list : index.lists_) { + const auto size = static_cast(read_value(in)); + if (size > n - id_count) throw std::runtime_error("invalid IVF list sizes"); + list.resize(size); + in.read(reinterpret_cast(list.data()), + static_cast(size * sizeof(int))); + id_count += size; + for (int id : list) + if (id < 0 || static_cast(id) >= n) + throw std::runtime_error("invalid vector id in IVF index"); + } + if (!in || id_count != n) throw std::runtime_error("truncated or incomplete IVF index"); + return index; +} + +} // namespace gvs diff --git a/09_vector_retrieval/ccyyy1023/src/main.cpp b/09_vector_retrieval/ccyyy1023/src/main.cpp new file mode 100644 index 00000000..c8bafe5f --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/src/main.cpp @@ -0,0 +1,259 @@ +#include "gvs/io.hpp" +#include "gvs/search.hpp" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace { + +using Clock = std::chrono::steady_clock; + +class Args { +public: + Args(int argc, char** argv, int start = 2) { + for (int i = start; i < argc; ++i) { + const std::string key = argv[i]; + if (key.rfind("--", 0) != 0 || i + 1 >= argc) + throw std::invalid_argument("expected --key value pairs"); + values_[key.substr(2)] = argv[++i]; + } + } + std::string get(const std::string& key, const std::string& fallback = "") const { + auto it = values_.find(key); + return it == values_.end() ? fallback : it->second; + } + std::string require(const std::string& key) const { + const auto value = get(key); + if (value.empty()) throw std::invalid_argument("missing --" + key); + return value; + } + std::size_t number(const std::string& key, std::size_t fallback) const { + const auto value = get(key); + return value.empty() ? fallback : static_cast(std::stoull(value)); + } +private: + std::map values_; +}; + +gvs::Metric parse_metric(const std::string& value) { + if (value == "l2") return gvs::Metric::L2; + if (value == "inner_product") return gvs::Metric::InnerProduct; + throw std::invalid_argument("metric must be l2 or inner_product"); +} + +double percentile(std::vector values, double p) { + if (values.empty()) return 0.0; + std::sort(values.begin(), values.end()); + const std::size_t index = static_cast( + std::ceil(p * static_cast(values.size()))) - 1; + return values[std::min(index, values.size() - 1)]; +} + +double mean_rank_score_error(const gvs::SearchResult& truth, + const gvs::SearchResult& candidate) { + if (truth.queries != candidate.queries || truth.k != candidate.k) + throw std::invalid_argument("quality result shapes differ"); + double sum = 0.0; + for (std::size_t i = 0; i < truth.scores.size(); ++i) + sum += std::abs(static_cast(truth.scores[i]) - candidate.scores[i]); + return truth.scores.empty() ? 0.0 : sum / truth.scores.size(); +} + +std::string json_escape(const std::string& value) { + std::string out; + for (char c : value) { + if (c == '"' || c == '\\') out.push_back('\\'); + out.push_back(c); + } + return out; +} + +void command_generate(const Args& args) { + const std::size_t n = args.number("n", 10000); + const std::size_t nq = args.number("nq", 100); + const std::size_t dim = args.number("dim", 128); + const auto metric = parse_metric(args.get("metric", "l2")); + const std::uint64_t seed = args.number("seed", 42); + std::mt19937_64 rng(seed); + std::normal_distribution normal(0.0f, 1.0f); + gvs::VectorFile database{n, dim, metric, std::vector(n * dim)}; + gvs::VectorFile queries{nq, dim, metric, std::vector(nq * dim)}; + for (float& value : database.values) value = normal(rng); + for (float& value : queries.values) value = normal(rng); + gvs::write_vector_file(args.require("db"), database, true); + gvs::write_vector_file(args.require("queries"), queries, false); + std::cout << "generated n=" << n << " nq=" << nq << " dim=" << dim << '\n'; +} + +void command_build(const Args& args) { + const auto database = gvs::read_vector_file(args.require("db"), true); + const auto start = Clock::now(); + gvs::IvfFlatIndex index(database.dim, args.number("nlist", 256), database.metric); + index.train(database.values, database.count, args.number("iterations", 12), + args.number("seed", 42)); + index.add(database.values, database.count); + index.save(args.require("index")); + const double ms = std::chrono::duration(Clock::now() - start).count(); + std::cout << "index_build_ms=" << ms << '\n'; +} + +void command_search(const Args& args) { + const auto database = gvs::read_vector_file(args.require("db"), true); + const auto queries = gvs::read_vector_file(args.require("queries"), false); + if (database.dim != queries.dim) throw std::runtime_error("database/query dim mismatch"); + const auto parameters = gvs::read_parameters(args.require("params")); + gvs::SearchConfig config{parameters.top_k, parameters.batch_size}; + gvs::GpuStats stats; + gvs::SearchResult result; + if (parameters.search_mode == "exact") { + result = gvs::gpu_exact_search(database.values, queries.values, + database.count, queries.count, database.dim, + database.metric, config, &stats); + } else { + auto index = gvs::IvfFlatIndex::load(args.require("index")); + result = index.search(queries.values, queries.count, parameters.nprobe, + config, &stats); + } + gvs::write_search_result(args.require("output"), result); + const double qps = stats.elapsed_ms == 0.0 ? 0.0 : queries.count * 1000.0 / stats.elapsed_ms; + std::cout << "mode=" << parameters.search_mode << " query_ms=" << stats.elapsed_ms + << " qps=" << qps << " workspace_mib=" + << stats.peak_workspace_bytes / (1024.0 * 1024.0) << '\n'; +} + +void command_benchmark(const Args& args) { + const auto database = gvs::read_vector_file(args.require("db"), true); + const auto queries = gvs::read_vector_file(args.require("queries"), false); + if (database.dim != queries.dim) throw std::runtime_error("database/query dim mismatch"); + const std::size_t k = args.number("k", 10); + const std::size_t batch = args.number("batch", 128); + const std::size_t nprobe = args.number("nprobe", 16); + const std::size_t cpu_limit = std::min(args.number("cpu-query-limit", 20), queries.count); + const std::size_t latency_limit = std::min(args.number("latency-query-limit", 100), queries.count); + const gvs::SearchConfig config{k, batch}; + + gvs::GpuStats exact_stats; + const auto exact = gvs::gpu_exact_search(database.values, queries.values, + database.count, queries.count, database.dim, + database.metric, config, &exact_stats); + + std::vector cpu_queries(queries.values.begin(), + queries.values.begin() + cpu_limit * database.dim); + const auto cpu_begin = Clock::now(); + const auto cpu = gvs::cpu_exact_search(database.values, cpu_queries, + database.count, cpu_limit, database.dim, + database.metric, k); + const double cpu_ms = std::chrono::duration( + Clock::now() - cpu_begin).count(); + gvs::SearchResult exact_prefix{cpu_limit, exact.k, + std::vector(exact.ids.begin(), exact.ids.begin() + cpu_limit * exact.k), + std::vector(exact.scores.begin(), exact.scores.begin() + cpu_limit * exact.k)}; + const double exact_recall = gvs::recall_at_k(cpu, exact_prefix); + + double build_ms = 0.0; + const std::string index_path = args.require("index"); + if (!std::filesystem::exists(index_path) || args.get("rebuild", "false") == "true") { + const auto build_begin = Clock::now(); + gvs::IvfFlatIndex built(database.dim, args.number("nlist", 256), database.metric); + built.train(database.values, database.count, args.number("iterations", 12), + args.number("seed", 42)); + built.add(database.values, database.count); + built.save(index_path); + build_ms = std::chrono::duration( + Clock::now() - build_begin).count(); + } + auto index = gvs::IvfFlatIndex::load(index_path); + gvs::GpuStats ivf_stats; + const auto approximate = index.search(queries.values, queries.count, nprobe, + config, &ivf_stats); + const double ivf_recall = gvs::recall_at_k(exact, approximate); + const double score_error = mean_rank_score_error(exact, approximate); + + std::vector exact_latencies; + exact_latencies.reserve(latency_limit); + for (std::size_t q = 0; q < latency_limit; ++q) { + std::vector one(queries.values.begin() + q * database.dim, + queries.values.begin() + (q + 1) * database.dim); + gvs::GpuStats one_stats; + gvs::gpu_exact_search(database.values, one, database.count, 1, database.dim, + database.metric, gvs::SearchConfig{k, 1}, &one_stats); + exact_latencies.push_back(one_stats.elapsed_ms); + } + + const double exact_qps = exact_stats.elapsed_ms == 0.0 ? 0.0 + : queries.count * 1000.0 / exact_stats.elapsed_ms; + const double ivf_qps = ivf_stats.elapsed_ms == 0.0 ? 0.0 + : queries.count * 1000.0 / ivf_stats.elapsed_ms; + const double cpu_per_query = cpu_limit == 0 ? 0.0 : cpu_ms / cpu_limit; + const double gpu_per_query = queries.count == 0 ? 0.0 : exact_stats.elapsed_ms / queries.count; + const double speedup = gpu_per_query == 0.0 ? 0.0 : cpu_per_query / gpu_per_query; + + std::ofstream log(args.require("log")); + if (!log) throw std::runtime_error("cannot open benchmark log"); + log << std::fixed << std::setprecision(6) + << "{\n" + << " \"status\": \"measured\",\n" + << " \"device\": \"" << json_escape(exact_stats.device_name) << "\",\n" + << " \"dataset\": {\"n\": " << database.count << ", \"nq\": " + << queries.count << ", \"dim\": " << database.dim << ", \"k\": " << k << "},\n" + << " \"exact\": {\"query_ms\": " << exact_stats.elapsed_ms + << ", \"qps\": " << exact_qps << ", \"p50_ms\": " + << percentile(exact_latencies, 0.50) << ", \"p99_ms\": " + << percentile(exact_latencies, 0.99) << ", \"workspace_mib\": " + << exact_stats.peak_workspace_bytes / (1024.0 * 1024.0) + << ", \"cpu_gpu_recall_at_k\": " << exact_recall + << ", \"speedup_vs_cpu\": " << speedup << "},\n" + << " \"cpu\": {\"queries_measured\": " << cpu_limit + << ", \"query_ms\": " << cpu_ms << "},\n" + << " \"ivf_flat\": {\"index_build_ms\": " << build_ms + << ", \"query_ms\": " << ivf_stats.elapsed_ms << ", \"qps\": " + << ivf_qps << ", \"nlist\": " << index.nlist() << ", \"nprobe\": " + << nprobe << ", \"recall_at_k\": " << ivf_recall + << ", \"mean_rank_score_error\": " << score_error + << ", \"workspace_mib\": " + << ivf_stats.peak_workspace_bytes / (1024.0 * 1024.0) << "}\n" + << "}\n"; + std::cout << "benchmark written to " << args.require("log") << '\n'; +} + +void usage() { + std::cout + << "GPU Vector Search\n" + << " generate --db DB --queries Q --n N --nq NQ --dim D --metric l2|inner_product\n" + << " build --db DB --index INDEX --nlist C [--iterations I]\n" + << " search --db DB --queries Q --params CONFIG --index INDEX --output OUT\n" + << " benchmark --db DB --queries Q --index INDEX --log JSON [--k K --batch B]\n"; +} + +} // namespace + +int main(int argc, char** argv) { + try { + if (argc < 2) { usage(); return 1; } + const std::string command = argv[1]; + const Args args(argc, argv); + if (command == "generate") command_generate(args); + else if (command == "build") command_build(args); + else if (command == "search") command_search(args); + else if (command == "benchmark") command_benchmark(args); + else { usage(); return 1; } + return 0; + } catch (const std::exception& error) { + std::cerr << "error: " << error.what() << '\n'; + return 2; + } +} + diff --git a/09_vector_retrieval/ccyyy1023/tests/test_search.cpp b/09_vector_retrieval/ccyyy1023/tests/test_search.cpp new file mode 100644 index 00000000..d02e24ca --- /dev/null +++ b/09_vector_retrieval/ccyyy1023/tests/test_search.cpp @@ -0,0 +1,72 @@ +#include "gvs/search.hpp" + +#include +#include +#include +#include +#include + +namespace { + +void require(bool condition, const char* message) { + if (!condition) throw std::runtime_error(message); +} + +void test_metric(gvs::Metric metric) { + constexpr std::size_t n = 257; + constexpr std::size_t nq = 7; + constexpr std::size_t dim = 33; + constexpr std::size_t k = 10; + std::vector database(n * dim); + std::vector queries(nq * dim); + for (std::size_t i = 0; i < database.size(); ++i) + database[i] = std::sin(static_cast(i) * 0.013f); + for (std::size_t i = 0; i < queries.size(); ++i) + queries[i] = std::cos(static_cast(i) * 0.017f); + const auto cpu = gvs::cpu_exact_search(database, queries, n, nq, dim, metric, k); + const auto gpu = gvs::gpu_exact_search(database, queries, n, nq, dim, metric, + gvs::SearchConfig{k, 4}); + require(gvs::recall_at_k(cpu, gpu) == 1.0, "GPU exact Top-K differs from CPU"); + for (std::size_t i = 0; i < cpu.scores.size(); ++i) + require(std::abs(cpu.scores[i] - gpu.scores[i]) < 2e-3f, + "GPU score differs from CPU tolerance"); +} + +void test_ivf_roundtrip() { + constexpr std::size_t n = 512; + constexpr std::size_t nq = 8; + constexpr std::size_t dim = 16; + std::vector database(n * dim); + std::vector queries(nq * dim); + for (std::size_t i = 0; i < n; ++i) + for (std::size_t d = 0; d < dim; ++d) + database[i * dim + d] = static_cast(i % 16) * 4.0f + + static_cast((i + d) % 7) * 0.01f; + for (std::size_t q = 0; q < nq; ++q) + std::copy_n(database.data() + q * 16 * dim, dim, queries.data() + q * dim); + gvs::IvfFlatIndex index(dim, 16, gvs::Metric::L2); + index.train(database, n, 8, 7); + index.add(database, n); + index.save("gvs_test_index.bin"); + auto loaded = gvs::IvfFlatIndex::load("gvs_test_index.bin"); + const auto exact = gvs::cpu_exact_search(database, queries, n, nq, dim, + gvs::Metric::L2, 10); + const auto approximate = loaded.search(queries, nq, 4, gvs::SearchConfig{10, 4}); + require(gvs::recall_at_k(exact, approximate) >= 0.90, + "IVF recall is unexpectedly low on clustered test data"); +} + +} // namespace + +int main() { + try { + test_metric(gvs::Metric::L2); + test_metric(gvs::Metric::InnerProduct); + test_ivf_roundtrip(); + std::cout << "all correctness tests passed\n"; + return 0; + } catch (const std::exception& error) { + std::cerr << "test failure: " << error.what() << '\n'; + return 1; + } +}