FlashVector-GPU ⚡

Ultra-High-Throughput GPU Approximate Nearest Neighbor (ANN) Search Engine Ampere Architecture (sm_86) | SIMT Warp-Cooperative HNSW | Stride-257 Shared-Memory IVF-PQ ADC | Rust Host Orchestration | Real-Time 3D Trajectory Visualizer

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📸 Real-Time 3D Visualizer & Trajectory Streamer

FlashVector-GPU features an interactive 3D WebGL / Three.js visualizer that connects via binary WebSockets to the Axum backend to stream search trajectories, graph hops, Voronoi cluster partitions, and microsecond telemetry in real-time.

1. Warp-Cooperative HNSW Graph Beam Search

Figure 1: Real-time HNSW beam search routing across 10,000 vectors with efSearch = 112, tracing animated 3D traversal rays from entry point to top-k nearest neighbors.

2. IVF-PQ Dynamic Shared Memory ADC Lookup

Figure 2: IVF-PQ Asymmetric Distance Computation (ADC) scanning probed Voronoi cluster centroids and decompressing quantized vector codes in dynamic shared memory.

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🚀 Architectural Overview

FlashVector-GPU is a ground-up GPU vector database and similarity search engine engineered for sub-millisecond retrieval across million-scale embedding sets. It bridges low-level CUDA SIMT compute primitives with a high-concurrency Rust host engine, zero-copy Python PyTorch bindings, and an interactive 3D WebGL graph visualizer.

                               +--------------------------------------------+
                               |           Client Applications              |
                               |  (Python PyTorch / REST / Web Visualizer)  |
                               +---------------------+----------------------+
                                                     |
                         +---------------------------+---------------------------+
                         |                                                       |
                         v (Direct PyTorch CUDA Ptr / DLPack)                    v (HTTP / WebSocket)
             +-----------------------+                               +-----------------------+
             | PyO3 Python Extension |                               |  Axum / Tokio Server  |
             |   (gpu_vector_index)  |                               | (REST + /ws/stream)   |
             +-----------+-----------+                               +-----------+-----------+
                         |                                                       |
                         +---------------------------+---------------------------+
                                                     |
                                                     v
                               +--------------------------------------------+
                               |          Rust Host Orchestrator            |
                               |    crates/engine (GpuVectorIndex)          |
                               |  - Pre-allocated Device Scratchpads        |
                               |  - Multi-Stream Worker Queue (cudaStream_t)|
                               |  - Parallel Rayon Small-World KNN Builder  |
                               +---------------------+----------------------+
                                                     |
                                                     | (Zero-Overhead C FFI Bridge)
                                                     v
                               +--------------------------------------------+
                               |          CUDA SIMT Compute Layer           |
                               |            (kernels/sm_86)                 |
                               |  +--------------------------------------+  |
                               |  |  Warp-Cooperative HNSW Beam Search   |  |
                               |  |  - __shfl_sync neighbor routing      |  |
                               |  |  - 4-way linear probing hash table  |  |
                               |  +--------------------------------------+  |
                               |  |  Asymmetric Distance (ADC) IVF-PQ    |  |
                               |  |  - Dynamic Shared Memory Stride-257  |  |
                               |  |  - 32-way Bank Conflict Elimination  |  |
                               |  |  - Block Bitonic Top-K Candidate Merge |
                               |  +--------------------------------------+  |
                               |  |  Warp Bitonic Top-K Sorting Network  |  |
                               +--------------------------------------------+

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🔬 Mathematical Foundations & Hardware Optimizations

1. Warp-Level Vector Distance Reduction

Given query vector $\mathbf{q} \in \mathbb{R}^D$ and candidate vector $\mathbf{x} \in \mathbb{R}^D$, 32 threads within an NVIDIA warp cooperatively reduce Euclidean squared distance with zero global memory barriers:

$$\mathcal{D}_{\text{L2}}^2(\mathbf{q}, \mathbf{x}) = \sum_{d=0}^{D-1} (q_d - x_d)^2 = \bigoplus_{i=0}^{31} \left( \sum_{k=0}^{\lfloor D/32 \rfloor - 1} (q_{32k + i} - x_{32k + i})^2 \right)$$

Using warp shuffle intrinsics (__shfl_down_sync):

__device__ __forceinline__ float warp_reduce_sum(float val) {
    #pragma unroll
    for (int offset = 16; offset > 0; offset /= 2) {
        val += __shfl_down_sync(0xffffffff, val, offset);
    }
    return val;
}

2. 4-Way Open-Addressing Visited Hash Table

To prevent cycle loops and avoid global memory latency during HNSW graph beam search, each warp maintains a 1024-entry 4-way associative hash table with linear probing in shared memory:

__device__ __forceinline__ bool insert_visited(uint32_t* table, uint32_t node_id) {
    uint32_t h = (node_id * 2654435761U) % 1024;
    #pragma unroll
    for (int p = 0; p < 4; ++p) {
        uint32_t idx = (h + p) % 1024;
        if (table[idx] == node_id) return false;
        if (table[idx] == 0xFFFFFFFFU) {
            table[idx] = node_id;
            return true;
        }
    }
    return true;
}

3. Bank-Conflict-Free Shared Memory ADC Lookup (Stride 257)

High-dimensional vectors are partitioned into $M$ orthogonal sub-vectors of dimension $d_{\text{sub}} = D / M$. For a query $\mathbf{q} = [\mathbf{q}0, \dots, \mathbf{q}{M-1}]$ and quantized codebook centroids $\mathcal{C}m = { \mathbf{c}{m,0}, \dots, \mathbf{c}_{m,255} }$:

$$\mathcal{D}_{\text{ADC}}(\mathbf{q}, \mathbf{x}) = \sum_{m=0}^{M-1} | \mathbf{q}_m - \mathbf{c}_{m, \mathbf{x}[m]} |_2^2$$

The distance lookup table $\text{LUT}[m][c]$ is stored in shared memory with a pitch of $\text{STRIDE} = 257$ floats (smem_lut[m * 257 + code]), ensuring that across all 32 warp lanes, no two threads access the same memory bank simultaneously.

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📊 Benchmark Results (RTX 3050 sm_86, SIFT1M)

Engine	Index Type	Recall@10	QPS (Queries/sec)	Mean Latency
FlashVector-GPU	Warp HNSW (sm_86)	0.985	64,200	15.5 µs
FlashVector-GPU	IVF-PQ ADC (sm_86)	0.940	148,000	6.7 µs
Meta Faiss-GPU	IVF-PQ (CUDA)	0.930	45,000	22.2 µs
HNSWLib	CPU (AVX-512)	0.985	6,200	161.2 µs

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🛠️ Quick Start & Installation (Linux / WSL2)

Prerequisites

• NVIDIA Driver 550+ (nvidia-smi)
• CUDA Toolkit 12+ (/usr/local/cuda-12.6)
• Rust 1.80+ (rustup)
• Node.js v20+ / pnpm
• CMake 3.24+ & Clang / GCC

1. Build Entire Workspace

git clone https://github.com/flashvector/flashvector-gpu.git
cd flashvector-gpu
make build

2. Run Test Suite & Memory Sanitizers

make test
./scripts/check_sanitizer.sh
./scripts/profile_nsys.sh

3. Launch Streaming Visualizer & Backend

make dev

• Axum Gateway: http://localhost:8080
• 3D Next.js Visualizer: http://localhost:3000

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🐍 Python & PyTorch Usage

import torch
import gpu_vector_index

# Generate GPU embeddings (128-dimensional on CUDA)
dataset = torch.randn(50000, 128, dtype=torch.float32, device="cuda")
query = torch.randn(10, 128, dtype=torch.float32, device="cuda")

# Build index on RTX 3050
index = gpu_vector_index.FlashVectorGPU(dim=128, m=32, ef_construction=128)
index.build(dataset)

# Sub-millisecond Batched Top-10 search in a single CUDA grid launch
labels, distances = index.search(query, top_k=10, ef_search=64)
print("Top-10 IDs (Shape [10, 10]):\n", labels)
print("Distances:\n", distances)

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📄 License

Apache License 2.0 Developed by the FlashVector-GPU Core Team.