fuzzgpu

Hardware-Accelerated Fuzzy String Matching & Sequence Alignment

Cross-platform GPU compute via WebGPU (wgpu) & Multi-Core CPU parallelism with Rayon. Zero CUDA dependencies.

Overview

fuzzgpu is a high-throughput string distance and sequence alignment engine written in Rust with native Python and WebAssembly bindings. It leverages GPU compute shaders (wgpu / WGSL) and Rayon multi-threading to accelerate large-scale batch queries and distance matrix computations across:

• Apple Silicon (Metal)
• Linux (Vulkan)
• Windows (DirectX 12 / Vulkan)
• Integrated GPUs (Intel Iris Xe, AMD Radeon)
• WebAssembly (In-browser execution)

No NVIDIA CUDA drivers or complex toolkits required.

Benchmark Results

Hardware: Intel(R) Iris(R) Xe Graphics (Vulkan) / Intel Core i7 CPU

1. Damerau-Levenshtein Batch (1 Query × N Candidates)

2. Levenshtein Cross-Product Matrix (cdist $N \times M$)

Utilizing dedicated 2D Grid Workgroup Shaders with $O(N + M)$ memory bandwidth:

3. Jaro-Winkler Similarity Batch

Installation

Python

pip install fuzzgpu

Rust (Cargo.toml)

[dependencies]
fuzzgpu-core = "0.1.1"

Quickstart

import fuzzgpu
from fuzzgpu.fuzz import ratio, partial_ratio, token_sort_ratio, token_set_ratio, extract, extractOne

# 1. Classical Distance Metrics
lev = fuzzgpu.levenshtein_distance("kitten", "sitting")         # 3
dam = fuzzgpu.damerau_levenshtein_distance("ab", "ba")          # 1 (transposition-aware)
jw  = fuzzgpu.jaro_winkler_similarity("MARTHA", "MARHTA", 0.1)  # 0.9611

# 2. High-Throughput Batch Processing (Auto-dispatched to GPU/CPU)
candidates = ["hallo", "hullo", "jello", "yellow", "hello world"] * 10_000
distances  = fuzzgpu.levenshtein_batch("hello", candidates)
jw_scores  = fuzzgpu.jaro_winkler_batch("hello", candidates, prefix_weight=0.1)

# 3. 2D Cross-Product Distance Matrix (Dedicated 2D Grid Shader)
matrix = fuzzgpu.levenshtein_cdist(["abc", "def", "xyz"], ["abd", "axy", "def"])

# 4. Global Sequence Alignment (Gotoh 1982 Linear & Affine Gap Penalties)
score_linear = fuzzgpu.needleman_wunsch_score("AGTACGCA", "TATGC", match=2, mismatch=-1, gap=-2)
score_affine = fuzzgpu.needleman_wunsch_affine("AGTACGCA", "TATGC", match=2, mismatch=-1, gap_open=-3, gap_extend=-1)

# 5. RapidFuzz-Compatible Scorer & Search API
score = ratio("fuzzy was a bear", "fuzzy was a bear")          # 100.0
part  = partial_ratio("hello", "oh hello there")              # 100.0
tsr   = token_sort_ratio("new york mets", "mets new york")     # 100.0
tset  = token_set_ratio("fuzzy was a bear", "fuzzy bear")      # 100.0

# 6. Top-K Best Match Search
best  = extractOne("hellp", ["hello", "world", "help"], score_cutoff=50.0)
# Output: ("hello", 80.0, 0)

top_3 = extract("apple", ["apply", "ape", "banana", "applesauce"], score_cutoff=50.0, limit=3)

# 7. Hardware Diagnostics
print(fuzzgpu.gpu_info())
# Output: Intel(R) Iris(R) Xe Graphics (Vulkan) / Apple M2 (Metal)

Technical Architecture

fuzzgpu combines a tiered execution pipeline to balance low-latency single queries and high-throughput batch workloads:

                          ┌──────────────────────────┐
                          │     User Query / API     │
                          └─────────────┬────────────┘
                                        │
                         Batch Size / Dataset Assessment
                                        │
                ┌───────────────────────┴───────────────────────┐
                ▼                                               ▼
     Small Workloads (< 500)                         Large Batches (≥ 500)
                │                                               │
   ┌───────────────────────────┐                 ┌───────────────────────────┐
   │    Rayon Multi-Threaded   │                 │     wgpu WebGPU Compute   │
   │      CPU Parallelism      │                 │  Shaders (Metal/Vulkan)   │
   │  - Myers 1999 Bit-Vector  │                 │  - 2D Workgroup Grids     │
   │  - Zero PCIe Latency      │                 │  - Streaming Chunking     │
   └───────────────────────────┘                 └───────────────────────────┘

Key Architectural Optimizations

1. 2D Grid Matrix Shaders (levenshtein_matrix.wgsl & jaro_matrix.wgsl): Instead of duplicating string pairs across PCIe ($O(N \cdot M)$ transfers), List A and List B are uploaded once ($O(N + M)$ memory bandwidth). Workgroups execute on a 2D grid (@workgroup_size(16, 16)).
2. Myers (1999) Bit-Parallel CPU Engine: For strings $\le 64$ characters, computes Levenshtein edit distance using bit-vector operations with zero inner dynamic programming loops ($O(N)$ execution).
3. Lowrance & Wagner (1975) Unrestricted Damerau-Levenshtein: Full support for character insertions, deletions, substitutions, and arbitrary transpositions.
4. Gotoh (1982) Affine Gap Sequence Alignment: Memory-efficient 3-state recurrence ($O(N)$ auxiliary space) for bioinformatics and long-sequence alignment.
5. Streaming Chunk Partitioner: Datasets exceeding GPU buffer limits (>128MB or >500,000 pairs) are automatically streamed in chunks to prevent VRAM overflow.

Project Structure

fuzzgpu/
├── assets/
│   └── logo.svg               # Vector brand asset
├── crates/
│   ├── fuzzgpu-core/          # Core Rust engine & compute shaders
│   │   ├── src/
│   │   │   ├── gpu.rs         # wgpu instance and device singleton
│   │   │   ├── levenshtein.rs # Levenshtein kernel & 2D matrix dispatch
│   │   │   ├── damerau.rs     # Lowrance-Wagner Damerau-Levenshtein
│   │   │   ├── needleman.rs   # Needleman-Wunsch (Linear & Affine)
│   │   │   ├── jaro.rs        # Jaro / Jaro-Winkler GPU & CPU kernels
│   │   │   ├── fuzz.rs        # Fuzzy ratio, token sort/set, extract
│   │   │   ├── simd.rs        # Myers bit-vector algorithms
│   │   │   └── shaders/       # WGSL compute shaders (1D & 2D)
│   ├── fuzzgpu-python/        # PyO3 CPython C-extension module
│   └── fuzzgpu-wasm/          # wasm-bindgen WebAssembly module
├── python/
│   └── fuzzgpu/               # Python package wrapper & typing
├── tests/
│   └── test_basic.py          # Comprehensive test suite (50 tests)
└── benchmarks/
    └── bench_compare.py       # Comparative benchmarking harness

Building from Source

Prerequisites

• Rust Toolchain (1.75+)
• Python 3.8+ & pip install maturin

Build Python Extension

# Clone the repository
git clone https://github.com/Flaxmbot/fuzzgpu.git
cd fuzzgpu

# Build and install into current virtual environment
maturin develop --release

Run Tests & Benchmarks

# Run pytest verification suite
pytest tests/ -v

# Run comparative benchmark harness
python benchmarks/bench_compare.py

Build WebAssembly (Browser Target)

cd crates/fuzzgpu-wasm
wasm-pack build --target web --release

License

This project is licensed under the MIT License.