L-yang-yang

L-yang-yang / cugenopt

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A GPU-accelerated general-purpose metaheuristic framework for combinatorial optimization

19
4
100% credibility
Found Mar 26, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Cuda
AI Summary

A GPU-powered toolkit for rapidly solving complex planning problems like traveling salesman routes, vehicle deliveries, and knapsack packing using smart adaptive searches.

How It Works

1
🔍 Discover cuGenOpt

You stumble upon this handy tool while hunting for smart ways to tackle tough planning puzzles like finding the shortest delivery routes or best packing arrangements.

2
📦 Get it ready

You quickly set up the tool on your computer so it's all prepared for action.

3
📊 Add your numbers

You input simple lists of details, like distances between spots or sizes of items to pack.

4
🚀 Hit solve

With one easy go, it uses your computer's speedy graphics power to hunt down the smartest solution lightning-fast.

5
🔧 Tweak for special cases

If your puzzle is unique, you adjust the instructions a touch to fit perfectly.

🎉 Enjoy top results

You receive the perfect plan or arrangement in moments, saving tons of time over old-school methods.

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Star Growth

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AI-Generated Review

What is cugenopt?

cuGenOpt is a GPU-accelerated, general-purpose metaheuristic framework for combinatorial optimization problems like TSP, VRP, knapsack, and QAP. Developers pip-install the Python package and call functions like solve_tsp(distance_matrix) or solve_knapsack(weights, values) to get solutions in seconds on NVIDIA GPUs via CUDA. It also lets you define custom problems by pasting CUDA snippets into solve_custom, with JIT compilation for one-off needs.

Why is it gaining traction?

It crushes CPU baselines like OR-Tools or MIP solvers on medium-large instances, hitting optimal TSP/VRPTW solutions with 0% gap in benchmarks, and scales linearly with GPU bandwidth (3.6x on A800 vs T4). The Python API hides CUDA complexity, auto-tunes population sizes to L2 cache, and supports multi-GPU for parallel runs. Adaptive operator selection kicks in at runtime, dodging cold starts better than static heuristics.

Who should use this?

Optimization researchers prototyping on TSPLIB/CVRPLIB instances beyond n=100, logistics engineers solving daily VRPs at scale, or ML ops teams needing fast graph coloring/bin packing without Gurobi licenses. Skip if you're stuck on exact solvers for tiny problems or lack Volta+ GPUs.

Verdict

Grab it for GPU-heavy combinatorial workloads—benchmarks impress despite 19 stars and 1.0% credibility score signaling early days. Polish the docs and add Windows CI, but the API delivers now; test on your hardware before committing.

(198 words)

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