danalec

danalec / DMMSY-SSSP

Public

Experimental C implementation of “Breaking the Sorting Barrier for Directed Single-Source Shortest Paths” by Ran Duan, Jiayi Mao, Xiao Mao, Xinkai Shu, and Longhui Yin (STOC 2025)

78
1
100% credibility
Found Feb 23, 2026 at 48 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
C
AI Summary

High-performance implementation of an advanced shortest path algorithm for large networks, with built-in tests showing huge speedups over traditional approaches.

How It Works

1
🔍 Discover DMMSY

You hear about a breakthrough tool that finds the shortest routes from one spot to everywhere else in huge networks, way faster than usual.

2
📥 Get the files

Download the ready-to-use files from the project page to your computer.

3
🛠️ Prepare the tool

Follow the easy guide to set up the program so it's ready to run.

4
⚡ Run performance tests

Start tests on big sample networks and watch it solve them in a flash.

5
📊 Check the results

See tables and charts proving massive speed gains over standard methods.

🎉 Unlock super speed

Celebrate having a powerful helper for quick pathfinding in your big projects!

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Star Growth

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AI-Generated Review

What is DMMSY-SSSP?

DMMSY-SSSP is an experimental C implementation of a breakthrough SSSP algorithm from STOC 2025 that computes shortest paths from a single source on directed graphs with non-negative weights. It targets large-scale sparse graphs, delivering results via a simple API that takes a graph in CSR format, source node, and outputs distances and predecessors. Developers get a drop-in replacement for Dijkstra with blazing speed on graphs up to 1M+ nodes, plus a built-in benchmark suite for random graph generation and timing comparisons.

Why is it gaining traction?

This experimental GitHub project breaks the long-standing sorting barrier in SSSP, slashing time complexity and yielding 20,000x speedups over standard Dijkstra on million-node graphs, as shown in its benchmark data. Users notice near-constant execution times around 800ns for 1M-node runs, zero-allocation design for low overhead, and cache-optimized handling of sparse data. The hook is plug-and-play performance on real-world scales where heaps choke.

Who should use this?

Graph algorithm researchers testing 2025 barrier-breaking techniques, backend engineers routing in massive directed networks like logistics or social graphs, and HPC devs simulating wave propagation in disordered media on sparse topologies. Ideal for anyone benchmarking SSSP on 250k-1M node datasets where traditional tools lag.

Verdict

Grab it if you're chasing experimental speedups on huge sparse directed graphs—benchmarks and correctness tests look solid—but treat as research code with its 1.0% credibility score and 42 stars signaling early maturity. Polish docs and add real-world loaders before production.

(198 words)

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