# GPU-accelerated simulated annealing based on p-bits with real-world device-variability modeling

arXiv: https://arxiv.org/abs/2601.14476 · HTML: https://mlsanity.com/paper/2601.14476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Massively Parallel Probabilistic Computing with Sparse Ising Machines](https://mlsanity.com/paper/2110.02481/) (2021)
- [Solving maximum cut problems by simulated annealing](https://mlsanity.com/paper/1505.03068/) (2015)
- [Recent Advances on the Graph Isomorphism Problem](https://mlsanity.com/paper/2011.01366/) (2020)
- [Physics-inspired Ising Computing with Ring Oscillator Activated p-bits](https://mlsanity.com/paper/2205.07402/) (2022)
- [Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealing](https://mlsanity.com/paper/2304.11839/) (2023)

## Abstract (arXiv)

Probabilistic computing using probabilistic bits (p-bits) presents an efficient alternative to traditional CMOS logic for complex problem-solving, including simulated annealing and machine learning. Realizing p-bits with emerging devices such as magnetic tunnel junctions (MTJs) introduces device variability, which was expected to negatively impact computational performance. However, this study reveals an unexpected finding: device variability can not only degrade but also enhance algorithm performance, particularly by leveraging timing variability. This paper introduces a GPU-accelerated, open-source simulated annealing framework based on p-bits that models key device variability factors -- timing, intensity, and offset -- to reflect real-world device behavior. Through CUDA-based simulations, our approach achieves a two-order magnitude speedup over CPU implementations on the MAX-CUT benchmark with problem sizes ranging from 800 to 20,000 nodes. By providing a scalable and accessible tool, this framework aims to advance research in probabilistic computing, enabling optimization applications in diverse fields.