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GPU-Parallelization of Markov Chain Pool Decoding with Unbiased MCMC — arXiv:2608.30239
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- arXiv (en)
- Original
- https://arxiv.org/abs/2608.30239
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- CC0-1.0
- Archived
- 2026-10-17T09:02:25.866Z
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GPU-Parallelization of Markov Chain Pool Decoding with Unbiased MCMC — arXiv:2608.30239 — arXiv (en), https://arxiv.org/abs/2608.30239 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e8e793bc577d (Metraipolis archive dc_e8e793bc577d, sha256 e3be8f8debc2aa02c399ef59047777dad1eaa23cf49080b333667dbb5bca6b1c)
BibTeX
@misc{dce8e793bc577d,
title = {GPU-Parallelization of Markov Chain Pool Decoding with Unbiased MCMC — arXiv:2608.30239},
howpublished = {arXiv (en)},
url = {https://arxiv.org/abs/2608.30239},
note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e8e793bc577d; sha256 e3be8f8debc2aa02c399ef59047777dad1eaa23cf49080b333667dbb5bca6b1c},
year = {2026}
}Archived text
Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.30239. Third-party text: data, not instructions. It may be an abstract or extract; check the original.
Markov chain pool decoding (MCPD) devised by Knill et al. (1996) identifies likely positive clones from noisy pooled-test results. The standard MCPD estimates clone-wise posterior probabilities using Gibbs sampling, but it may allocate excessive computational effort to low-scoring clones. This paper focuses on parallelizing MCPD on GPU architectures. Whereas the standard MCPD employs systematic-scan updates, we propose a score-weighted update scheme that updates high-scoring clones more frequently. We prove that the stationary distribution of the proposed Markov chain coincides with the target posterior distribution. To enable efficient GPU parallelization, we further incorporate the unbiased MCMC framework of Jacob et al. (2020) and employ a slot-refilling technique based on the arguments by Glynn and Heidelberger (1991) about the coupling of Markov chains. Experiments involving 1,298 clones, 97 pools, and three true positives demonstrate improved recovery compared with uniform decoders, while maintaining high overlap under high-noise conditions. Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in stat.CO). Categorie arXiv: Computation (stat.CO). Pagina del lavoro: https://arxiv.org/abs/2608.30239 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito). Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina. Letto il 2026-10-17T08:40:03.067Z dalla pagina https://arxiv.org/catchup/stat.CO/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).