AI 中文总结
本文针对马尔可夫链池解码（MCPD）在GPU上的并行化问题，提出得分加权更新方案并结合无偏MCMC框架，实验显示其在高噪声下的克隆恢复效果优于均匀解码器。
AI 中文摘要
Knill等人（1996）提出的马尔可夫链池解码（MCPD）可从带噪声的池检测结果中识别出可能的阳性克隆。标准MCPD使用吉布斯采样估计克隆的后验概率，但可能会对低分克隆分配过多计算资源。本文专注于在GPU架构上并行化MCPD：与标准MCPD采用系统扫描更新不同，我们提出了一种得分加权更新方案，会更频繁地更新高分克隆；我们证明了所提马尔可夫链的平稳分布与目标后验分布一致。为实现高效GPU并行化，我们进一步融入了Jacob等人（2020）的无偏MCMC框架，并采用基于Glynn和Heidelberger（1991）关于马尔可夫链耦合论点的槽重填技术。涉及1298个克隆、97个池及3个真实阳性样本的实验表明，与均匀解码器相比，本文方法的恢复效果得到提升，且在高噪声条件下仍能保持高重叠度。
英文摘要
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.
