Computational Pangenomics is an emerging field that studies genetic variation using a graph structure encompassing multiple genomes. Visualizing pangenome graphs is vital for understanding genome diversity. Yet, handling large graphs can be challenging due to the high computational demands of the graph layout process. In this work, we conduct a thorough performance characterization of a state-of-the-art pangenome graph layout algorithm, revealing significant data-level parallelism, which makes GPUs a promising option for compute acceleration. However, irregular data access and the algorithm's memory-bound nature present significant hurdles. To overcome these challenges, we develop a solution implementing three key optimizations: a cache-friendly data layout, coalesced random states, and warp merging. Additionally, we propose a quantitative metric for scalable evaluation of pangenome layout quality. Evaluated on 24 human whole-chromosome pangenomes, our GPU-based solution achieves a 57.3x speedup over the state-of-the-art multithreaded CPU baseline without layout quality loss, reducing execution time from hours to minutes.

翻译：计算泛基因组学是一个新兴领域，它利用包含多个基因组的图结构来研究遗传变异。可视化泛基因组图对于理解基因组多样性至关重要。然而，由于图布局过程的高计算需求，处理大型图形可能具有挑战性。在这项工作中，我们对一种最先进的泛基因组图布局算法进行了全面的性能表征，揭示了显著的数据级并行性，这使得 GPU 成为加速计算的一个有前景的选择。然而，不规则的数据访问和算法的内存受限特性带来了重大障碍。为了克服这些挑战，我们开发了一个解决方案，实现了三个关键优化：缓存友好的数据布局、合并随机状态以及线程束合并。此外，我们提出了一种用于可扩展评估泛基因组布局质量的定量指标。在 24 个人类全染色体泛基因组上评估，我们基于 GPU 的解决方案在没有布局质量损失的情况下，比最先进的多线程 CPU 基线实现了 57.3 倍的加速，将执行时间从数小时缩短至数分钟。