用GPU加速的肯德尔距离计算,用于大或稀疏的数据
Pavel Akhtyamov1,2, Ausaaf Nabi1,2, Vladislav Gafurov1,2
1Department of Biomedical Physics, Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Russia.
GigaScience
|December 10, 2024
概括
GPU辅助距离估计软件 (GADES) 加快Kendall-tau距离矩阵计算大,稀疏的数据集. 这种计算策略允许使用图形处理单元 (GPU) 高效处理大数据.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 数据科学是数据科学.
背景情况:
- 实验实践产生了大型的多维数据集.
- 距离矩阵计算对于数据预处理至关重要,但计算密集.
- 数据稀疏性在各种领域提出了算法挑战,包括单细胞测序.
研究的目的:
- 开发一个GPU增强的包,用于快速计算肯德尔-陶距离矩阵.
- 在大型数据集中解决计算需求和数据稀疏性挑战.
主要方法:
- 开发了GPU辅助距离估计软件 (GADES),用于大规模并行的肯达尔-陶距离计算.
- 实现了特定的内存管理,以克服GPU内存限制.
- 集成的算法解决方案来处理数据稀疏性和增强加速.
主要成果:
- 与基于CPU的包相比,GADES在模拟和真实单细胞测序数据上显示出明显更高的速度.
- 该软件有效地处理稀疏和密集的数据集.
- 在稀疏的数据处理中观察到额外的性能提升.
结论:
- GADES显著推进了高性能肯德尔距离矩阵计算的计算策略.
- 该软件能够有效地利用GPU功率进行大数据处理.
- GADES是公开使用和进一步开发的.
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