The analysis of complex biological datasets beyond DNA scenarios is gaining increasing interest in current bioinformatics. Particularly, protein sequence data introduce additional complexity layers that impose new challenges from a computational perspective. This work is aimed at investigating GPU solutions to address these issues in a representative algorithm from the phylogenetics field: Fitch’s parsimony. GPU strategies are adopted in accordance with the protein-based formulation of the problem, defining an optimized kernel that takes advantage of data parallelism at the calculations associated with different amino acids. In order to understand the relationship between problem sizes and GPU capabilities, an extensive evaluation on a wide range of GPUs is conducted, covering all the recent NVIDIA GPU architectures—from Kepler to Turing. Experimental results on five real-world datasets point out the benefits that imply the exploitation of state-of-the-art GPUs, representing a fitting approach to address the increasing hardness of protein sequence datasets.

中文翻译：

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Fitch 对蛋白质数据简约的 GPU 加速：从开普勒到图灵

对 DNA 场景之外的复杂生物数据集的分析对当前的生物信息学越来越感兴趣。特别是，蛋白质序列数据引入了额外的复杂性层，从计算的角度来看，这带来了新的挑战。这项工作旨在研究 GPU 解决方案，以解决系统发生学领域的代表性算法中的这些问题：Fitch 的简约。根据问题的基于蛋白质的公式采用 GPU 策略，定义优化的内核，该内核在与不同氨基酸相关的计算中利用数据并行性。为了理解问题大小和 GPU 能力之间的关系，我们对各种 GPU 进行了广泛的评估，涵盖了所有最近的 NVIDIA GPU 架构——从 Kepler 到 Turing。