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在后基因组时代,基因结构、功能及其与疾病的关系已成为生物医学的研究热点。在各种实验技术飞速发展的促进下,高复杂度、多元化、海量的生物数据正以指数级的速度不断增长,关于这些数据的存储、计算、分析等已成为严峻挑战。以超级计算机为平台的高性能计算是解决此类难题的有效方法之一。根据对本院超级刀片计算机的使用情况,本文从数据存储、通讯优化、计算效率、资源分配等角度,分析和总结提高任务并行的生物计算程序效率的技术策略,并进一步展望其在生物医学中的应用前景。这些技术策略对于使用同类高性能计算机具有重要的参考价值。
In the post-genome era, the relationship between gene structure, function and disease has become a hot research topic in biomedicine. With the rapid development of various experimental techniques, the biological data of high complexity, diversity and magnanimity are growing exponentially, and the storage, calculation and analysis of these data have become serious challenges. HPC-based HPC is one of the effective ways to solve such problems. Based on the use of super blade computers in our hospital, this paper analyzes and summarizes the technical strategies to improve the efficiency of parallel computing tasks in terms of data storage, communication optimization, computational efficiency, resource allocation and so on, and further prospects its application in biomedicine The application prospects. These technical strategies for the use of similar high-performance computer has an important reference value.