HIGH PERFORMANCE BIOMEDICAL COMPUTING

高性能生物医学计算计划的目标是 识别和解决生物医学中可以受益的计算问题 从高性能计算和通信硬件，到现代软件 工程原理和高效算法。这一努力包括 为NIH的工作人员提供高性能的并行计算机系统 用于生物医学应用的并行算法的发展。 使用高性能并行计算机，生物医学科学家可以大大 减少完成计算密集型任务所需的时间 采用新的方法处理实验数据。这可能会允许 在计算中包含更多数据，确定更多 准确的结果，减少了完成长时间 计算，或者新算法的实现，或者更现实 模特。具有适当的计算机网络连接和交互用户 接口，并行计算可以很容易地用于生物医学 实验室或诊所的研究人员在研究人员的计算机前 工作站。 为了解决这些计算挑战，CBEL正在开发算法 对于许多生物医学应用，其中计算速度是 很重要。其中包括电子显微照片的图像处理， 放射治疗计划、医学成像、蛋白质和核酸 序列分析、人类遗传连锁分析、蛋白质折叠 预测，核磁共振光谱学，X射线结晶学， 量子化学方法和分子动力学模拟。终极的 目标是让高性能的并行计算促进科学 这是美国国立卫生研究院的研究成果。在开发这些计算要求的同时 应用程序，CBEL正在研究以下高性能计算 问题：将问题划分为可以独立的多个部分 在不同的处理器上执行；设计部件以使计算 负载可以在可用处理器上均匀分布，也可以动态分布 平衡；设计算法，使处理器的数量是 参数和算法可以针对可用的 机床.生产便携式并联机床的开发工具和环境 程序；监视系统性能，并证明并行性 给定机器上的算法符合IT规范。

The goals of the High Performance Biomedical Computing Program are to identify and solve computational problems in biomedicine that can benefit from high performance computing and communication hardware, modern software engineering principles, and efficient algorithms. This effort includes providing high performance parallel computer systems for the NIH staff and the development of parallel algorithms for biomedical applications. Using high performance parallel computer, biomedical scientists can greatly reduce the time it takes to complete computationally intensive tasks and adopt new approaches for processing experimental data. This may allow the inclusion of more data in a calculation, the determination of a more accurate result, a reduction in the time needed to complete a long computation, or the implementation of a new algorithm or more realistic model. With proper computer network connections and interactive user interface, parallel computing is readily available to a biomedical researcher in the laboratory or clinic at the investigator's computer workstation. In addressing these computational challenges, CBEL is developing algorithms for a number of biomedical applications where computational speed is important. These include image processing of electron micrographs, radiation treatment planning, medical imaging, protein and nucleic acid sequence analysis, human genetic linkage analysis, protein folding prediction, nuclear magnetic resonance spectroscopy, x-ray crystallography, quantum chemical methods, and molecular dynamics simulations. The ultimate goal is to have high performance parallel computing facilitate the science that is researched at NIH. While developing these computational demanding applications, CBEL is investigating the following high performance computin issues: partitioning a problem into many parts that can be independently executed on a different processor; designing the parts so that the computin load can be distributed evenly over the available processors or dynamically balanced; designing algorithms so that the number of processors is a parameter and the algorithms can be configured dynamically for the availabl machine; developing tools and environments for producing portable parallel programs; monitoring system performance, and proving that parallel algorithms on a given machine meets it specifications.