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的计算问题， 受益于高性能计算和通信硬件， 现代软件工程原理和高效算法。 这 努力包括提供高性能并行计算机系统 为国家卫生研究院的工作人员和并行算法的发展， 生物医学应用 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 任务，并采用新的方法来处理实验数据。 这 可以允许在计算中包括更多的数据， 一个更准确的结果，减少所需的时间来完成 一个长时间的计算，或者一个新算法的实现， 现实模型 通过适当的计算机网络连接， 交互式用户界面，并行计算是很容易获得的， 研究者所在实验室或诊所的生物医学研究人员 计算机工作站 为了应对这些计算挑战，CBEL正在开发 许多生物医学应用的算法，其中计算 速度很重要。 这些包括电子的图像处理 显微照片，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，核磁共振波谱，x射线 晶体学、量子化学方法和分子动力学 模拟 最终目标是拥有高性能并行 计算促进了NIH的科学研究。 而发展中 这些计算要求很高的应用，CBEL正在研究 以下高性能计算问题：分区问题 分成许多部分，可以在不同的 处理器;设计部件，以便计算负载可以 均匀地分布在可用的处理器上或者动态地 平衡;设计算法，使处理器的数量是一个 参数和算法可以动态配置， 可用的机器;开发生产工具和环境 可移植并行程序;监视系统性能和证明 给定机器上的并行算法符合其规格。

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 a 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 work-station. 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 done at NIH. While developing these computationally demanding applications, CBEL is investigating the following high performance computing issues: partitioning a problem into many parts that can be independently executed on different processors; designing the parts so that the computing 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 available machine; developing tools and environments for producing portable parallel programs; monitoring system performance, and proving that parallel algorithms on a given machine meets its specifications.