HIGH PERFORMANCE BIOMEDICAL COMPUTING

高性能生物医学计算程序的目标是 识别和解决生物医学中的那些计算问题 受益于高性能硬件、现代软件工程 原则和高效的算法。这一努力包括提供高 NIH工作人员使用的高性能并行计算机系统及其开发 用于生物医学应用的并行算法。 使用高性能并行计算机，生物医学科学家可以 大大减少了完成计算密集型任务所需的时间 任务，并采取新的方法处理他们的数据。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 可受益于多种生物医学应用的算法 计算加速，包括电子显微图像处理， 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、核磁共振波谱、x射线 结晶学、量子化学方法和分子动力学 模拟。最终目标是拥有高性能的并行 计算促进了NIH所做的科学工作。在发展中 这些计算要求很高的应用程序，CBEL正在调查 以下是高性能计算问题：将问题划分为 许多部件可以在不同的处理器上独立执行； 设计算法，使处理器间通信的延迟可以 保持计算时间的一小部分；这样设计部件 计算负载可以均匀分布在可用的 处理器或动态平衡；设计算法以使 处理器数量是一个参数，可以配置算法 为可用机器动态配置；开发工具和环境 用于生成可移植的并行程序和监控系统 性能；并证明了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 those computational problems in biomedicine that can benefit from high performance hardware, modern software engineering principles, and efficient algorithms. This effort includes providing high performance parallel computer systems for the NIH staff and developing parallel algorithms for biomedical applications. Using high performance parallel computers, biomedical scientists can greatly reduce the time it takes to complete computationally intensive tasks and take new approaches in processing their 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 that can benefit from computational speedup, including 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 algorithms so that delays of interprocessor communication can be kept to a small fraction of the computation time; 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 and monitoring system performance; and proving that a parallel algorithm on a given machine meets its specifications.