HIGH PERFORMANCE BIOMEDICAL COMPUTING

高性能生物医学的目标 计算程序是为了识别和解决这些计算 可受益于高性能计算和 通信硬件、现代软件工程原理、 和高效的算法。这一努力包括提供 NIH和NIH的高性能并行计算机系统 面向生物医学的并行算法研究进展 应用。使用高性能并行计算机， 生物医学科学家可以极大地减少 完成计算密集型任务并采用新的 实验数据的处理方法。这可能会允许 在计算中包含更多数据，确定一个 更准确的结果，减少了完成一个 长时间的计算，或者执行新的算法或更多 逼真的模型。具有高带宽网络连接和 交互式用户界面，并行计算很容易访问 提供给实验室或诊所的生物医学研究人员 调查员的计算机工作站。在解决这些问题时 计算挑战、计算生物科学和 工程实验室(CBEL)正在为 广泛的生物医学应用，其中计算速度 很重要。其中包括电子显微照片的图像处理， 医学成像、放射治疗计划、电子 顺磁共振成像和光谱学，人类遗传学 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。最终目标是 拥有高性能的并行计算促进科学 这项工作在美国国立卫生研究院进行。在通过计算开发这些程序时 要求苛刻的应用程序，CBEL正在调查以下内容 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；设计部件，以便计算负载可以 在可用处理器上均匀分布或动态分布 平衡；设计算法，使处理器的数量是 参数和算法可以为 可用的机器；生产所需的开发工具和环境 可移植的并行程序；监控系统性能；以及 证明给定机器上的并行算法满足其 规格。

The goals of the High Performance Biomedical Computing Program are to identify and solve those computational problems that can benefit from high-performance computing and communications hardware, modern software engineering principles, and efficient algorithms. This effort includes providing high-performacne parallel computer systems for the NIH and 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 for 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 calculation, or the implementation of a new algorithm or more realistic model. With high-bandwidth network connections and interactive user interfaces, parallel computing is readily accessible to a biomedical researcher in the laboratory or clinic at the investigator's computer workstation.In addressing these computational challenges, the Computational Bioscience and Engineering Laboratory (CBEL) is developing algorithms for a wide range of biomedical applications where computational speed is important. These include image processing of electron micrographs, medical imaging, radiation treatment planning, electron paramagnetic resonance imaging and spectroscopy, human genetic linkage analysis, protein folding prediction, nuclear magnetic resonance spectroscopy, x-ray crystallography, quantum chemical method, and molecular dynamics simulations. The ultimate goal is to have high-performance, parallel computing facilitate the science that is done at the 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 a parallel algorithm on a given machine meets its specifications.