HIGH PERFORMANCE BIOMEDICAL COMPUTING

高性能生物医学计算计划的目标是识别和解决那些复杂的生物医学问题，这些问题可以从高性能计算和通信硬件、现代软件工程原理和高效算法中受益。这项工作包括为美国国立卫生研究院提供高性能并行计算机系统和为生物医学应用开发并行算法。使用高性能并行计算机，生物医学科学家可以大大减少完成计算密集型任务所需的时间，并采用新的方法处理实验数据。这可能允许在计算中包含更多的数据，确定更准确的结果，减少完成长计算所需的时间，或者实现新的算法或更现实的模型。通过高带宽网络连接和交互式用户界面，实验室或诊所的生物医学研究人员可以在研究人员的计算机工作站轻松访问并行计算。为了解决这些计算挑战，计算生物科学与工程实验室（CBEL）正在开发用于广泛生物医学应用的算法，其中计算速度和先进的可视化技术非常重要。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、人类遗传连锁分析、cDNA微阵列数据分析、蛋白质折叠预测、核磁共振光谱、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，并行计算促进在NIH进行的科学研究。在开发这些计算要求很高的应用程序时，CBEL正在研究以下高性能计算问题：将问题划分为可以在不同处理器上独立执行的许多部分；设计部件，使计算负载可以在可用的处理器上均匀分布或动态平衡；设计算法，使处理器数量成为一个参数，算法可以为可用的机器动态配置；开发生产可移植并行程序的工具和环境；将实时数据可视化纳入用户环境，监控系统性能；并证明在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 complex biomedical problems that can benefit from high performance computing and communications hardware, modern software engineering principles, and efficient algorithms. This effort includes providing high performance 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 investigators 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 and advanced visualization techniques are important. These include image processing of electron micrographs, medical imaging, radiation treatment planning, electron paramagnetic resonance imaging and spectroscopy, human genetic linkage analysis, cDNA microarray data 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 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; incorporating real-time data visualization into the user environment, monitoring system performance; and proving that a parallel algorithm on a given machine meets its specifications