Using high-performance, parallel computing and knowledge-based data management systems, biomedical scientists can greatly reduce the time it takes to complete computationally intensive tasks, adopt new approaches for processing experimental data, and mine large, complex datasets to find important data patterns. These benefits may allow for the inclusion of more data into 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 a more realistic model. High-performance parallel computing allows biomedical scientists to analyze and study large datasets that cannot be processed within a practical amount of computer analysis time on conventional sequential or vector processing computing machines. 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. Knowledge-based databases and data mining tools are powerful resources in modern biomedical research. They provide researchers ways to extract and use information from vast amounts of knowledge and data collected from wide-ranging sources such as public and commercial databases (e.g., genome sequences, chromosome, SNP, genetic diseases, EST cluster, gene mapping, gene expression, molecular biology, and literature databases). In addressing these complex data analysis and management challenges, the High Performance Computing and Informatics Office (HPCIO) of the Division of Computational Bioscience (DCB) in the Center for Information Technology (CIT) is developing high performance computing hardware and software infrastructure for a wide range of biomedical applications where computational speed, advanced data analysis, smart data mining and large-scale data management are important. These include gene expression data analysis, image processing of live-cell arrays, medical image and clinical information management, biostatistics, population genetics, and human genetic linkage analysis. The ultimate goal is to provide high performance parallel computing, scientific databases, and data analysis and visualization tools to facilitate the science that is done at the NIH. While developing the computationally demanding applications and scientific database systems, HPCIO is investigating the following challenging issues: (1) partitioning a problem into many parts that can be executed independently on different processors; (2) designing the parts so that the computing load can be distributed evenly or dynamically balanced over the available processors; (3) designing algorithms so that the number of processors is a parameter and the algorithms can be configured dynamically for the available machine; (4) developing tools and environments for producing portable parallel programs; (5) incorporating interactive data analysis and visualization tools into the user environment; (6) monitoring system performance; (7) proving that a parallel algorithm on a given machine meets its specifications; (8) evaluating modern parallel computer architectures for their performance characteristics on biomedical applications; and (9) developing and providing high performance database systems and data mining tools for the archival and analysis of scientific data and medical images via web-accessible interfaces.
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