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. CSL is developing algorithms for a number of biomedical applications that can benefit from computational speedup including image processing of electron micrographs, protein and nucleic acid sequence analysis, nuclear magnetic resonance spectroscopy, x-ray crystallography, protein folding prediction, quantum chemical methods, molecular dynamics simulations, human genetic linkage analysis, medical imaging, and radiation treatment planning. Development teams for each application area include computer engineers and scientists from CSL who design and implement the required parallel algorithms and methods, and biomedical scientists who provide the necessary application knowledge and become users of the developed software. The ultimate goal is to have high performance parallel computing facilitate the science that is done at NIH. While developing these computationally demanding applications. CSL 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 from producing portable parallel programs and monitoring system performance, and proving that a parallel algorithm on a given machine meets its specifications.
Dharmaraj, Christopher D; Thadikonda, Kishan; Fletcher, Anthony R et al. (2009) Reconstruction for Time-Domain In Vivo EPR 3D Multigradient Oximetric Imaging-A Parallel Processing Perspective. Int J Biomed Imaging 2009:528639 |
Lau, William W; Johnson, Calvin A; Becker, Kevin G (2007) Rule-based human gene normalization in biomedical text with confidence estimation. Comput Syst Bioinformatics Conf 6:371-9 |
Becker, Kevin G; Barnes, Kathleen C; Bright, Tiffani J et al. (2004) The genetic association database. Nat Genet 36:431-2 |
Joy, Deirdre A; Feng, Xiaorong; Mu, Jianbing et al. (2003) Early origin and recent expansion of Plasmodium falciparum. Science 300:318-21 |