Abstract-Heart disease is the number one killer in the United States, and finding indicators of the disease at an early stage is critical for treatment and prevention. In this paper we evaluate visualization techniques that enable the diagnosis of coronary artery disease. A key physical quantity of medical interest is endothelial shear stress (ESS). Low ESS has been associated with sites of lesion formation and rapid progression of disease in the coronary arteries. Having effective visualizations of a patient's ESS data is vital for the quick and thorough non-invasive evaluation by a cardiologist. We present a task taxonomy for hemodynamics based on a formative user study with domain experts. Based on the results of this study we developed HemoVis, an interactive visualization application for heart disease diagnosis that uses a novel 2D tree diagram representation of coronary artery trees. We present the results of a formal quantitative user study with domain experts that evaluates the effect of 2D versus 3D artery representations and of color maps on identifying regions of low ESS. We show statistically significant results demonstrating that our 2D visualizations are more accurate and efficient than 3D representations, and that a perceptually appropriate color map leads to fewer diagnostic mistakes than a rainbow color map.

We present the first large-scale simulation of blood flow in the coronary artieries and other vessels supplying blood to the heart muscle, with a realistic description of human arterial geometry at spatial resolutions from centimeters down to 10 microns (near the size of red blood cells). This multiscale simulation resolves the fluid into a billion volume units, embedded in a bounding space of 300 billion voxels, coupled with the concurrent motion of 300 million red blood cells, which interact with one another and with the surrounding fluid. The level of detail is sufficient to describe phenomena of potential physiological and clinical significance, such as the development of atherosclerotic plaques. The simulation achieves excellent scalability on up to 294, 912 Blue Gene/P computational cores.

A patent data base of 6.7 million compounds generated by a very high performance computer (Blue Gene) requires new techniques for exploitation when extensive use of chemical similarity is involved. Such exploitation includes the taxonomic classification of chemical themes, and data mining to assess mutual information between themes and companies. Importantly, we also launch candidates that evolve by "natural selection" as failure of partial match against the patent data base and their ability to bind to the protein target appropriately, by simulation on Blue Gene. An unusual feature of our method is that algorithms and workflows rely on dynamic interaction between match-and-edit instructions, which in practice are regular expressions. Similarity testing by these uses SMILES strings and, less frequently, graph or connectivity representations. Examining how this performs in high throughput, we note that chemical similarity and novelty are human concepts that largely have meaning by utility in specific contexts. For some purposes, mutual information involving chemical themes might be a better concept.

In the life sciences, genomic databases for sequence search have been growing exponentially in size. As a result, faster sequencesearch algorithms to search these databases continue to evolve to cope with algorithmic time complexity. The ubiquitous tool for such search is the Basic Local Alignment Search Tool (BLAST) [1] from the National Center for Biotechnology Information (NCBI). Despite continued algorithmic improvements in BLAST, it cannot keep up with the rate at which the database is exponentially increasing in size. Therefore, parallel implementations such as mpiBLAST have emerged to address this problem. The performance of such implementations depends on a myriad of factors including algorithmic, architectural, and mapping of the algorithm to the architecture. This paper describes modifications and extensions to a parallel and distributed-memory version of BLAST called mpiBLAST-PIO and how it maps to a massively parallel system, specifically IBM Blue Gene/L (BG/L). The extensions include a virtual file manager, a "multiple master" runtime model, efficient fragment distribution, and intelligent load balancing. In this study, we have shown that our optimized mpiBLAST-PIO on BG/L using a query with 28014 sequences and the NR and NT databases scales to 8192 nodes (two cores per node). The cases tested here are well suited for a massively parallel system.

EUDOCe is a molecular docking program that has successfully helped to identify new drug leads. This virtual screening (VS) tool identifies drug candidates by computationally testing the binding of these drugs to biologically important protein targets. This approach can reduce the research time required of biochemists, accelerating the identification of therapeutically useful drugs and helping to transfer discoveries from the laboratory to the patient. Migration of the EUDOC application code to the IBM Blue Gene/Le (BG/L) supercomputer has been highly successful. This migration led to a 200-fold improvement in elapsed time for a representative VS application benchmark. Three focus areas provided benefits. First, we enhanced the performance of serial code through application redesign, hand-tuning, and increased usage of SIMD (single-instruction, multiple-data) floating-point unit operations. Second, we studied computational load-balancing schemes to maximize processor utilization and application scalability for the massively parallel architecture of the BG/L system. Third, we greatly enhanced system I/O interaction design. We also identified and resolved severe performance bottlenecks, allowing for efficient performance on more than 4,000 processors. This paper describes specific improvements in each of the areas of focus.