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.

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Purpose The purpose of this paper is to illustrate the importance of redesigning, reusing, remanufacturing, recovering, recycling and reducing (6R) to sustainable manufacturing and discuss the general procedure to reconfigure robots. Two critical challenges in adopting industrial robots in small and medium-sized enterprise (SMEs) are flexibility and cost, as the number of tasks of the same type can be limited because of the size of an SME. The challenges can be alleviated by 6R. The 6R processes allow a robot to adopt new tasks, increase its utilization rate and reduce unit costs of products. Design/methodology/approach There is no shortcut to implement sustainable manufacturing. All of the manufacturing resources in a system should be planned optimally to reduce waste and maximize the utilization rates of resources. In this paper, modularization and reconfiguration are emphasized to implement 6R processes in sustainable manufacturing; robots are especially taken into consideration as core functional modules in the system. Modular architecture makes it feasible to integrate robots with low-cost customized modules for various tasks for the high utilization rates. A case study is provided to show the feasibility. Findings Finding the ways to reuse manufacturing resources could bring significant competitiveness to an SME, in the sense that sophisticated machines and tools, such as robots, can be highly utilized even in a manufacturing environment with low or medium product volumes. The concepts of modularization and 6R processes can be synergized to achieve this goal. Research limitations/implications The authors propose the strategy to enhance the utilization rates of core manufacturing resources using modular architecture and 6R practice. The axiomatic design theory can be applied as the theoretical fundamental to guide the 6R processes; however, a universal solution in the implementation is not available. The solutions have to be tailored to specific SMEs, and the solutions should vary with respect to time. Practical implications To operate a sustainable manufacturing system, a continuous design effort is required to reconfigure existing resources and enhance their capabilities to fulfill new tasks in the dynamic environment. Social implications The authors focus on the importance of sustainable manufacturing to modern society, and they achieve this goal by reusing robots as system components in different applications. Originality/value Sustainable manufacturing has attracted a great deal of attention, although the operable guidance for system implementation is scarce. The presented work has thrown some light in this research area. The 6R concept has been introduced in a modular system to maximize the utilizations of critical manufacturing resources. It is particularly advantageous for SMEs to adopt sophisticated robots cost-effectively.

High Throughput Computing (HTC) environments strive "to provide large amounts of processing capacity to customers over long periods of time by exploiting existing resources on the network" according to Basney and Livny [1]. A single Blue Gene/L rack can provide thousands of CPU resources into HTC environments. This paper discusses the implementation of an asynchronous task dispatch system that exploits a recently released feature of the Blue Gene/L control system -called HTC mode -and presents data on experimental runs consisting of the asynchronous submission of multiple batches of thousands of tasks for financial workloads. The methodology developed here demonstrates how systems with very large processor counts and light-weight kernels can be configured to deliver capacity computing at the individual processor level in future petascale computing systems.