Shodor

As a Blue Waters Student Internship Program project, we have developed a model of interplanetary low-thrust trajectories from Earth to Mars for spacecrafts supplying necessary cargo for future human-crewed missions. Since these cargo missions use ionic propulsion that causes a gradual change in the spacecraft's velocity, the modeling is more computationally expensive than conventional trajectories assuming instantaneous spacecraft velocity changes. This model calculates the spacecraft's time of flight and swept angle at different payload masses with other parameters kept constant and correlates them with known locations of the planets. With parallelization using OpenMP on Blue Waters, its runtime has decreased from 10.55 to 1.53 hours. The program takes a user-selected Mars arrival date and outputs a given range of dates with maximum payload capabilities. This parallelized model will greatly reduce the time required for future mission design projects when other factors like spacecraft solar panel power output may vary with new mission specifications. The internship experience has enhanced the intern's ability to manage a project and will impact positively on his future graduate studies or research career.

Bayesian networks may be utilized to infer genetic relations among genes. This has proven useful in providing information about how gene interactions influence life. However, Bayesian network learning is slow due to the nature of the algorithm. K2, a search space reduction, helps speed up the learning process but may introduce bias. To eliminate this bias, multiple Bayesian networks must be computed. This paper evaluates and realizes parallelization of network generation and the reasoning behind the choices made. Methods are developed and tested to evaluate the results of the implemented accelerations. Generating networks across multiple cores results in a linear speed-up with negligible overhead. Distributing the generation of networks across multiple machines also introduces linear speed-up, but results in additional overhead.

Several microbial genome databases provide collections of thousands of genome annotation files in formats suitable for the performance of complex cognitive activities such as decision making, sense making and analytical reasoning. The goal of the research reported in this article was to interactive analytics resources to support the performance of complex cognitive activities on a collection of publicly available genome information spaces. A supercomputing infrastructure (Blue Waters Supercomputer) provided computational tools to construct information spaces while visual analytics software and online bioinformatics resources provided tools to interact with the constructed information spaces. The Rhizobiales order of bacteria that includes the Brucella genus was the use case for preforming the complex cognitive activities. An interesting finding among the genomes of the dolphin pathogen, Brucella ceti, was a cluster of genes with evidence for function in conditions of limited nitrogen availability.

We attempted to find a more sustainable solution for performing virtual screening with AutoDock Vina which uses less electricity than computers using typical x64 CPUs. We tested a cluster of ODROID-XU3 Lite computers with ARM CPUs and compared its performance to a server with x64 CPUs. In order to be a viable solution, our cluster needed to perform the screen without sacrificing speed or increasing hardware costs. The cluster completed the virtual screen in a little less time than our comparison server while using just over half the electricity that the server used. Additionally, the hardware for the cluster cost about 38% less than the server, making it a viable solution.

Computing is ubiquitous and perhaps the most common element of our shared experience. However, many students do not seem to recognize the serious applications and implications of computing to the sciences. Wagner College, like many liberal arts colleges, requires a semester of a Computer Science affiliated course to provide students with an exposure to ``technological skills''. Sadly, such courses typically do not delve into high-level computational skills or computational thinking and generally provide instruction in using Microsoft Office extregistered{} products and rudimentary worldwide web concepts. These courses and approaches were probably valuable a decade ago when computing devices were not quite as prevalent. However, in today's world these courses appear outdated and do not provide relevant skills to the modern undergraduate student. We have created a course called, ``Introduction to Scientific Computing'', to remedy this problem and to provide students with state-of-the-art technological tools. The course provides students with hands-on training on typical work-flows in scientific data analysis and data visualization. Students are trained in the symbolic computing platform, Wolfram Mathematica extregistered{}, to apply functional programming to develop data analysis and problem solving skills. The course presents computational thinking examples in the framework of various scientific disciplines. This exposure helps students to understand the advantages of technical computing and its direct relevance to their educational goals. The students are also trained to perform molecular visualization using open source software packages to understand secondary and tertiary protein structures, construct molecular animations, and to analyze computer simulation data. These experiences stimulate students to apply these skills across multiple courses and their research endeavors. Student self-assessment data suggests that the course satisfies a unique niche in undergraduate education and enriches the training of future STEM graduates.