Computers in Science and Engineering

Copublished with IEEE, CiSE features the latest computational science and engineering research in an accessible format along with departments covering news and analysis, CSE in education, and emerging technologies.

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The gap between large-scale data production rate and the rate of generation of data-driven scientific insights has led to an analytical bottleneck in scientific domains like climate, biology, and so on. This is primarily due to the lack of innovative analytical tools that can help scientists efficiently analyze and explore alternative hypotheses about the data and communicate their findings effectively to a broad audience. In this article, by reflecting on a set of successful collaborative research efforts between with a group of climate scientists and visualizationresearchers, the authors introspect how interactive visualization can help reduce the analytical bottleneck for domain scientists.

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A research team based at Virginia Tech University leveraged computing resources at the US Department of Energy's (DOE's) Oak Ridge National Laboratory to explore subsurface multiphase flow phenomena that can't be experimentally observed. Using the Cray XK7 Titan supercomputer at the Oak Ridge Leadership Computing Facility, the team took Micro-CT images of subsurface geologic systems and created two-phase flow simulations. The team's model development has implications for computational research pertaining to carbon sequestration, oil recovery, and contaminant transport.

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Database management systems have become an indispensable tool for industry, government, and academia, and form a significant component of modern datacenters. They can be used in a multitude of scenarios, including online analytical processing, data mining, e-commerce, and scientific analysis. Given the exponential growth in new data produced each year, there is a pressure on software and hardware developers to create datacenters that can cope with increasing requirements. The authors look at the organization of a modern relational database management system and propose optimizations and redesign for the storage access, memory, and CPU.

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Progress in science and engineering is increasingly tied to the effective use of computational modeling on high-performance computing systems. Expertise in parallelcomputing techniques is therefore required, given the current architecture of those systems. Many campuses don't offer such a course because of course load limits, a lack of faculty expertise, and/or lack of access to appropriate computing resources. As part of the XSEDE project, an initial MOOC-style course on parallelcomputing proved to be ineffective at resolving this problem. A subsequent blended online course with collaborating faculty from multiple institutions where students receive academic credit has proven to be a much more effective approach to creating a STEM workforce with the requisite parallelcomputing knowledge.