Main memory in clusters may dominate total system power. The resulting energy consumption increases system operating cost and the heat produced reduces reliability. Emergent memory technology will provide servers with the ability to dynamically turn-on (online) and turn-off (offline) memory devices at runtime. This technology, coupled with slack in memory… (More)
Main memory in many tera-scale systems requires tens of kilowatts of power. The resulting energy consumption increases system cost and the heat produced reduces reliability. Emergent memory technologies will provide systems the ability to dynamically turn-on (online) and turn-off (offline) memory devices at runtime. This technology, coupled with slack in… (More)
Multiple innovations will be required to navigate the challenging road to developing exascale systems. The stage is set for a global race to design and build the first exascale system within a 20-megawatt (MW) power envelope. Perhaps not since the race to the moon have we seen a worldwide competition of this scale and complexity.
Rough set theory is a popular and powerful machine learning tool. It is especially suitable for dealing with information systems that exhibit inconsistencies, i.e. objects that have the same values for the conditional attributes but a different value for the decision attribute. In line with the emerging granular computing paradigm, rough set theory groups… (More)
Driven by the need to improve efficiency, modern buildings are instrumented with numerous sensors to monitor utilization and regulate environmental conditions. While these sensor systems serve as valuable tools for managing the comfort and health of occupants, there is an increasing need to expand the deployment of sensors to provide additional insights.… (More)
Size and complexity of Big Data requires advances in machine learning algorithms to adequately learn from such data. While distributed shared-nothing architectures (Hadoop/Spark) are becoming increasingly popular to develop such new algorithms, it is quite challenging to adapt existing machine learning algorithms. In this paper, we propose a solution for… (More)