Optimally designing radiotherapy and radiosurgery treatments to increase the likelihood of a successful recovery from cancer is an important application of operations research. Researchers have been hindered by the lack of academic software that supports head-to-head comparisons of different techniques, and this article addresses the inherent difficulties of designing and implementing an academic treatment planning system. In particular, this article details the algorithms and the software design of Radiotherapy optimAl Design (RAD).
Historically, energy management in computer science has been predominantly treated as an activity of hardware optimization. A great deal of the effort in this area is concentrated on component activation, deactivation, and resources scheduling in order to provide a reduction of total power consumption. This study focuses on the subject of power consumption from the software developer's point of view, using a reliable power measurement framework to validate the programming literature's premise that programming options-such as multiplication operations-are high consumers of power. Besides some elementary operations and authors' suggestions about alternatives for power consumption reduction on the programming stage, two well-known and widely applied algorithms for large number multiplication were compared: Karatsuba and Toom-Cook. The results lead to conclusions that might help a developer to choose, in some cases, between execution speed or power consumption reduction, or establish a maximum power consumption limit for the software execution.
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