This paper will investigate energy-efficiency for various real-world industrial computed-tomography reconstruction algorithms, both CPU- and GPU-based implementations. This work shows that the energy required for a given reconstruction is based on performance and problem size. There are many ways to describe performance and energy efficiency, thus this work will investigate multiple metrics including performance-per-watt, energy-delay product, and energy consumption. This work found that irregular GPU-based approaches1 realized tremendous savings in energy consumption when compared to CPU implementations while also significantly improving the performance-per- watt and energy-delay product metrics. Additional energy savings and other metric improvement was realized on the GPU-based reconstructions by improving storage I/O by implementing a parallel MIMD-like modularization of the compute and I/O tasks.
We report laboratory results of a coronagraphic testbed to assess the intensity reduction differences between a "Gaussian" tapered focal plane coronagraphic mask and a classical hard-edged "Top Hat" function mask at Extreme Adaptive Optics (ExAO) Strehl ratios of ~94%. However, unlike a traditional coronagraph design, we insert a reflective focal plane mask at 45o to the optical axis and used a "spot of Arago blocker" (axicon stop) before a final image in order to block additional mask edge-diffracted light. The testbed simulates the optical train of ground-based telescopes (in particular the 8.1m Gemini North telescope) and includes one spider vane and different mask radii (r= 1.9λ/D, 3.7λ/D, 7.4λ/D) and two types of reflective focal plane masks (hard-edged "Top Hat" and "Gaussian" tapered profiles). In order to investigate the performance of these competing coronagraphic designs with regard to extra-solar planet detection sensitivity, we utilize the simulation of realistic extra-solar planet populations (Nielsen et al. 2006). With an appropriate translation of our laboratory results to expected telescope performance, a "Gaussian" tapered mask radius of 3.7λ/D with an axicon stop performs best (highest planet detection sensitivity). For a full survey with this optimal design, the simulation predicts ~30% more planets detected compared to a similar sized "Top Hat" function mask with an axicon stop. Using the best design, the "point contrast ratio" between the stellar PSF peak and the coronagraphic PSF at 10λ/D (0.4" in H band if D = 8.1m) is 1.4 x 106. This is ~10 times higher than a classical Lyot "Top Hat" coronagraph.