On Energy Efficiency of Big Data Computed Tomography (CT) CPU- and GPU-based Algorithms
Date/Time: September 30, 2:30pm
Location: 223 Atanasoff
This talk 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. This work found that irregular GPU-based approaches 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 improvements were realized on the GPU-based reconstructions by modularizing storage I/O operations.
Dr. Edward Jimenez obtained his Ph.D. in Applied Mathematics at the University of Arizona. He has worked at Sandia National Laboratories in Albuquerque for 5 years and is currently a Principal Member in Computer Science R&D in the Software Systems R&D Group where he conducts research in Mathematics, High-Performance Computing, Threat Detection in Radiography and Computed Tomography as well as Computed Tomography Reconstruction algorithms. He has won many awards including being named the 2014 Most Promising Engineer or Scientist with Advanced Degree by HENAAC.
Sandia Laboratories will provide refreshments.
The talk will follow with a short information session about Sandia and Software Systems R&D for interested individuals.