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UID:submissions.pasc-conference.org_PASC22_sess181_pos151@linklings.com
SUMMARY:P32 - Performance Analysis of Nonlinear Optimization Problems from
  Radiation Therapy Treatment Planning on HPC Systems
DESCRIPTION:Poster\n\nP32 - Performance Analysis of Nonlinear Optimization
  Problems from Radiation Therapy Treatment Planning on HPC Systems\n\nLiu,
  Fredriksson, Markidis\n\nNonlinear optimization is widely used in the pla
 nning process for modern radiation therapy. The goal is to determine contr
 ol parameters for the treatment machine in order to conform the dose deliv
 ered to the patient to the tumour volume as well as possible. Treatment pl
 anning is a time consuming process, which in part is due to the optimizati
 on problem being computationally intensive. As such the performance of the
  optimization solver is crucial which makes it natural to consider how HPC
  resources, such as GPU accelerators or computational clusters could be us
 ed to accelerate the process. Many software libraries, e.g. IPOPT and PETS
 c/TAO, exist for solving nonlinear optimization problems, with varying sup
 port for accelerators and distributed computing. In this work, we analyze 
 the performance of different optimization codes on nonlinear optimization 
 problems from radiation therapy. We present performance analysis of the di
 fferent computational kernels involved in order to better understand compu
 tational bottlenecks in different problems, as well as to understand how d
 ifferent optimizers can utilize HPC hardware today. These results can help
  guide future research efforts on HPC codes for nonlinear optimization tai
 lored for radiation therapy problems.
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