The Journal of Supercomputing ( IF 2.157 ) Pub Date : 2020-03-24 , DOI: 10.1007/s11227-020-03254-6
J. J. Moreno, J. Miroforidis, E. Filatovas, I. Kaliszewski, E. M. Garzón

Abstract In radiotherapy planning, which involves optimization, efforts to produce better (more effective and at the same time with lesser adverse effects) patient radiation plans must trade-off with higher computing times needed to achieve this goal. Computing times is the key (but not only) factor in radiotherapy planning, which is always performed in clinical workflows regimes. ‘Win–win’ is when better plans can be produced within a non-increasing time budget. This work reports on the authors’ attempt to put radiotherapy planning in a ‘win–win’ situation. We looked into unexploited till now reserves, which lie in the performance of optimization methods and algorithms, namely the reserves in performing linear algebra computations when such methods are applied to radiotherapy. A specific feature of such applications is the necessity of numerous sparse matrix $$\times$$ vector computations, with matrices and vectors of large sizes. Our first step was to propose ways to include sparse matrix procedures from existing libraries into an optimization algorithm for testing experiments. Our second step in our quest for reserves was to resort to High Performance Computing. We chose graphical processing units because of their versatility, low cost, accessibility, and the existence of linear algebra libraries dedicated to these platforms. We report on the reserves identified in this way, i.e., on speedups of optimization computations achievable by such an optimization algorithm hybridization. We tested our hybrid algorithm numerically on a clinical case.

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