A Fine-Grained CUDA Implementation of the Multi-objective Evolutionary Approach NSGA-II: Potential Impact for Computational and Systems Biology Applications

D’Agostino, Daniele; Pasquale, Giulia; Merelli, Ivan

doi:10.1007/978-3-319-24462-4_23

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GPUs are widely used for the purpose of high-performance computing, both within the field of medical physics, 15 for example, for dose calculation, [16][17][18][19] and in the field of evolutionary computation. [20][21][22][23] Because the design of GPUs is particularly suited for specific forms of large-scale parallel computation, they are very suitable for high-performance dose calculation, as the dose in all DC points can be calculated in parallel.…”

Section: F Gpu-accelerated Bi-objective Treatment Planningmentioning

confidence: 99%

GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy

et al. 2019

View full text Add to dashboard Cite

Purpose The purpose of this study is to improve upon a recently introduced bi‐objective treatment planning method for prostate high‐dose‐rate (HDR) brachytherapy (BT), both in terms of resulting plan quality and runtime requirements, to the extent that its execution time is clinically acceptable. Methods Bi‐objective treatment planning is done using a state‐of‐the‐art multiobjective evolutionary algorithm, which produces a large number of potential treatment plans with different trade‐offs between coverage of the target volumes and sparing organs at risk. A graphics processing unit (GPU) is used for large‐scale parallelization of dose calculations and the calculation of the dose‐volume (DV) indices of potential treatment plans. Moreover, the objectives of the previously used bi‐objective optimization model are modified to produce better results. Results We applied the GPU‐accelerated bi‐objective treatment planning method to a set of 18 patients, resulting in a set containing a few hundred potential treatment plans with different trade‐offs for each of these patients. Due to accelerations introduced in this article, results previously achieved after 1 hour are now achieved within 30 seconds of optimization. We found plans satisfying the clinical protocol for 15 of 18 patients, whereas this was the case for only 4 of 18 clinical plans. Higher quality treatment plans are obtained when the accuracy of DV index calculation is increased using more dose calculation points, requiring still no more than 3 minutes of optimization for 100 000 points. Conclusions Large sets of high‐quality treatment plans that trade‐off coverage and sparing are now achievable within 30 seconds, due to the GPU‐acceleration of a previously introduced bi‐objective treatment planning method for prostate HDR brachytherapy. Higher quality plans can be achieved when optimizing for 3 minutes, which we still consider to be clinically acceptable. This allows for more insightful treatment plan selection in a clinical setting.

show abstract

Section: F Gpu-accelerated Bi-objective Treatment Planningmentioning

confidence: 99%

GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Modern GPUs are equipped with several thousands of cores, but are restricted by the fact that they require a fine-grained parallelization model to be effective. In the field of evolutionary computation, GPUs are becoming widely used to speed-up various algorithms through the parallelization of their most time-consuming operations [9,16,19,23].…”

Section: Introductionmentioning

confidence: 99%

Large-scale parallelization of partial evaluations in evolutionary algorithms for real-world problems

Bouter

Alderliesten

Bel

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

View full text Add to dashboard Cite

The importance and potential of Gray-Box Optimization (GBO) with evolutionary algorithms is becoming increasingly clear lately, both for benchmark and real-world problems. We consider the GBO setting where partial evaluations are possible, meaning that sub-functions of the evaluation function are known and can be exploited to improve optimization efficiency. In this paper, we show that the efficiency of GBO can be greatly improved through largescale parallelism, exploiting the fact that each evaluation function requires the calculation of a number of independent sub-functions. This is especially interesting for real-world problems where often the majority of the computational effort is spent on the evaluation function. Moreover, we show how the best parallelization technique largely depends on factors including the number of sub-functions and their required computation time, revealing that for different parts of the optimization the best parallelization technique should be selected based on these factors. As an illustration, we show how large-scale parallelization can be applied to optimization of highdose-rate brachytherapy treatment plans for prostate cancer. We find that use of a modern Graphics Processing Unit (GPU) was the most efficient parallelization technique in all realistic scenarios, leading to substantial speed-ups up to a factor of 73.

show abstract

A Fine-Grained CUDA Implementation of the Multi-objective Evolutionary Approach NSGA-II: Potential Impact for Computational and Systems Biology Applications

Cited by 2 publications

References 12 publications

GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy

GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy

Large-scale parallelization of partial evaluations in evolutionary algorithms for real-world problems

Contact Info

Product

Resources

About