## 1 Introduction
The authors also include multiresolution analysis (to take into account distant landscape features that may cause obstruction) via nearest-neighbor scaling. Their usage of CUDA for accelerating their shadow casting is similar to the approach proposed in the present paper. [PERSON] et al. [7] use a similar approach, but their goal is to compute the optimal tilt and orientation of the various points in the elevation model, in order to have solar panels that are not necessarily adherent to a roof, but at an optimal angle. [PERSON] et al. [8] provide an approximation of the irradiance of all points in the elevation model by computing not only the sky view factor (the portion of the sky visible from a given point) but also the sun coverage factor (the ratio between the amount of time that the sun is obstructed and the total duration of sunlight the point would get if there were no surrounding geometry). This approximation allows for computing the sun coverage factor in parallel with the sky view factor at no additional computational expense, trading off some accuracy for speed. On this topic, our work does a more thorough analysis, by computing the direct illumination at each hour on representative days of each month (weighting them per the irradiation at the given time). However, it remains to be seen how much accuracy is gained by not performing the simplifications described in the latter article. [PERSON] et al. [9] have a very in-depth analysis of radiation, and use the GPU to compute final radiation parameters, while their obstruction analysis uses the Hillshade module of the ArcGIS software (ESRI, Redlands, CA, USA). In our tests, with our current setup, the most computationally challenging aspect is precisely shadow casting, so we preferred using the GPU for that rather than for the final irradiation calculation. To summarize, GPU processing is widely used in several approaches, mainly raster-oriented. However, the way the GPU is used in the references mentioned above is generally incomplete and partial. In some cases, key parameters like the Sky View Factor are not processed; in others, simplifications are made to save computing time to the detriment of accuracy. The literature lacks a complete and integrated applicative approach using the GPU that performs the entire process of solar irradiation modeling in an hourly time scale, at a high raster resolution and large regional area. This paper deals with an efficient and integrated computing solution to process solar energy potential in built areas. The proposed solution is implemented on GPU and relies on a shadow-casting algorithm and solar modelling, which leverage digital models at high resolution in large areas. The use of GPU is intended to boost computation time and enable processing wide areas. A new solar potential modelling architecture is proposed for this purpose. The proposed solution consists in a suit of encoded algorithms that process digital elevation maps and some auxiliary inputs, and generates monthly and yearly solar irradiation values for each point in the area. It has been evaluated and applied in the real concrete situation of the Greater Geneva area as a case study. This trans-border region has an area of about 2000 km\({}^{2}\) and contains nearly one million inhabitants, with Geneva as the main city in the middle. A first solar cadaster was developed for the Canton of Geneva only (280 km\({}^{2}\)), as presented in [PERSON] et al. (2010). The goal now is to update and extend this solar cadaster for the whole of Greater Geneva area, with the aim of intensifying solar energy production. The specifics of the project require to produce a solar map at both a regional scale and in high resolution (50 cm) with a reasonable computation time, hence the need to use infrastructures like the GPU. The next sections of the article will be structured as follows: Section 2 presents the previous work and the adopted methodology to calculate irradiance, both from algorithmic and technical standpoints.