IN THIS ARTICLE
PIX4Dmapper can fully utilize a single CPU per instance of the application. On systems with more than one CPU, a second project can be processed at the same to take advantage of the second CPU. This could be particularly useful with large datasets that require merging. In this scenario, two subprojects could be processed through step 1 simultaneously to save time.
Both CPU speed and core count are important factors that can impact significantly on total processing time. Refer to Recommended hardware for more information about which CPUs perform well with PIX4Dmapper.
The CPU has the greatest impact on reducing overall processing time for large and small projects. Check the Hardware and Pix4D article for more information on selecting computer components for processing with PIX4Dmapper
PIX4Dmapper does not utilize multiple GPUs for processing. There is a marginal benefit to having dual GPU on a system designed for PIX4Dmapper. Any benefit is due to the fact that the work of driving displays can be offset to the second GPU. This has minimal impact on overall processing time and the cost of the additional GPU would be better spent on purchasing a better CPU.
GPU processing requires CUDA cores which is only available on NVIDIA cards.
Refer to our hardware support articles for estimates of how much RAM is recommended for various dataset sizes. More RAM can enable you to process larger datasets but can also help improve processing speeds in some cases. The determining factor of how much RAM is required is the number of images, image resolution, processing options, and budget.
For more information check the master hardware article HERE