Hardware components usage during processing - PIX4Dmatic
This article outlines the hardware components utilized during processing, detailing how CPU, GPU, and RAM resources are allocated for optimal performance. Understanding hardware usage helps in fine-tuning system configurations for better efficiency.
PIX4Dmatic is highly parallelized and takes advantage of multi-core CPUs, as well as MMX/SIMD instructions and NVIDIA GPU Cuda processing. However, the different steps of the processing do not use the resources the same way, and not all parts can take advantage of multi-core or CUDA.
The table below categorizes the usage of each hardware component during the processing steps as High, Medium, or Low.
| Processing step | CPU | RAM | GPU | Storage drive |
| Calibration | High | Medium | Medium | Medium |
| Reoptimization | High | Medium | Low |
Medium |
| Dense point cloud | High | Medium | High | High |
| Depth & fuse | High | Medium | Low | High |
| Image pre-processing | High | Medium | High | Medium |
| Mesh | High | Medium | Low | Medium |
| DSM | High | Medium | Low | High |
| Orthomosaic | High | Medium | High | High |
Disclaimer: The hardware usage during processing is influenced by factors such as project size, image resolution, and the capabilities of the available hardware. For instance, a large project with 2,000 high-resolution images processed on a laptop equipped with an NVIDIA RTX 3050ti GPU and 32GB of RAM will likely push most hardware components to their maximum limits, particularly the GPU and RAM. Smaller projects or lower-resolution images may have less intensive hardware demands, while high-end systems can handle larger workloads more efficiently. It is also highly recommended to use a Solid-State Drive (SSD) for overall faster read and write speeds and efficient processing.