When using arivis and looking at the resource monitor, it appears that the software is not using all the CPU, RAM or GPU capabilities. This article aims to explain why this is so.
When using arivis and looking at the resource monitor, it appears that the software is not using all the CPU, RAM or GPU capabilities. This article aims to explain why this is so.
When arivis was started, the software was built on the principle that the file size should never be the issue. The main bottlenecks when it comes to processing large datasets are the CPU processing power, availability of RAM, availability of GPU processing. Each of these affects the ability to process images in different ways. When looking at the Task Manager at the usage of PC resources we may get the impression that arivis is not using all of the computer's resources. This article explains in more details why looking at the Task Manager doesn't tell the whole story and why arivis uses all the resources it needs.
Again this topic is covered in more detail here, but in short, for 3D visualisation we typically use the Graphics Card (GPU), and the GPU can be considered thought of as a computer within the computer.
The GPU has its own computing cores, its own clock rate, its own memory etc. The amount of data that a GPU can process is also limited by these factors. Again there are no consumer GPUs with anything close to 1TB of memory. Typically, GPUs come with 2-20GB of VideoRAM, with some high end server GPUs going up to 128GB of VRAM. However, being able to hold that much data in memory doesn't mean the GPU is capable of rendering these pixels on the screen in 3D in the time needed for an interactive visualisation.
For an interactive visualisation we need to render an image every 100ms as a bare minimum, though most users would consider 10fps laggy and prefer something closer to 30-60fps. At 60fps we only have around 16ms to calculate and display each image, and no GPU can get anywhere near to rendering more than around 2 gigapixels of 3D data within that time. Therefore, arivis will subsample the dataset into the GPU RAM and display the subsampled dataset as fast as possible.
Again, if we look in the Task Manager this will appear as if the software is only using 10-20% of the GPU resources, but using more would usually result in a noticeable drop in performance.