The new GeForce RTX series cards perform well in GPU based rendering, as individual cards, and have great potential for the future thanks to their new RT cores. However, when stacking them together to measure multi-GPU scaling we ran into some serious problems.
PhotoScan makes use of the video cards in a computer to assist with the computation of certain steps. As such, the model of video card used can have an impact on the amount of time those steps take. In this article, we take a look at AMD’s Radeon line – including the Vega 64 and 56 – and see how they stack up to NVIDIA’s GeForce 1000-series.
GPU rendering engines like OctaneRender and Redshift utilize the computational power of the graphics processing chips on video cards to create photo-realistic images and animations. The more powerful the video card, the faster the rendering process goes – and multiple video cards can be used together to further improve performance. But can those video cards be a mix of different models, or do they all need to be identical?
Redshift is a GPU-based rendering engine, which recently updated from version 2.5.72 to 2.6.11. That update added compatibility with NVIDIA’s Volta GPU architecture, and cards like the Titan V, but did it also improve render speeds?
Redshift is a GPU-based rendering engine, and the latest version 2.6.11 introduced compatibility with NVIDIA’s Volta graphics architecture and cards like the Titan V. Lets take a look at how different GeForce and Titan models perform.
Redshift is a GPU-based rendering engine, but how well does its performance scale when multiple video cards are used in a system?
Now that OctaneRender has been updated to support the Volta GPU architecture, how well does its performance scale when using multiple Titan Vs? And how does that compare to other popular rendering cards like the GeForce GTX 1080 Ti?
As of version 3.08, the Volta GPU architecture is now supported in OctaneRender. How does it stack up compared to other Titan and GeForce series graphics cards – in terms of both performance and value?
PhotoScan makes heavy use of both the central processors (CPUs) in a computer and the video cards (GPUs) to run many of the calculations involved in turning still images into a 3D model or map. Intel’s new Xeon Scalable processors offer configurations with dozens of CPU cores, as well as the ability to support multiple GPUs – so let’s see how they perform in PhotoScan.
PhotoScan makes use of both the CPU and GPUs (video cards) in a computer, during different steps of the photogrammetry workflow. One of the configuration options within this program also allows the CPU to be utilized during steps that are primarily performed on the GPU – and it is enabled by default. However, we have found in our testing that this option usually hampers performance more than it helps!
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