![]() ![]() With shared memory, the GPU can just allocate as much memory as it requires rather than having its own discrete pool that you are limited to. This means I can't take advantage of OptiX rendering on the GPU which is significantly faster. I do a lot of rendering with blender, 3ds Max and V-Ray etc, and I will often have to render more complex scenes on CPU because they exceed the available VRAM on my GPU (8GB)-especially with V-Ray and archviz scenes. At this point, you are at the mercy of whichever component is causing the memory bandwidth bottleneck.Īnother advantage of unified memory is that the memory is shared. ![]() If you then added some more pixel layers, adjustments etc above that vector layer, the compositing data must once again go back to the GPU.Īlthough it may be less frequent in Photo and Designer, you will continuously run into this scenario with Publisher, since page layout design is the very definition of mixed discipline. These vector layers are then going to be processed on the CPU, so the compositing result held in memory must be copied back to the CPU in order to continue compositing. However, let's say that you then add a vector layer above those layers-for example, a rectangle quick shape, piece of text, or poly curve using the Pen Tool. These will compile kernels that then run on the GPU, so all your compositing is done in 'hardware' and you will see the effect of a faster GPU very clearly. Let's say you take an image, then add a few adjustment layers and live filter layers. I can give you a practical example using Affinity Photo. That is why you are seeing such a dramatic drop in the combined score: it is factoring in CPU operations, and the memory transfer is clearly quite a bottleneck for the GPU in this instance. Data has to be copied to and from both devices, which incurs a bandwidth penalty if they are separate rather than unified. For example, you may have operations on the CPU (general compositing, vector calculations), then operations on the GPU (raster processing with pixel content). The combined score benchmark measures a combination of operations that must be performed on CPU and GPU separately. The reason the combined score is so low, however, is exactly why Apple's M1 chips are so beneficial-since they have UMA (unified memory architecture), there is no penalty when copying data between the CPU and GPU. When in Raster (GPU) RX 50 points and RX 6900 XT has shocking ~50000 points. More interesting fact is that even mine RX 580 getting 8600 points in Combined (GPU) and RX 6900 XT has only 10000 points. And Combined (GPU) seems to be just 25-30% lower compared to Raster (GPU). Why RX 6900 XT, being 150% faster than M1 Max GPU in Raster (GPU) test and 250% faster in Geekbench Metal has so low Combined (GPU) score?įor M1 machines the difference between these GPU scores isn't that huge. Almost 50000 points.Īndy, any thoughts on why we're seeing significant drop in Combined (GPU) score on our machines? Almost 500% lower than Raster (GPU). Raster (GPU) is 150% faster on RX 6900 XT than M1 Max 32C GPU. Seems like we have new world record here! Just thought it might be interesting for you to see these numbers. ![]() MacOS Monterey 12.1, Intel Core i9-12900K, AMD Radeon RX 6900 Somerfield Good friend of mine was able to perform these tests on his custom hackintosh build. ![]()
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