Moving on, AMD is also offering limited native 8-bit support via a pair of specific instructions. In AMD’s own slide deck, they illustrate this, pointing out that using 16-bit functions makes specific rendering steps of 3DMark Serra 20-25% faster, and those are just parts of a whole. So while FP16 execution is twice as fast as FP32 execution on paper specifically for a compute task, the percentage of such calculations in a game will be lower. Though overall it’s important to keep in mind here that even in the best case scenario, only some operations in a game are suitable for FP16.
It will be an uphill battle, but one that can significantly improve AMD’s performance if they can win it, and even more so if NVIDIA chooses not to budge on their position.
So for AMD there’s a real risk of developers not bothering with FP16 support when 70% of all GPUs sold similarly don’t support it.
GP104, by comparison, offers a painful 1/64 th native FP16 rate, making it just useful enough for compatibility/development purposes, but not fast enough for real-world use. NVIDIA also supports native FP16 operations, however unlike AMD, they restrict it to their dedicated compute GPUs (GP100 & GV100). The biggest obstacle for AMD here in the long-term is in fact NVIDIA. Vega will, for the present, live and die in the gaming space primarily based on its FP32 performance. So while there is some long-term potential here for Vega’s fast FP16 math to become relevant for gaming, at the moment it doesn’t do much outside of a couple of benchmarks and some AMD developer relations enhanced software.
Pascal fp64 sucks Pc#
When PC GPUs made the jump to unified shaders in 2006/2007, the decision was made to do everything at FP32 since that’s what vertex shaders typically required to begin with, and it’s only recently that anyone has bothered to look back. While FP16 operations can be used for games (and in fact are somewhat common in the mobile space), in the PC space they are virtually never used. That said, there are whole categories of compute tasks where the high precision isn’t necessary deep learning is the poster child right now, and for Vega Instinct AMD is practically banking on it.Īs for gaming, the situation is more complex still. Which is why fast FP64-capable GPUs are a whole market unto themselves. In the compute world especially, precisions are picked for a reason, and compute users can be quite fussy on the matter. The reason why that matters is two-fold: virtually no existing programs use FP16s, and not everything that is FP32 is suitable for FP16.
Pascal fp64 sucks code#
It requires API support and it requires compiler support, but above all it requires code that explicitly asks for FP16 data types. Taking advantage of this feature, in turn, requires several things. In this respect fast FP16 math is another step in GPU designs becoming increasingly min-maxed the ceiling for GPU performance is power consumption, so the more energy efficient a GPU can be, the more performant it can be. Processing data at a higher precision than is necessary unnecessarily burns power, as the extra work required for the increased precision accomplishes nothing of value. The purpose of integrating fast FP16 and INT16 math is all about power efficiency. This is an extension of AMD’s FP16 support in GCN 3 & GCN 4, where the company supported FP16 data types for the memory/register space savings, but FP16 operations themselves were processed no faster than FP32 operations. If a pair of instructions are compatible – and by compatible, vendors usually mean instruction-type identical – then those instructions can be packed together on a single FP32 ALU, increasing the number of lower-precision operations that can be performed in a single clock cycle. This is similar to what NVIDIA has done with their high-end Pascal GP100 GPU (and Tegra X1 SoC), which allows for potentially massive improvements in FP16 throughput. Which is AMD’s name for packing two FP16 operations inside of a single FP32 operation in a vec2 style. Rapid Packed Math: Fast FP16 Comes to Consumer Cards (& INT16 Too!)Īrguably AMD’s marquee feature from a compute standpoint for Vega is Rapid Packed Math.
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