I bought a 1080ti and tried switching to FP16 from FP32 for a training session and was like "wait what?" since it was much slower. There is another issue related: the bottleneck from Nvidia on their consumer cards for FP16. Then I started to search a bit about this, and I stumbled across different papers/articles stating that FP16 actually could give similar results than FP32, but faster. My first question is: why FP32? Is there actually any reason at all? I understand there must be a tradeoff between the high-cost but very precise FP64 and fast but less precise (?) FP16. I'll just paste the comment I made in the linked thread:īasically when I got into DL I started by using tensorflow/keras, and when I dug a bit deeper I saw that all variables were stored in FP32 by default. Or use mixed precision models where you have FP32 as input and then reduce precision in later layers.I'm actually starting a thread to start a discussion about FP16 vs FP32 vs FP64 or other floating point precisions (from here) The best precision for 5989.12345 will most likely be 5988.0 (played with bits on ) If this precision and magnitude is not enough for you then you could scale your data before training to fit FP16 and then train with double the speed. The maximum value for FP16 is 65504 and the minimum is 5.96 × 10−8.
Fp32 vs fp64 software#
Thats all really theoretical anyway, since real software wont run anywhere near the limit for floating point calculations of a CPU. The floating-point units consume a large part of total area, and power consumption, and hence architectural choices are important to evaluate when. But most CPUs these days are very similar in that regard, with none being particularly well-suited for either type of calculation. I compiled on a single table the values I found from various articles and reviews over the web. That means worst-case is 2:1 FLOPS for FP32 vs. This post briefly introduces the variety of precisions and Tensor Core capabilities that the NVIDIA Ampere GPU architecture offers for AI training. Operations using FP64 or one of the 16-bit formats are not affected and continue to use those corresponding types. If you cannot then you do not get any additional benefits from FP16 compatible cards. AMD Radeon and NVIDIA GeForce FP32/FP64 GFLOPS Table 5 JeGX Here is the GFLOPS comparative table of recent AMD Radeon and NVIDIA GeForce GPUs in FP32 (single precision floating point) and FP64 (double precision floating point). The variable affects only the mode of FP32 operations. So in the end you need to understand whether you could rewrite your neural network to use FP16 fully or partially. You could also theoretically use FP32 weights and convert some of them to FP16 weights, but the accuracy could fall. in high precision (e.g., FP64) and some in low precision (e.g., FP32, or lower). But you see in the last link that the speed is faster for mixed precision, but is not 2 times faster as when you use pure FP16. applications use double-precision floating-point arithmetic (FP64). Case in point is the matter of FP64 performance. Some choose to use mixed precision models in order to be fast and accurate ( ). FP16 have lower accuracy by design, because they have much less bits to represent the same number. Your neural network needs to be written using FP16 and it should also have the same accuracy. Taking into account that newer cards that support FP16 (like NVidia 2080 series) are also about 20% faster for FP32 compared to their predecessor (1080) you get an increase of 140% to train FP16 neural networks compared to FP32 on previous cards.īut there is a caveat. If you look at some benchmarks ( ) you will see that GPUs that support FP16 are almost twice as fast calculating FP16 than FP32. Nowadays a lot of GPUs have native support of FP16 to speed up the calculation of neural networks. GPUs originally focused on FP32 because these are the calculations needed for 3D games. More transistors more production failure probability so a 1024 FP32 GPU could be more probably produced than a 512 FP64flexible GPU. FP32 and FP16 mean 32-bit floating point and 16-bit floating point. ALso making 2xFP32 out of a FP64 would need more transistors than pure FP64, more heat, more latency maybe.
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