The original IEEE FP16 was not designed with deep learning applications in mind, its dynamic range is too narrow. The name flows from “Google Brain”, which is an artificial intelligence research group at Google where the idea for this format was conceived. “Half Precision” 16-bit Floating Point ArithmeticĪnother 16-bit format originally developed by Google is called “ Brain Floating Point Format”, or “bfloat16” for short.Half-Precision Floating-Point, Visualized.Right now well-supported on modern GPUs, e.g. Was poorly supported on older gaming GPUs (with 1/64 performance of FP32, see the post on GPUs for more details).Not supported in x86 CPUs (as a distinct type).Supported in TensorFlow (as tf.float16)/ PyTorch (as torch.float16 or torch.half).Otherwise, can be used with special libraries. Currently not in the C/C++ standard (but there is a short float proposal).Other formats in use for post-training quantization are integer INT8 (8-bit integer), INT4 (4 bits) and even INT1 (a binary value). Can be used for post-training quantization for faster inference ( TensorFlow Lite).Can be used for training, typically using mixed-precision training ( TensorFlow/ PyTorch).Additional precision gives nothing, while being slower, takes more memory and reduces speed of communication. There is a trend in DL towards using FP16 instead of FP32 because lower precision calculations seem to be not critical for neural networks.Range: ~5.96e−8 (6.10e−5) … 65504 with 4 significant decimal digits precision. Another IEEE 754 format, the single-precision floating-point with: The format that was the workhorse of deep learning for a long time. Among recent GPUs with unrestricted FP64 support are GP100/102/104 in Tesla P100/P40/P4 and Quadro GP100, GV100 in Tesla V100/Quadro GV100/Titan V and GA100 in recently announced A100 (interestingly, the new Ampere architecture has 3rd generation tensor cores with FP64 support, the A100 Tensor Core now includes new IEEE-compliant FP64 processing that delivers 2.5x the FP64 performance of V100).Most GPUs, especially gaming ones including RTX series, have severely limited FP64 performance (usually 1/32 of FP32 performance instead of 1/2, see the post on GPUs for more details). ![]() Supported in TensorFlow (as tf.float64)/ PyTorch (as torch.float64 or torch.double).On most C/C++ systems represents the double type.The format is used for scientific computations with rather strong precision requirements.It's only the Radeon HD 6900 and HD 5800 series with R600g that have working ARB_gpu_shader_fp64 and thus GL4 support already.Range: ~2.23e-308 … ~1.80e308 with full 15–17 decimal digits precision. This work is particularly beneficial on the Radeon side for discrete GPUs like the Radeon HD 6800 series and others that have a fair amount of performance potential for older games but still only expose OpenGL 3.3 due to lacking FP64. Of course, most (all?) games don't need FP64, but it's a requirement of meeting OpenGL 4.0.Īs of writing, the Radeon R600g driver side changes haven't landed to expose this soft FP64/INT64 support but we'll see if that still happens in time for Mesa 19.0. ![]() On the Intel side, this work is beneficial for Sandy Bridge (and older) while Ivy Bridge tapped OpenGL 4.2 in 2017 and obviously newer generations are up-to-spec. Rounding out the work are the necessary Intel compiler back-end tweaks followed by enabling the FP64 software routines and then finally enabling the FP64 and INT64 OpenGL extensions unconditionally now that it will work everywhere regardless of actual hardware support. This contains all of Tournier's GLSL code plus various NIR changes for lowering these 64-bit data types. Since that GSoC 2016 project, there's been slow work by both the Intel and Radeon driver teams to implement the support and various patches while finally overnight the work was merged in time for next week's Mesa 19.0 feature freeze. Elie Tournier has since gone on to work for Collabora but getting this code merged has taken quite some time. Going back to the summer of 2016 was a Google Summer of Code project by Elie Tournier to implement "soft" FP64 support using GLSL to help out older GPUs that otherwise couldn't expose OpenGL 4.0 due to not supporting ARB_gpu_shader_fp64. The FP64 one is most notable with that being a requirement for OpenGL 4.0 but some older GPUs lacking that capability for bumping past OpenGL 3.3. For those with older graphics processors, rejoice as with the upcoming Mesa 19.0 driver release it might now be possible to have OpenGL 4.0 thanks to software-based implementations of ARB_gpu_shader_int64 and ARB_gpu_shader_fp64 finally being merged to mainline.
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