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The TensorFloat-32 (TF32) precision format in the NVIDIA Ampere architecture speeds single-precision training and some HPC apps up to 20x.
TensorFloat-32 (TF32) is a numeric floating point format designed for Tensor Core running on certain Nvidia GPUs.
TF32 mode is the default option for AI training with 32-bit variables on Ampere GPU architecture. It brings Tensor Core acceleration to single-precision DL workloads, without needing any changes to model scripts.
TF32采用与FP32相同的8位指数，因此可以支持相同的数值范围。 这种组合使TF32成为FP32的绝佳替代品，用于处理单精度数学，特别是深度学习和许多HPC应用程序核心的大量乘法累加函数。
The NVIDIA A100 brought the biggest single-generation performance gains ever in our company's history. These speedups are a product of architectural innovations that include Multi-Instance GPU (MIG), support for accelerated structural sparsity, and a new precision called TF32, which is the focus of this post. TF32 is a great precision to use for deep learning training, as it combines the ...
TF32 is a 32-bit floating-point data type introduced by NVIDIA in their Tensor Cores. It is designed to provide high-performance matrix operations and is used in NVIDIA's mixed precision training.
It accommodates Int8, FP8, FP16, BF16, FP32 and TF32, providing exceptionally efficient training performance in data centres. Gaudi3's architecture is designed for low-latency AI operations and is highly effective in the large-scale training of neural networks.
TensorFloat32 (TF32) has recently become popular as a drop-in replacement for these FP32 based models. However, there is a pressing need to provide additional performance gains for these models by using faster datatypes (such as BFloat16 (BF16)) without requiring additional code changes.
tf.config.experimental.enable_tensor_float_32_execution( enabled ) TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere GPUs and above. TensorFloat-32 execution causes certain float32 ops, such as matrix multiplications and convolutions, to run much faster on such GPUs but with reduced precision. This reduced precision should not impact convergence of deep learning models in ...
Accuracy Considerations # Reduced Precision Formats # The choice of floating-point precision can significantly impact both performance and accuracy. In addition to a standard single-precision floating-point (FP32), TensorRT supports three reduced precision formats: TensorFloat-32 (TF32), half-precision floating-point (FP16), and Brain Floating Point (BF16). TF32, enabled by default in TensorRT ...