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TensorFloat-32 or TF32 is a numeric floating point format designed for Tensor Core running on certain Nvidia GPUs. Format. The binary format is: 1 sign bit; 8 exponent bits; 10 fraction bits (also called mantissa, or precision bits)
TF32 strikes a balance that delivers performance with range and accuracy. TF32 uses the same 10-bit mantissa as the half-precision (FP16) math, shown to have more than sufficient margin for the precision requirements of AI workloads. And TF32 adopts the same 8-bit exponent as FP32 so it can support the same numeric range.
TF32 is the default mode for AI on A100 when using the NVIDIA optimized deep learning framework containers for TensorFlow, PyTorch, and MXNet, starting with the 20.06 versions available at NGC. TF32 is also enabled by default for A100 in framework repositories starting with PyTorch 1.7, TensorFlow 2.4, as well as nightly builds for MXNet 1.8 ...
Enable or disable the use of TensorFloat-32 on supported hardware.
TF32 is also supported in CuBLAS (basic linear algebra) and CuTensor (tensor primitives). For HPC applications, CuSolver, a GPU-accelerated linear solver, can take advantage of TF32. Linear solvers use algorithms with repetitive matrix-math calculations and are found in a wide range of fields such as earth science, fluid dynamics, healthcare ...
156 dense TFLOPS for TF32, with Tensor Cores 312 dense TFLOPS for FP16, with Tensor Cores Data and instructions are accessed from DRAM through the shared L2 cache A100: 1.555 TB/s from DRAM L2 cache is faster, but space is limited
NVIDIA Ampere GPU architecture introduced the third generation of Tensor Cores, with the new TensorFloat32 (TF32) mode for accelerating FP32 convolutions and matrix multiplications. TF32 mode is the default option for AI training with 32-bit variables on Ampere GPU architecture.
NVIDIA Ampere, an upcoming generation of NVIDIA GPUs announced at GTC 2020, introduces a new numeric format called TensorFloat-32, or TF32 for short.TF32 has the range of float32/bfloat16 (i.e. 8 bits of exponent) and the precision of fp16 (i.e. 10 bits of mantissa). It is not an in-memory format, but tensor cores natively support it as a computation format.
The NVIDIA A100, based on the NVIDIA Ampere GPU architecture, offers a suite of exciting new features: third-generation Tensor Cores, Multi-Instance GPU and third-generation NVLink.. Ampere Tensor Cores introduce a novel math mode dedicated for AI training: the TensorFloat-32 (TF32). TF32 is designed to accelerate the processing of FP32 data types, commonly used in DL workloads.
TensorFloat-32, or TF32, is the new math mode in NVIDIA A100 GPUs. TF32 uses the same 10-bit mantissa as the half-precision (FP16) math, shown to have more than sufficient margin for the precision requirements of AI workloads. And TF32 adopts the same 8-bit exponent as FP32 so it can support the same numeric range. It is technically a 19-bit ...