#WMMA 5 CHARACTERS FULL#
Figure 2: Volta GV100 Tensor Core operation.ĭuring program execution, multiple Tensor Cores are used concurrently by a full warp of execution. The FP16 multiply results in a full-precision result that is accumulated in FP32 operations with the other products in a given dot product for a 4x4x4 matrix multiply, as Figure 8 shows. Tensor Cores operate on FP16 input data with FP32 accumulation. This is a dramatic 8X increase in throughput for deep learning applications per SM compared to Pascal GP100 using standard FP32 operations, resulting in a total 12X increase in throughput for the Volta V100 GPU compared to the Pascal P100 GPU. The matrix multiply inputs A and B are FP16 matrices, while the accumulation matrices C and D may be FP16 or FP32 matrices.įigure 1: Tensor Core 4x4x4 matrix multiply and accumulate.Įach Tensor Core performs 64 floating point FMA mixed-precision operations per clock (FP16 input multiply with full-precision product and FP32 accumulate, as Figure 2 shows) and 8 Tensor Cores in an SM perform a total of 1024 floating point operations per clock. Clock gating is used extensively to maximize power savings.Įach Tensor Core provides a 4x4x4 matrix processing array which performs the operation D = A * B + C, where A, B, C and D are 4×4 matrices as Figure 1 shows. The Tesla V100 GPU contains 640 Tensor Cores: 8 per SM. Tensor Cores and their associated data paths are custom-crafted to dramatically increase floating-point compute throughput at only modest area and power costs. Tesla V100’s Tensor Cores are programmable matrix-multiply-and-accumulate units that can deliver up to 125 Tensor TFLOPS for training and inference applications.
#WMMA 5 CHARACTERS HOW TO#
In this blog post we show you how you to use Tensor Cores in your own application using CUDA Libraries as well as how to program them directly in CUDA C++ device code. For more information about enabling Tensor Cores when using these frameworks, check out the Mixed-Precision Training Guide. For Deep Learning inference the recent TensorRT 3 release also supports Tensor Cores. Tensor Cores are already supported for Deep Learning training either in a main release or via pull requests in many Deep Learning frameworks (including Tensorflow, PyTorch, MXNet, and Caffe2). Tensor Cores enable AI programmers to use mixed-precision to achieve higher throughput without sacrificing accuracy. Tensor cores are programmable using NVIDIA libraries and directly in CUDA C++ code.Ī defining feature of the new Volta GPU Architecture is its Tensor Cores, which give the Tesla V100 accelerator a peak throughput 12 times the 32-bit floating point throughput of the previous-generation Tesla P100. Tensor cores provide a huge boost to convolutions and matrix operations.