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Many computational applications use GEMMs: signal processing, fluid dynamics, and many, many others. cuBLAS uses Tensor Cores to speed up GEMM computations (GEMM is the BLAS term for a matrix-matrix multiplication) cuDNN uses Tensor Cores to speed up both convolutions and recurrent neural networks (RNNs). Two CUDA libraries that use Tensor Cores are cuBLAS and cuDNN.
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These C++ interfaces provide specialized matrix load, matrix multiply and accumulate, and matrix store operations to efficiently utilize Tensor Cores in CUDA C++ programs.īut before we get into the details of low-level programming of Tensor Cores, let’s look at how to access their performance via CUDA libraries. CUDA exposes these operations as warp-level matrix operations in the CUDA C++ WMMA API. The threads within a warp provide a larger 16x16x16 matrix operation to be processed by the Tensor Cores.
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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. 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.
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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.