Unlocking the Power of NVIDIA Tensor Cores with CUDA 9

Summary: NVIDIA Tensor Cores are specialized units in NVIDIA GPUs designed to accelerate matrix multiplication and accumulation operations, crucial for deep learning and linear algebra. This article explores how to program Tensor Cores using CUDA 9, highlighting their benefits, programming techniques, and practical examples.

Introduction to Tensor Cores

Tensor Cores are a defining feature of the NVIDIA Volta GPU architecture, introduced in the Tesla V100 accelerator. These programmable matrix-multiply-and-accumulate units can deliver up to 125 Tensor TFLOPS for training and inference applications. Each Tensor Core provides a 4x4x4 matrix processing array, significantly increasing floating-point compute throughput with modest area and power costs.

Programming Tensor Cores in CUDA 9

CUDA 9 provides a preview API for programming Tensor Cores, enabling developers to leverage these powerful units in their applications. The nvcuda::wmma namespace exposes functions and types for loading and initializing values into the special format required by Tensor Cores, performing matrix multiply-accumulate (MMA) steps, and storing values back out to memory.

Accessing Tensor Cores in Kernels

Tensor Cores can be accessed in kernels through CUDA 9.0 as a preview feature. The data structures, APIs, and code described in this section are subject to change in future CUDA releases.

Example: Matrix Multiplication on Tensor Cores

To illustrate how to program Tensor Cores, let’s consider a matrix multiplication example. Tensor Cores are restricted to certain matrix dimensions, so we’ll use 16x16 matrices where the input is half precision and the output is single precision.

#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <nvcuda/wmma.h>

__global__ void matrixMultiply(half *a, half *b, float *c) {
    // Define the matrix dimensions
    int M = 16;
    int N = 16;
    int K = 16;

    // Initialize the fragments
    wmma::fragment<wmma::matrix_a, M, N, K, half, wmma::row_major> a_frag;
    wmma::fragment<wmma::matrix_b, M, N, K, half, wmma::col_major> b_frag;
    wmma::fragment<wmma::accumulator, M, N, K, float> c_frag;

    // Load the matrices into the fragments
    wmma::load_matrix_sync(a_frag, a, M);
    wmma::load_matrix_sync(b_frag, b, N);

    // Perform the matrix multiplication
    wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);

    // Store the result
    wmma::store_matrix_sync(c, c_frag, M, wmma::mem_row_major);
}

Benefits of Using Tensor Cores

Tensor Cores provide a significant boost to convolutions and matrix operations, making them ideal for deep learning and linear algebra applications. By leveraging Tensor Cores, developers can achieve higher throughput without sacrificing accuracy.

Supported Frameworks and Libraries

Tensor Cores are supported in many deep learning frameworks, including TensorFlow, PyTorch, MXNet, and Caffe2. The NVIDIA TensorRT 3 release also supports Tensor Cores for deep learning inference.

Practical Considerations

When working with Tensor Cores, it’s essential to consider the matrix dimensions and data types. Tensor Cores are restricted to certain matrix dimensions, and the input and output data types must match the requirements of the Tensor Cores.

Example: 32x32 Matrix Multiplication

To perform a 32x32 matrix multiplication using Tensor Cores, we can divide the large matrix into 4 tiles, each 16x16. We can then load the 16x16 tiles of the input matrices into the fragments, perform the matrix multiplication, and store the result.

// Define the matrix dimensions
int M = 32;
int N = 32;
int K = 32;

// Initialize the fragments
wmma::fragment<wmma::matrix_a, 16, 16, 16, half, wmma::row_major> a_frag;
wmma::fragment<wmma::matrix_b, 16, 16, 16, half, wmma::col_major> b_frag;
wmma::fragment<wmma::accumulator, 16, 16, 16, float> c_frag;

// Loop over the tiles
for (int i = 0; i < 4; i++) {
    // Load the matrices into the fragments
    wmma::load_matrix_sync(a_frag, a + i * 16, 16);
    wmma::load_matrix_sync(b_frag, b + i * 16, 16);

    // Perform the matrix multiplication
    wmma::mma_sync(c_frag, a_frag, b_frag, c_frag);

    // Store the result
    wmma::store_matrix_sync(c + i * 16, c_frag, 16, wmma::mem_row_major);
}

Key Takeaways

  • Tensor Cores are specialized units in NVIDIA GPUs designed to accelerate matrix multiplication and accumulation operations.
  • CUDA 9 provides a preview API for programming Tensor Cores using the nvcuda::wmma namespace.
  • Tensor Cores are restricted to certain matrix dimensions and require specific data types.
  • Developers can achieve higher throughput without sacrificing accuracy by leveraging Tensor Cores.
  • Tensor Cores are supported in many deep learning frameworks and libraries, including TensorFlow, PyTorch, MXNet, and Caffe2.

Conclusion

NVIDIA Tensor Cores are powerful units that can significantly accelerate matrix multiplication and accumulation operations. By leveraging the nvcuda::wmma namespace in CUDA 9, developers can program Tensor Cores to achieve higher throughput without sacrificing accuracy. With support in many deep learning frameworks and libraries, Tensor Cores are an essential tool for deep learning and linear algebra applications.