Our results reveal the possibility of utilizing hardware with limited FP32/FP64 resources and fast low-precision processing units (such as AI-oriented processors) for general-purpose workloads.', For example, when the matrices were initialized with random numbers over a dynamic range of 1E+9, our DGEMM-equivalent implementation achieved up to approximately 980 GFlops of FP64 operation on the Titan RTX GPU (with 130 TFlops on Tensor Cores), although cublasDgemm can achieve only 539 GFlops on FP64 floating-point units. The achievable performance of the method depends on the absolute-value range of each element of the input matrices. The proposed method has three prominent advantages: first, it can be built upon the cublasGemmEx routine using Tensor Core operations second, it can achieve higher accuracy than standard DGEMM, including the correctly-rounded result third, it ensures bit-level reproducibility even for different numbers of cores and threads. The proposed method adopts the Ozaki scheme, an accurate matrix multiplication algorithm based on error-free transformation for matrix multiplication. Tensor Cores are special processing units that perform 4×4 matrix multiplications on FP16 inputs with FP32 precision, and return the result on FP32.

Our results reveal the possibility of utilizing hardware with limited FP32/FP64 resources and fast low-precision processing units (such as AI-oriented processors) for general-purpose workloads.Ībstract = 'This paper proposes a method for implementing dense matrix multiplication on FP64 (DGEMM) and FP32 (SGEMM) using Tensor Cores on NVIDIAs graphics processing units (GPUs). This paper proposes a method for implementing dense matrix multiplication on FP64 (DGEMM) and FP32 (SGEMM) using Tensor Cores on NVIDIA’s graphics processing units (GPUs).

Fp64 titan