A WMMA implementation of batched GEMM reaches a performance of 4 GPU, seven and three times the performance in single and half precision Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100 After experimenting with different approaches, we found that NVIDIA Matrix-multiply-and-accumulate on Tensor Cores: the CUDA Warp Matrix MultiplyĪccumulate (WMMA) API, CUTLASS, a templated library based on WMMA, and cuBLAS
Precision loss due to computation in mixed precision.Ĭurrently, NVIDIA provides three different ways of programming In this paper, we investigateĬurrent approaches to program NVIDIA Tensor Cores, their performances and the Performance of 125 Tflops/s in mixed precision. Microarchitecture, provides 640 Tensor Cores with a theoretical peak The NVIDIA Tesla V100 accelerator, featuring the Volta " Tensor Core" that performs one matrix-multiply-and-accumulate on 4x4 matrices The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called
0 kommentar(er)
