基于此编程模型,Triton大量使用了编译器来做各种自动优化,譬如自动进行shared memory管理、自动使用tensor cores等,使得在大大简化编程的同时也能达到和cuBLAS基本持平的性能。下面我们看两个例子来感受下Triton的编程模型和性能。 向量相加 下面的例子以vector addition为例展示了Triton的编程模型: BLOCK=512# This is a...
void hgemm_naive_f16(torch::Tensor a, torch::Tensor b, torch::Tensor c); void hgemm_sliced_k_f16(torch::Tensor a, torch::Tensor b, torch::Tensor c); void hgemm_t_8x8_sliced_k_f16x4(torch::Tensor a, torch::Tensor b, torch::Tensor c); void hgemm_t_8x8_sliced_k_f16x4...
Benchmarking GPU Tensor Cores on General Matrix Multiplication Kernels through CUTLASS necessary fine tuning, hardware-level ASICs like tensor cores could dramatically boost performance in specific operations like GEMM offloading to modern GPUs... X Huang,X Zhang,P Yang,... - Applied Sciences (2076...
For the purposes of illustration in the following animations, we're using a fictitious GPU that has 8 threads per warp and tensor cores that operate on 8x8 chunks of the weight matrix. While simplified, this closely matches the types of layouts used by NVIDIA tensor cores, albeit scaled...
OpClassTensorOp, // tag indicating Tensor Cores cutlass::arch::Sm70 // tag indicating target GPU compute architecture >; Gemm gemm_op; cutlass::Status status; // // Launch GEMM on the device // status = gemm_op({ {m, n, k}, {ptrA, lda}, {ptrB, ldb}, {ptrC, ldc}, {...
As we all know, GPU is fast because it have a large amount of cores, and the cores are grouped into blocks. In the GPU kernels, we can allocate tasks to different blocks by decomposing them into a series of unrelated sub-tasks. Furthermore, we can further decompose the sub-tasks and ...
OpClassTensorOp, // tag indicating Tensor Cores cutlass::arch::Sm70 // tag indicating target GPU compute architecture >; Gemm gemm_op; cutlass::Status status; // // Launch GEMM on the device // status = gemm_op({ {m, n, k}, {ptrA, lda}, {ptrB, ldb}, {ptrC, ldc}, {...
Once the convolution matrix is formed in Shared Memory, the existing components computing warp-level GEMM accumulate the result of convolution and update the output tensor.This section describes the structure of an efficient Implicit GEMM Convolution CUDA kernel for Turing Tensor Cores....
Benchmarking GPU Tensor Cores on General Matrix Multiplication Kernels through CUTLASS necessary fine tuning, hardware-level ASICs like tensor cores could dramatically boost performance in specific operations like GEMM offloading to modern GPUs... X Huang,X Zhang,P Yang,... - Applied Sciences (2076...
This repository contains CUDA implementations of Gemm operation to compare CUDA and Tensor cores performance. Getting Started Prerequisites NVIDIA GPU with CUDA support CUDA Toolkit installed Installation Clone the repository: $ git clone https://github.com/msiavashi/cuda-tensor-operations.git $ cd ...