Compute capability defines the hardware features and supported instructions for each NVIDIA GPU architecture.
// Matrix multiplication kernel called by MatMul() __global__ void MatMulKernel(Matrix A, Matrix B, Matrix C) { // Block row and column int blockRow = blockIdx.y; int blockCol = blockIdx.x; // Each thread block computes one sub-matrix Csub of C Matrix Csub = GetSubMatrix(C, b...
compute Csub // Multiply each pair of sub-matrices together // and accumulate the results for (int m = 0; m < (A.width / BLOCK_SIZE); ++m) { // Get sub-matrix Asub of A Matrix Asub = GetSubMatrix(A, blockRow, m); // Get sub-matrix Bsub of B Matrix Bsub = GetSubMatrix...
编写一个高性能的CUDA SGEMM(Single precision General Matrix Multiplication) 需要多少工作呢?我们将从一个简单的kernel开始,逐步优化,直到达到95%的cuBLAS(NVIDIA的官方库)性能。 译者注:SGEMM执行的操作是:C = αAB+βC,其中C/A/B都是矩阵,α/β是标量。DGEMM是表示双精度浮点乘法。 Kernel GFLOPs/s ...
Ampere Tensor Core一条Tensor Core指令可以支持16x8x16的fp16的矩阵乘法,因此我们后续反汇编查看到指定compute capability = 86的SASS代码中清一色的都是HMMA.16816指令了。 Matrix 16 - 8 - 256bit Layout 从Volta第一次引入Tensor Core开始,到Ampere的Tensor Core,基本的演进除了数据类型的增加,更重要的是峰值性...
NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility to evaluate and determine the applicability of any informatio...
比如说：Compute Capability 5.x ,A multiprocessor consists of:128 CUDA cores for arithmetic operations (see Arithmetic Instructions for throughputs of arithmetic operations),32 special function units for single-precision floating-point transcendental functions,4 warp schedulers.Compute Capability...
在 Compute Capability 5.x 及之后的卡上,shared memory 具有 32 个 bank,刚好是一个 warp 中线程的数量。而如果同一个 warp 中不同 thread 均只访问 4 Byte 数据且希望同时访问同一个 bank 的数据将会有两种结果。(对于每一个 thread 访问更多数据的行为将在后面提到)...
A thread block size of 16x16 (256 threads), although arbitrary(任意的) in this case, is a common choice. The grid is created with enough blocks to have one thread per matrix element as before. For simplicity, this example assumes that the number of threads per grid in each dimension is...
NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility to evaluate and determine the applicability of any informatio...