diff --git a/examples/16_ampere_tensorop_conv2dfprop/ampere_tensorop_conv2dfprop.cu b/examples/16_ampere_tensorop_conv2dfprop/ampere_tensorop_conv2dfprop.cu index 11ece8a6..7195c75e 100644 --- a/examples/16_ampere_tensorop_conv2dfprop/ampere_tensorop_conv2dfprop.cu +++ b/examples/16_ampere_tensorop_conv2dfprop/ampere_tensorop_conv2dfprop.cu @@ -53,7 +53,7 @@ can be used to form warp tiles (the tile shape each warp computes), and multiple warp tiles can be used to compute threadblock tiles (the tile shape computed by a threadblock). -In thie example, we split variable initialization into two parts. +In this example, we split variable initialization into two parts. 1. Setting up data properties: describes how tensors are laid out in the memory and how the kernel can view them (logical to physical mapping) diff --git a/examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu b/examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu index 49bfe2f8..563c2a0d 100644 --- a/examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu +++ b/examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu @@ -30,7 +30,7 @@ **************************************************************************************************/ /** -The example demenstrates how to reduce one of the operands of the GEMM along the k-dimension when +The example demonstrates how to reduce one of the operands of the GEMM along the k-dimension when computing GEMM. So the output also contains either a Mx1 or 1XN vector. It only works with Ampere 16x8x16 FP16/BF16 tensor cores, though it is not difficult to apply to other Turing/Ampere tensor core instructions.