Examples
Group		Example	Description
	Subgroup
Introduction Examples		introduction_example	cuBLASDx API introduction example
Simple GEMM Examples	Basic Example	simple_gemm_fp32	Performs fp32 GEMM
		simple_gemm_int8_int8_int32	Performs integral GEMM using Tensor Cores
		simple_gemm_cfp16	Performs complex fp16 GEMM
		simple_gemm_fp8	Performs fp8 GEMM
	Extra Examples	simple_gemm_leading_dimensions	Performs GEMM with non-default leading dimensions
		simple_gemm_fp32_decoupled	Performs fp32 GEMM using 16-bit input type to save on storage and transfers
		simple_gemm_std_complex_fp32	Performs GEMM with cuda::std::complex as data type
		simple_gemm_mixed_precision	Performs GEMM with different data type for matrices A, B and C
		simple_gemm_transform	Performs GEMM with transform operators
		simple_gemm_custom_layout	Performs GEMM with custom matrix layouts
		simple_gemm_aat	Performs GEMM where C = A * A^T
NVRTC Examples		nvrtc_gemm	Performs GEMM, kernel is compiled using NVRTC
GEMM Performance		single_gemm_performance	Benchmark for single GEMM
		fused_gemm_performance	Benchmark for 2 GEMMs fused into a single kernel
		device_gemm_performance	Benchmark entire device GEMMs using cuBLASDx for single tile
Advanced Examples	Fusion	gemm_fusion	Performs 2 GEMMs in a single kernel
		gemm_fft	Perform GEMM and FFT in a single kernel
		gemm_fft_fp16	Perform GEMM and FFT in a single kernel (half-precision complex type)
		gemm_fft_performance	Benchmark for GEMM and FFT fused into a single kernel
	Deep Learning	scaled_dot_prod_attn	Scaled dot product attention using cuBLASDx
		scaled_dot_prod_attn_batched	Multi-head attention using cuBLASDx
	Other	batched_gemm_fp64	Manual batching in a single CUDA block
		blockdim_gemm_fp16	BLAS execution with different block dimensions