All example, including more advanced onces, are shipped within cuBLASDx package.
This folder demonstrates cuBLASDx APIs usage.
- cuBLASDx/MathDx package
- See cuBLASDx requirements
- CMake 3.18 or newer
- Linux system with installed NVIDIA drivers
- NVIDIA GPU of Volta (SM70) or newer architecture
- You may specify
CUBLASDX_CUDA_ARCHITECTURESto limit CUDA architectures used for compilation (see CMake:CUDA_ARCHITECTURES) mathdx_ROOT- path to mathDx package (XX.Y - version of the package)
mkdir build && cd build
cmake -DCUBLASDX_CUDA_ARCHITECTURES=70-real -Dmathdx_ROOT=/opt/nvidia/mathdx/XX.Y ..
make
// Run
ctest
For the detailed descriptions of the examples please visit Examples section of the cuBLASDx documentation.
| Group | Subgroup | Example | Description |
|---|---|---|---|
| Introduction Examples | introduction_example | cuBLASDx API introduction example | |
| Simple GEMM Examples | Basic Example | simple_gemm_fp32 | Performs fp32 GEMM |
| simple_gemm_cfp16 | Performs complex fp16 GEMM | ||
| Extra Examples | simple_gemm_leading_dimensions | Performs GEMM with non-default leading dimensions | |
| simple_gemm_std_complex_fp32 | Performs GEMM with cuda::std::complex as data type | ||
| 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 | ||
| Advanced Examples | Fusion | fused_gemm | 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 | multiblock_gemm | Proof-of-concept for single large GEMM using multiple CUDA blocks | |
| batched_gemm_fp64 | Manual batching in a single CUDA block | ||
| blockdim_gemm_fp16 | BLAS execution with different block dimensions |