This code demonstrates a usage of cuBLAS gemmStridedBatched function to compute strided batches of matrix-matrix products
A = | 1.0 | 2.0 | 5.0 | 6.0 |
| 3.0 | 4.0 | 7.0 | 8.0 |
B = | 5.0 | 6.0 | 9.0 | 10.0 |
| 7.0 | 8.0 | 11.0 | 12.0 |
This function performs the matrix-matrix multiplication of a batch of matrices. The batch is considered to be "uniform", i.e. all instances have the same dimensions (m, n, k), leading dimensions (lda, ldb, ldc) and transpositions (transa, transb) for their respective A, B and C matrices. Input matrices A, B and output matrix C for each instance of the batch are located at fixed offsets in number of elements from their locations in the previous instance. Pointers to A, B and C matrices for the first instance are passed to the function by the user along with offsets in number of elements - strideA, strideB and strideC that determine the locations of input and output matrices in future instances.
See documentation for further details.
All GPUs supported by CUDA Toolkit (https://developer.nvidia.com/cuda-gpus)
Linux
Windows
x86_64
ppc64le
arm64-sbsa
- A Linux/Windows system with recent NVIDIA drivers.
- CMake version 3.18 minimum
$ mkdir build
$ cd build
$ cmake ..
$ make
Make sure that CMake finds expected CUDA Toolkit. If that is not the case you can add argument -DCMAKE_CUDA_COMPILER=/path/to/cuda/bin/nvcc to cmake command.
$ mkdir build
$ cd build
$ cmake -DCMAKE_GENERATOR_PLATFORM=x64 ..
$ Open cublas_examples.sln project in Visual Studio and build
$ ./cublas_gemmStridedBatched_example
Sample example output:
A[0]
1.00 2.00
3.00 4.00
=====
A[1]
5.00 6.00
7.00 8.00
=====
B[0]
5.00 6.00
7.00 8.00
=====
B[1]
9.00 10.00
11.00 12.00
=====
C[0]
19.00 22.00
43.00 50.00
=====
C[1]
111.00 122.00
151.00 166.00
=====