The main role of the project:
- CUDA'usage(each test code gives the implementation of C ++ and CUDA, respectively, and gives the calculation time for each method)
- TensorRT's usage
- Simd's usage Simd GitHub
- OpenMP's usage
- SIMD's usage
- Assembly Language's usage(MASM, NASM)
- Eigen's usage
CUDA test code(Note: depend on opencv):
- simple
- vector add: C = A + B
- matrix multiplication: C = A * B
- dot product
- Julia
- ripple
- green ball
- ray tracking
- heat conduction
- calculate histogram
- streams' usage
- layer(approximate)
- channel normalize(mean/standard deviation)
- reverse
- prior_vbox
- image process
- bgr to gray
- bgr to bgr565
- gray image histogram equalization(only C++ implementation)
- gray image edge detection: Laplacian(only C++ implementation)
Eigen test code:
- transpose
- determinant
- inverse matrix
- norm
- eigenvalues/eigenvectors
- SVD(Singular Value Decomposition)
- pseudoinverse
- trace
- mean, variance, standard deviation
- covariance matrix
TensorRT 2.1.2 test code(only support linux):
- MNIST
- MNIST API(use api produce network)
- GoogleNet
- CharRNN
- Plugin(add a custom layer)
- MNIST Infer(serialize TensorRT model)
The project support platform:
- windows10 64 bits: It can be directly build with VS2022 in windows10 64bits(Except TensorRT).
- Linux:
- CUDA supports cmake build(file position: prj/linux_cuda_cmake)
- TensorRT support cmake build(file position: prj/linux_tensorrt_cmake)
- Simd_Test support cmake build(file position: prj/linux_simd_cmake)
- AssemblyLanguage_Test support cmake build(file position: prj/linux_assembly_language_cmake)
- Eigen_Test support cmake build(file position: prj/linux_eigen_cmake)
Screenshot:
Blog: fengbingchun