cudnn samples build and run

cudnn sample deb
/usr/src/cudnn_sample_v8

cuda 安裝在　/usr/local/cuda

copy sample 到　home folder
到　conv_sample 下

~/cudnn_samples_v8/conv_sample$ make
CUDA_VERSION is 10020
Linking agains cublasLt = true
CUDA VERSION: 10020
TARGET ARCH: x86_64
TARGET OS: linux
SMS: 35 50 53 60 61 62 70 72 75 
/usr/local/cuda/bin/nvcc -ccbin g++ -I/usr/local/cuda/include -I/usr/local/cuda/targets/ppc64le-linux/include  -m64    -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_62,code=sm_62 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_72,code=sm_72 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o fp16_dev.o -c fp16_dev.cu
g++ -I/usr/local/cuda/include -I/usr/local/cuda/targets/ppc64le-linux/include   -o fp16_emu.o -c fp16_emu.cpp
g++ -I/usr/local/cuda/include -I/usr/local/cuda/targets/ppc64le-linux/include   -o conv_sample.o -c conv_sample.cpp
/usr/local/cuda/bin/nvcc -ccbin g++   -m64      -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_62,code=sm_62 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_72,code=sm_72 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o conv_sample fp16_dev.o fp16_emu.o conv_sample.o -I/usr/local/cuda/include -I/usr/local/cuda/targets/ppc64le-linux/include -L/usr/local/cuda/lib64 -L/usr/local/cuda/targets/ppc64le-linux/lib -lcublasLt -lcudart -lcublas -lcudnn -lstdc++ -lm

測試一下...

~/cudnn_samples_v8/conv_sample$ ./conv_sample 
Executing: conv_sample
Using format CUDNN_TENSOR_NCHW (for INT8x4 and INT8x32 tests use CUDNN_TENSOR_NCHW_VECT_C)
Testing single precision
====USER DIMENSIONS====
input dims are 1, 32, 4, 4
filter dims are 32, 32, 1, 1
output dims are 1, 32, 4, 4
====PADDING DIMENSIONS====
padded input dims are 1, 32, 4, 4
padded filter dims are 32, 32, 1, 1
padded output dims are 1, 32, 4, 4
Testing conv
^^^^ CUDA : elapsed = 7.00951e-05 sec,  
Test PASSED
Testing half precision (math in single precision)
====USER DIMENSIONS====
input dims are 1, 32, 4, 4
filter dims are 32, 32, 1, 1
output dims are 1, 32, 4, 4
====PADDING DIMENSIONS====
padded input dims are 1, 32, 4, 4
padded filter dims are 32, 32, 1, 1
padded output dims are 1, 32, 4, 4
Testing conv
^^^^ CUDA : elapsed = 3.69549e-05 sec,  
Test PASSED