Jetson TX2上的demo（原创）
Jetson TX2上的demo
一、快速傅里叶-海动图 sample
The CUDA samples directory is copied to the home directory on the device by JetPack. The built binaries are in the following directory:
/home/ubuntu/NVIDIA_CUDA-<version>_Samples/bin/armv7l/linux/release/gnueabihf/
这里的version需要看你自己安装的CUDA版本而定
Run the samples at the command line or by double-clicking on them in the file browser. For example, when you run the oceanFFT sample, the following screen is displayed.
二、车辆识别加框sample
nvidia@tegra-ubuntu:~/tegra_multimedia_api/samples/backend$
./backend 1 ../../data/Video/sample_outdoor_car_1080p_10fps.h264 H264
–trt-deployfile ../../data/Model/GoogleNet_one_class/GoogleNet_modified_oneClass_halfHD.prototxt
–trt-modelfile ../../data/Model/GoogleNet_one_class/GoogleNet_modified_oneClass_halfHD.caffemodel –trt-forcefp32 0 –trt-proc-interval 1 -fps 10
三、GEMM（通用矩阵乘法）测试
nvidia@tegra-ubuntu:/usr/local/cuda/samples/7_CUDALibraries/batchCUBLAS$ ./batchCUBLAS -m1024 -n1024 -k1024
batchCUBLAS Starting…
GPU Device 0: “NVIDIA Tegra X2” with compute capability 6.2
==== Running single kernels ====
Testing sgemm#### args: ta=0 tb=0 m=1024 n=1024 k=1024 alpha = (0xbf800000, -1) beta= (0x40000000, 2)#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.00372291 sec GFLOPS=576.83@@@@ sgemm test OK
Testing dgemm#### args: ta=0 tb=0 m=1024 n=1024 k=1024 alpha = (0x0000000000000000, 0) beta= (0x0000000000000000, 0)#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.10940003 sec GFLOPS=19.6296@@@@ dgemm test OK
==== Running N=10 without streams ====
Testing sgemm#### args: ta=0 tb=0 m=1024 n=1024 k=1024 alpha = (0xbf800000, -1) beta= (0x00000000, 0)#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.03462315 sec GFLOPS=620.245@@@@ sgemm test OK
Testing dgemm#### args: ta=0 tb=0 m=1024 n=1024 k=1024 alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0)#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 1.09212208 sec GFLOPS=19.6634@@@@ dgemm test OK
==== Running N=10 with streams ====
Testing sgemm#### args: ta=0 tb=0 m=1024 n=1024 k=1024 alpha = (0x40000000, 2) beta= (0x40000000, 2)#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.03504515 sec GFLOPS=612.776@@@@ sgemm test OK
Testing dgemm#### args: ta=0 tb=0 m=1024 n=1024 k=1024 alpha = (0xbff0000000000000, -1) beta= (0x0000000000000000, 0)#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 1.09177494 sec GFLOPS=19.6697@@@@ dgemm test OK
==== Running N=10 batched ====
Testing sgemm#### args: ta=0 tb=0 m=1024 n=1024 k=1024 alpha = (0x3f800000, 1) beta= (0xbf800000, -1)#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 0.03766394 sec GFLOPS=570.17@@@@ sgemm test OK
Testing dgemm#### args: ta=0 tb=0 m=1024 n=1024 k=1024 alpha = (0xbff0000000000000, -1) beta= (0x4000000000000000, 2)#### args: lda=1024 ldb=1024 ldc=1024
^^^^ elapsed = 1.09389901 sec GFLOPS=19.6315@@@@ dgemm test OK
Test Summary0 error(s)
四、内存带宽测试
nvidia@tegra-ubuntu:/usr/local/cuda/samples/1_Utilities/bandwidthTest$ ./bandwidthTest
[CUDA Bandwidth Test] – Starting…
Running on…
Device 0: NVIDIA Tegra X2
Quick Mode
Host to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 20215.8
Device to Host Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 20182.2
Device to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 35742.8
Result = PASS
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
五、设备查询
nvidia@tegra-ubuntu:~/work/TensorRT/tmp/usr/src/tensorrt$ cd /usr/local/cuda/samples/1_Utilities/deviceQuery
nvidia@tegra-ubuntu:/usr/local/cuda/samples/1_Utilities/deviceQuery$ ls
deviceQuery deviceQuery.cpp deviceQuery.o Makefile NsightEclipse.xml readme.txt
nvidia@tegra-ubuntu:/usr/local/cuda/samples/1_Utilities/deviceQuery$ ./deviceQuery
./deviceQuery Starting…
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: “NVIDIA Tegra X2”
CUDA Driver Version / Runtime Version 8.0 / 8.0
CUDA Capability Major/Minor version number: 6.2
Total amount of global memory: 7851 MBytes (8232062976 bytes)
( 2) Multiprocessors, (128) CUDA Cores/MP: 256 CUDA Cores
GPU Max Clock rate: 1301 MHz (1.30 GHz)
Memory Clock rate: 1600 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 524288 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 32768
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: No
Integrated GPU sharing Host Memory: Yes
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = NVIDIA Tegra X2Result = PASS
六、大型项目的测试
详情查看https://developer.nvidia.com/embedded/jetpack
这里面还有一些项目