Jetson TX2 初体验
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摘要： # 0. 简介 Jetson TX2【1】是基于 NVIDIA Pascal™ 架构的 AI 单模块超级计算机，性能强大（1 TFLOPS），外形小巧，节能高效（7.5W），非常适合机器人、无人机、智能摄像机和便携医疗设备等智能终端设备。 Jatson TX2 与 TX1 相比，内存和 eMMC 提高了一倍，CUDA 架构升级为 Pascal，每瓦性能提高一倍，支持 Jetson TX1
Jetson TX2【1】是基于 NVIDIA Pascal™ 架构的 AI 单模块超级计算机，性能强大（1 TFLOPS），外形小巧，节能高效（7.5W），非常适合机器人、无人机、智能摄像机和便携医疗设备等智能终端设备。
Jatson TX2 与 TX1 相比，内存和 eMMC 提高了一倍，CUDA 架构升级为 Pascal，每瓦性能提高一倍，支持 Jetson TX1 模块的所有功能，支持更大、更深、更复杂的深度神经网络。
TX2 内部结构如下：
过程细节不展开，板卡上电后来张照片：
TX2 出厂时，已经自带了 Ubuntu 16.04 系统，可以直接启动。但一般我们会选择刷机，目的是更新到最新的 JetPack L4T，并自动安装最新的驱动、CUDA Toolkit、cuDNN、TensorRT。
刷机注意以下几点：
刷机成功后，重启 TX2，连接键盘鼠标显示器，就可以跑 Demo 了。
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
视频截图如下：
TensorRT 【3】是 Nvidia GPU 上的深度学习 inference 优化库，可以将训练好的模型通过优化器生成 inference 引擎
将 TX2 设置为 MAXP （最高性能）模式，运行 TensorRT 加速的 GoogLeNet、VGG16 得到处理性能如下：
【1】嵌入式系统开发者套件和模块 | NVIDIA Jetson | NVIDIA
【2】Download and Install JetPack L4T
【3】TensorRT
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 X2
Result = PASS
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.
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 Summary
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