ffmpeg Nvidia硬件加速总结

淡淡的烟草味﹌ 2022-06-13 02:48 364阅读 0赞

2017年5月25日

## 0. 概述 ##

FFmpeg可通过Nvidia的GPU进行加速，其中高层接口是通过Video Codec SDK来实现GPU资源的调用。Video Codec SDK包含完整的的高性能工具、源码及文档，支持，可以运行在Windows和Linux系统之上。从软件上来说，SDK包含两类硬件加速接口，用于编码加速的NVENCODE API和用于解码加速的NVDECODE API(之前被称为NVCUVID API)。从硬件上来说，Nvidia GPU有一到多个编解码器(解码器又称硬件加速引擎)，它们独立于CUDA核。从视频格式上来说，编码支持H.264、H.265、无损压缩，位深度支持8bit、10bit，色域空间支持YUV 4:4:4和4:2:0，分辨率支持最高8K；解码支持MPEG-2、VC1、VP8、VP9、H.264、H.265、无损压缩，位深度支持8 bit、10bit、12bit，色域空间支持YUV 4:2:0，分辨率支持最高8K。Video Codec SDK已经被集成在ffmpeg工程中，但是ffmpeg对编解码器配置参数较少，如果需要充分的发挥编解码器特性，还需要直接使用SDK进行编程。  
![这里写图片描述][SouthEast]  
Nvidia编码器与CPU上的x264的性能对比与质量对比如下图所示，性能以每秒钟编码帧数为参考指标，质量以PSNR为参考指标。  
![这里写图片描述][SouthEast 1]  
可看出性能方面Nvidia编码器是x264的2~5倍，质量方面对于fast stream场景来说Nvidia编码器优于x264，高质量场景来说低于x264，但没有说明是哪款Nvidia的产品，以及对比测试的x264运行平台的CPU的型号及平台能力。下图可以看出对于1080P@30fps，NVENC可支持21路的编码或9路的高质量编码。  
![这里写图片描述][SouthEast 2]  
不同型号的GPU的编码的能力表格如下：  
![这里写图片描述][SouthEast 3]  
Nvidia解码器性能指标如下图所示，不过只有两款Tesla的产品。  
![这里写图片描述][SouthEast 4]  
解码的能力表格如下：  
![这里写图片描述][SouthEast 5]

## 1. 安装驱动与SDK ##

## 1.1 前期准备 ##

需要关闭所有开源的显示驱动  
vi /etc/modprobe.d/blacklist.conf  
添加  
blacklist amd76x\_edac  
blacklist vga16fb  
blacklist nouveau  
blacklist nvidiafb  
blacklist rivatv

## 1.2 驱动安装 ##

(1). 删除原来的驱动  
apt-get remove –purge nvidia\*  
(2). 官方下载run文件的驱动进行安装  
service lightdm stop  
chmod 777 NVIDIA-Linux-x86\_64-367.44.run  
./NVIDIA-Linux-x86\_64-367.44.run  
service lightdm start  
reboot  
(2). 驱动安装验证  
运行nvidia-smi，有如下输出则安装成功  
![这里写图片描述][SouthEast 6]  
问题1：如果重启之后发现图形界面进不去，发生了循环登录，说明视频驱动没有安装完全，需要重装驱动，保险的方法是联网安装  
console中执行  
apt-get remove –purge nvidia-\*  
add-apt-repository ppa:graphics-drivers/ppa  
apt-get update  
service lightdm stop  
apt-get install nvidia-375 nvidia-settings nvidia-prime  
nvidia-xconfig  
apt-get install mesa-common-dev //安装缺少的库  
apt-get install freeglut3-dev  
update-initramfs -u  
reboot

## 1.3 SDK安装 ##

(1). 官方下载run文件的驱动进行安装  
cuda\_8.0.44\_linux.run –no-opengl-libs //不需要opengl支持  
apt-get install freeglut3-dev build-essential libx11-dev  
apt-get install libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa  
apt-get install libglu1-mesa-dev  
gedit ~/.bashrc  
添加  
export PATH=/usr/local/cuda/bin:$PATH

export LD\_LIBRARY\_PATH=/usr/local/cuda/lib64:$LD\_LIBRARY\_PATH

gedit /etc/ld.so.conf.d/cuda.conf  
添加  
/usr/local/cuda/lib64  
/lib  
/lib32  
/lib64  
/usr/lib  
/user/lib32  
sudo ldconfig  
(2). SDK安装验证  
运行nvcc -V，有如下输出则安装成功。  
![这里写图片描述][SouthEast 7]

## 2. Sample测试 ##

## 2.1 Sample编译 ##

进入Samples目录，运行make，如果没有安装OpenGL相关库，则NvDecodeGL会编译不通过  
每个工程的含义可参考《NVIDIA\_Video\_Codec\_SDK\_Samples\_Guide》  
NvEncoder: 基本功能的编码  
NvEncoderCudaInterpo: CUDA surface的编码  
NvEncoderD3D9Interpo: D3D9 surface的编码，Linux下没有  
NvEncoderLowLatency: 低延时特征的使用，如帧内刷新与参考图像有效性(RPI)  
NvEncoderPerf: 最大性能的编码  
NvTranscoder: NVENC的转码能力  
NvDecodeD3D9: 视频解码D3D9显示，Linux下没有  
NvDecodeD3D11: 视频解码D3D11显示，Linux下没有  
NvDecodeGL: 视频解码OpenGL显示

## 2.2 Sample测试 ##

参见《NVIDIA\_Video\_Codec\_SDK\_Samples\_Guide》  
问题2：如果运行例子后显示libcuda.so failed!  
在/usr/lib/x86\_64-linux-gnu下制作链接libcuda.so，链接至libcuda.so.375.26

## 3. ffmpeg结合 ##

## 3.1 ffmpeg编译 ##

## 3.1.1 前期工作 ##

确保Video\_Codec\_SDK\_7.1.9/Samples/common/inc 目录下有基本的头文件  
确保Video\_Codec\_SDK\_7.1.9/Samples/common/lib/linux/x86\_64 目录下有libGLEW.a

## 3.1.2 configure命令 ##

configure \
      --enable-version3 \
      --enable-libfdk-aac \
      --enable-libmp3lame \
      --enable-libx264 \
      --enable-nvenc \
      --extra-cflags=-I/root/workspace/Video_Codec_SDK_7.1.9/Samples/common/inc \
      --extra-ldflags=-L/root/workspace/Video_Codec_SDK_7.1.9/Samples/common/lib/linux/x86_64 \
      --enable-shared \
      --enable-gpl \
      --enable-postproc \
      --enable-nonfree \
      --enable-avfilter \
      --enable-pthreads
     
      
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## 3.1.2 make ##

运行make & make install

## 3.2 ffmpeg测试 ##

运行ffmpeg -codecs|grep nvenc  
显示一下信息说明

ffmpeg version 3.0.git Copyright (c) 2000-2016 the FFmpeg developers
      built with gcc 5.4.0 (Ubuntu 5.4.0-6ubuntu1~16.04.1) 20160609
      configuration: --enable-version3 --enable-libfdk-aac --enable-libmp3lame --enable-libx264 --enable-nvenc --extra-cflags=-I/workspace/Video_Codec_SDK_7.1.9/Samples/common/inc --extra-ldflags=-L/workspace/Video_Codec_SDK_7.1.9/Samples/common/lib/linux/x86_64 --enable-shared --enable-gpl --enable-postproc --enable-nonfree --enable-avfilter --enable-pthreads
      libavutil      55. 29.100 / 55. 29.100
      libavcodec     57. 54.100 / 57. 54.100
      libavformat    57. 48.100 / 57. 48.100
      libavdevice    57.  0.102 / 57.  0.102
      libavfilter     6. 57.100 /  6. 57.100
      libswscale      4.  1.100 /  4.  1.100
      libswresample   2.  1.100 /  2.  1.100
      libpostproc    54.  0.100 / 54.  0.100
     DEV.LS h264                 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10 (encoders: libx264 libx264rgb h264_nvenc nvenc nvenc_h264 )
     DEV.L. hevc                 H.265 / HEVC (High Efficiency Video Coding) (encoders: nvenc_hevc hevc_nvenc )
     
      
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其中前缀含义如下：  
前缀含义  
D….. = Decoding supported  
.E…. = Encoding supported  
..V… = Video codec  
..A… = Audio codec  
..S… = Subtitle codec  
…I.. = Intra frame-only codec  
….L. = Lossy compression  
…..S = Lossless compression

## 3.3 编解码器使用方法 ##

h265编码测试  
(1). ffmpeg -s 1920x1080 -pix\_fmt yuv420p -i BQTerrace\_1920x1080\_60.yuv -vcodec hevc\_nvenc -r 60 -y 2\_60.265  
(2). ffmpeg -s 1920x1080 -pix\_fmt yuv420p -i BQTerrace\_1920x1080\_60.yuv -vcodec hevc\_nvenc -r 30 -y 2\_30.265

h264编码测试  
(3). ffmpeg -s 1920x1080 -pix\_fmt yuv420p -i BQTerrace\_1920x1080\_60.yuv -vcodec h264\_nvenc -r 60 -y 2\_60.264  
(4). ffmpeg -s 1920x1080 -pix\_fmt yuv420p -i BQTerrace\_1920x1080\_60.yuv -vcodec h264\_nvenc -r 30 -y 2\_30.264

h264转h265  
(5). ffmpeg -i 1\_60.264 -vcodec hevc\_nvenc -r 60 -y 2\_60\_264to265.265  
(6). ffmpeg -i 1\_30.264 -vcodec hevc\_nvenc -r 30 -y 2\_30\_264to265.265

h265转h264  
(7). ffmpeg -i 1\_60.265 -vcodec h264\_nvenc -r 60 -y 2\_60\_265to264.264  
(8). ffmpeg -i 1\_30.265 -vcodec h264\_nvenc -r 30 -y 2\_30\_265to264.264

## 3.4 程序开发使用方法 ##

av\_find\_encoder\_by\_name(“h264\_nvenc”);  
av\_find\_encoder\_by\_name(“hevc\_nvenc”);

## 4. 辅助工具 ##

watch -n 1 nvidia-smi  
以1秒钟为间隔来查看GPU资源占用情况

## 5. 实测结果 ##

## 5.1 硬件性能 ##

本人用Geforce GTX1070与Tesla P4进行了测试，两者都是Pascal架构。  
(1). GTX1070的硬件信息如下(deviceQuery显示)：

CUDA Device Query (Runtime API) version (CUDART static linking)
    Detected 1 CUDA Capable device(s)
    Device 0: "GeForce GTX 1070"
      CUDA Driver Version / Runtime Version          8.0 / 8.0
      CUDA Capability Major/Minor version number:    6.1
      Total amount of global memory:                 8110 MBytes (8504279040 bytes)
      (15) Multiprocessors, (128) CUDA Cores/MP:     1920 CUDA Cores
      GPU Max Clock rate:                            1683 MHz (1.68 GHz)
      Memory Clock rate:                             4004 Mhz
      Memory Bus Width:                              256-bit
      L2 Cache Size:                                 2097152 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: 65536
      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 2 copy engine(s)
      Run time limit on kernels:                     Yes
      Integrated GPU sharing Host Memory:            No
      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 / 5 / 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 = GeForce GTX 1070
    Result = PASS
     
      
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(2). P4的硬件信息如下：

CUDA Device Query (Runtime API) version (CUDART static linking)
    Detected 1 CUDA Capable device(s)
    Device 0: "Tesla P4"
      CUDA Driver Version / Runtime Version          8.0 / 8.0
      CUDA Capability Major/Minor version number:    6.1
      Total amount of global memory:                 7606 MBytes (7975862272 bytes)
      (20) Multiprocessors, (128) CUDA Cores/MP:     2560 CUDA Cores
      GPU Max Clock rate:                            1114 MHz (1.11 GHz)
      Memory Clock rate:                             3003 Mhz
      Memory Bus Width:                              256-bit
      L2 Cache Size:                                 2097152 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: 65536
      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 2 copy engine(s)
      Run time limit on kernels:                     Yes
      Integrated GPU sharing Host Memory:            No
      Support host page-locked memory mapping:       Yes
      Alignment requirement for Surfaces:            Yes
      Device has ECC support:                        Enabled
      Device supports Unified Addressing (UVA):      Yes
      Device PCI Domain ID / Bus ID / location ID:   0 / 5 / 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 = Tesla P4
    Result = PASS
     
      
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## 5.2 实验结果 ##

(1). GTX1070  
| | hevc编码 | h264编码 | h264转h265 | h265转h264 |  
| 60fps | 387fps(6.45x) | 430fps(7.17x) | 348fps(5.79x) | 170fps(2.84x) |  
| 30fps | 345fps(11.5x) | 429fps(14.3x) | 318fps(10.6x) | 94fps(3.13x) |  
(2). P4

## 5.3 实验分析 ##

虽然在硬件性能上，P4比GTX1070显存略少，主频略低，CUDA的数量多出了33%，但从实验结果上看除了h265->h264结果持平外，P4表现都要逊色于GTX1070，这和官网所言“编解码器独立于CUDA核”相一致。

## 6. 源码分析 ##

集成在ffmpeg框架内的视频编解码器需要定义一个AVCodec结构体包含（私有结构体AVClass、三个函数等）

## 6.1 h264部分 ##

(1). 结构体(nvenc\_h264.c)

AVCodec ff_h264_nvenc_encoder = {
        .name           = "h264_nvenc",
        .long_name      = NULL_IF_CONFIG_SMALL("NVIDIA NVENC H.264 encoder"),
        .type           = AVMEDIA_TYPE_VIDEO,
        .id             = AV_CODEC_ID_H264,
        .init           = ff_nvenc_encode_init, //初始化函数
        .encode2        = ff_nvenc_encode_frame, //编码函数
        .close          = ff_nvenc_encode_close, //关闭函数
        .priv_data_size = sizeof(NvencContext),  //内部数据结构，见nvenc.h
        .priv_class     = &h264_nvenc_class,     //私有结构体
        .defaults       = defaults,
        .capabilities   = AV_CODEC_CAP_DELAY,
        .caps_internal  = FF_CODEC_CAP_INIT_CLEANUP,
        .pix_fmts       = ff_nvenc_pix_fmts,
    };
    
    static const AVClass h264_nvenc_class = {
        .class_name = "h264_nvenc",
        .item_name = av_default_item_name,
        .option = options, //编码器选项参数在这个AVOption结构体中
        .version = LIBAVUTIL_VERSION_INT,
    };
     
      
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注意还有两个AVCodec，一个名字叫nvenc、一个叫nvenc\_h264，对应三大函数与h264\_nvenc是一样的  
(2). 处理函数(nvenc.c)

av_cold int ff_nvenc_encode_init(AVCodecContext *avctx)
    {
       NvencContext *ctx = avctx->priv_data; //读入私有结构体
       ...
       //下面是一些nvenc的api
       nvenc_load_libraries
       nvenc_setup_device
       nvenc_setup_encoder
       nvenc_setup_surfaces
       nvenc_setup_extradata
       ...
    }
    int ff_nvenc_encode_frame(AVCodecContext *avctx, AVPacket *pkt,
                              const AVFrame *frame, int *got_packet)
    {
        ...
        if (frame) {
            inSurf = get_free_frame(ctx); //来一帧
            ...
            res = nvenc_upload_frame(avctx, frame, inSurf);//编一帧
            ...
        }
    }
    av_cold int ff_nvenc_encode_close(AVCodecContext *avctx)
    {
       ...
       //一些free和destroy的工作
    }
     
      
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## 6.2 h265部分 ##

(1). 结构体(nvenc\_hevc.c)

AVCodec ff_hevc_nvenc_encoder = {
        .name           = "hevc_nvenc",
        .long_name      = NULL_IF_CONFIG_SMALL("NVIDIA NVENC hevc encoder"),
        .type           = AVMEDIA_TYPE_VIDEO,
        .id             = AV_CODEC_ID_HEVC,
        .init           = ff_nvenc_encode_init, //初始化函数
        .encode2        = ff_nvenc_encode_frame, //编码函数
        .close          = ff_nvenc_encode_close, //关闭函数
        .priv_data_size = sizeof(NvencContext),  //内部数据结构，见nvenc.h
        .priv_class     = &hevc_nvenc_class, //私有结构体
        .defaults       = defaults,
        .pix_fmts       = ff_nvenc_pix_fmts,
        .capabilities   = AV_CODEC_CAP_DELAY,
        .caps_internal  = FF_CODEC_CAP_INIT_CLEANUP,
    };
    
    static const AVClass hevc_nvenc_class = {
        .class_name = "hevc_nvenc",
        .item_name = av_default_item_name,
        .option = options,//编码器选项参数在这个AVOption结构体中
        .version = LIBAVUTIL_VERSION_INT,
    };
     
      
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注意还有一个AVCodec，一个叫nvenc\_hevc，对应三大函数与h264\_nvenc是一样的  
(2) 处理函数(nvenc.c)  
同h264的处理函数

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