這段時間在實現Gpu的視頻流解碼，遇到了很多的問題。
得到了阿里視頻處理專家蔡鼎老師以及英偉達開發季光老師的指導，在這里表示感謝！
基本命令（linux下）
1.查看物理顯卡
lspci | grep -i vga root@g1060server:/home/user# lspci | grep -i vga 09:00.0 VGA compatible controller: ASPEED Technology, Inc. ASPEED Graphics Family (rev 30) 81:00.0 VGA compatible controller: NVIDIA Corporation Device 1c03 (rev a1) 82:00.0 VGA compatible controller: NVIDIA Corporation Device 1c03 (rev a1)
2.直接查看英偉達的物理顯卡信息
有的時候因為服務器型號，GPU型號等不兼容等問題，會導致主板無法識別到插入的顯卡，
我們可用下面的命令來查看主板是否識別到了顯卡：
root@g1060server:/home/user# lspci | grep -i nvidia 81:00.0 VGA compatible controller: NVIDIA Corporation Device 1c03 (rev a1) 81:00.1 Audio device: NVIDIA Corporation Device 10f1 (rev a1) 82:00.0 VGA compatible controller: NVIDIA Corporation Device 1c03 (rev a1) 82:00.1 Audio device: NVIDIA Corporation Device 10f1 (rev a1)
出現上面的東西，說明主板已經識別到顯卡信息
cuda版本，驅動信息
root@g1060server:/home/user# nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2013 NVIDIA Corporation Built on Wed_Jul_17_18:36:13_PDT_2013 Cuda compilation tools, release 5.5, V5.5.0
英偉達顯卡運行狀態信息
root@g1060server:/home/user# nvidia-smi modprobe: ERROR: could not insert 'nvidia_340': No such device NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.
查看失敗，一般沒安裝驅動
user@g1060server:~$ nvidia-smi Fri Jan 5 21:50:34 2018 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 384.90 Driver Version: 384.90 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 GeForce GTX 106... Off | 00000000:81:00.0 On | N/A | | 32% 35C P8 10W / 120W | 3083MiB / 6071MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 1 GeForce GTX 106... Off | 00000000:82:00.0 Off | N/A | | 32% 37C P8 10W / 120W | 2542MiB / 6072MiB | 0% Default | +-------------------------------+----------------------+----------------------+
查看成功
查看cuda驅動是否安裝成功
root@g1060server:/home/user# cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery root@g1060server:/usr/local/cuda-8.0/samples/1_Utilities/deviceQuery# ls deviceQuery deviceQuery.cpp deviceQuery.o Makefile NsightEclipse.xml readme.txt root@g1060server:/usr/local/cuda-8.0/samples/1_Utilities/deviceQuery# make make: 沒有什么可以做的為 `all'。 root@g1060server:/usr/local/cuda-8.0/samples/1_Utilities/deviceQuery# ./deviceQuery ./deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) cudaGetDeviceCount returned 35 -> CUDA driver version is insufficient for CUDA runtime version Result = FAIL
再次確認cuda驅動安裝失敗
查看cuda是否安裝成功 /usr/local/cuda/extras/demo_suite/deviceQuery root@g1060server:/home/user/mjl/test# /usr/local/cuda/extras/demo_suite/deviceQuery /usr/local/cuda/extras/demo_suite/deviceQuery Starting... CUDA Device Query (Runtime API) version (CUDART static linking) Detected 2 CUDA Capable device(s) Device 0: "GeForce GTX 1060 6GB" CUDA Driver Version / Runtime Version 9.0 / 8.0 CUDA Capability Major/Minor version number: 6.1 Total amount of global memory: 6071 MBytes (6366363648 bytes) (10) Multiprocessors, (128) CUDA Cores/MP: 1280 CUDA Cores GPU Max Clock rate: 1709 MHz (1.71 GHz) Memory Clock rate: 4004 Mhz Memory Bus Width: 192-bit L2 Cache Size: 1572864 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: No 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 / 129 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > Device 1: "GeForce GTX 1060 6GB" CUDA Driver Version / Runtime Version 9.0 / 8.0 CUDA Capability Major/Minor version number: 6.1 Total amount of global memory: 6073 MBytes (6367739904 bytes) (10) Multiprocessors, (128) CUDA Cores/MP: 1280 CUDA Cores GPU Max Clock rate: 1709 MHz (1.71 GHz) Memory Clock rate: 4004 Mhz Memory Bus Width: 192-bit L2 Cache Size: 1572864 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: No 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 / 130 / 0 Compute Mode: < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) > > Peer access from GeForce GTX 1060 6GB (GPU0) -> GeForce GTX 1060 6GB (GPU1) : Yes > Peer access from GeForce GTX 1060 6GB (GPU1) -> GeForce GTX 1060 6GB (GPU0) : Yes deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.0, CUDA Runtime Version = 8.0, NumDevs = 2, Device0 = GeForce GTX 1060 6GB, Device1 = GeForce GTX 1060 6GB Result = PASS
查看成功
主要流程
要想實現ffempg的GPU化，必須要要對ffempg的解碼流程有基本的認識才能改造（因為GPU也是這個流程，不過中間一部分用GPU運算）
我在http://www.cnblogs.com/baldermurphy/p/7828337.html 中曾經帖出過CPU解碼的流程
主要流程如下
avformat_network_init(); av_register_all();//1.注冊各種編碼解碼模塊，如果3.3及以上版本，里面包含GPU解碼模塊 std::string tempfile = “xxxx”;//視頻流地址 avformat_find_stream_info(format_context_, nullptr）//2.拉取一小段數據流分析，便於得到數據的基本格式 if (AVMEDIA_TYPE_VIDEO == enc->codec_type && video_stream_index_ < 0)//3.篩選出視頻流 codec_ = avcodec_find_decoder(enc->codec_id);//4.找到對應的解碼器 codec_context_ = avcodec_alloc_context3(codec_);//5.創建解碼器對應的結構體 av_read_frame(format_context_, &packet_); //6.讀取數據包 avcodec_send_packet(codec_context_, &packet_) //7.發出解碼 avcodec_receive_frame(codec_context_, yuv_frame_) //8.接收解碼 sws_scale(y2r_sws_context_, yuv_frame_->data, yuv_frame_->linesize, 0, codec_context_->height, rgb_data_, rgb_line_size_) //9.數據格式轉換
GPU解碼需要改變4,7,8,9這幾個步驟，也就是
找到gpu解碼器，
拉取數據給GPU解碼器，
得到解碼后的數據，
數據格式使用gpu轉換（如果需要的話，如nv12轉bgra），
最終的格式由具體的需求確定，比如很多opengl的互操作，轉成指定的格式（bgra），共用一段內存，數據立刻刷新，連拷貝都不用；
如果是轉化成圖片，又是另一種需求(bgr)；
適用場景的匹配
不得不提的一點是，GPU運算是一個很好的功能，可是也要看需求和場景，如果不考慮這個，可能得不償失
比如一個極端的例子，opencv里面也有實現圖片的解碼，可是在追求效率的時候不會使用它的，
因為一張圖片數據上傳到GPU（非並行，很耗時），解碼（非常快），GPU顯存拷貝到內存（非並行，很耗時）
在上傳和拷貝出來的就花了幾百毫秒，而圖片數據的操作很頻繁，這會導致cpu占用率的得不到很好的緩解，甚至有的時候，不降反升，解碼雖然快，可是用戶的體驗是慢，而且CPU，GPU都占用了
主要的幾個網站
英偉達推薦的ffempg的gpu解碼sdk
https://developer.nvidia.com/nvidia-video-codec-sdk
檢查顯存泄露的工具
http://docs.nvidia.com/cuda/cuda-memcheck/index.html#device-side-allocation-checking