ffmpeg中是怎么使用NV 的CUDA进行编码的
ffmpeg中是怎么使用NV 的CUDA进行编码的nvidia有NvENC，不过使用的encoder
https://developer.nvidia.com/nvidia-video-codec-sdk
在编译ffmpeg的时候configure配置时需要开启
--enable-nvenc enable NVIDIA NVENC support
下面是记录的笔记，安装SDK后需要确认一些信息
nvidia-smi-a 查看GPU状态命令
/root/NVIDIA_CUDA-7.0_Samples/bin/x86_64/linux/release 这个下面有一系列的GPU的测试工具，可以使用
查看
# ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "Tesla K20c"
CUDA Driver Version / Runtime Version 7.0 / 7.0
CUDA Capability Major/Minor version number: 3.5
Total amount of global memory: 4800 MBytes (5032706048 bytes)
(13) Multiprocessors, (192) CUDA Cores/MP: 2496 CUDA Cores
GPU Max Clock rate: 706 MHz (0.71 GHz)
Memory Clock rate: 2600 Mhz
Memory Bus Width: 320-bit
L2 Cache Size: 1310720 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers2D=(16384, 16384), 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: Enabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 4 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.0, CUDA Runtime Version = 7.0, NumDevs = 1, Device0 = Tesla K20c
Result = PASS
https://github.com/Brainiarc7/ffmpeg_libnvenc
也可以参考这个 大师兄 这个nvenc搞出来了但是我用的是GTX TITANX 这个显卡，每个系统限制2路转码，这个是不能接受的，现在准备改成cuda并行计算进行加速，你有没有什么资料？谢谢大师兄:D 好牛逼的显卡 learn_ffmpeg 发表于 2016-6-13 15:21
好牛逼的显卡
现在看懂了？ 知道cuda怎么用了！但是要把实现cuda加速x265的编码，我还需要走很长的路:lol
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