How to use GPU to accelerate the processing speed of ffmpeg filter?

To speed up FFmpeg filter processing using GPU, follow these steps:

1. Selecting the Right GPU Acceleration Library

First, identify the GPU type in your system, such as NVIDIA or AMD, as different GPUs support distinct acceleration libraries. For example, NVIDIA GPUs typically support CUDA and NVENC/NVDEC, while AMD GPUs support OpenCL and VCE.

2. Installing and Configuring FFmpeg for GPU Support

Ensure your FFmpeg version is compiled with support for the relevant GPU. For NVIDIA GPUs, verify that FFmpeg is compiled with the --enable-cuda-nvcc, --enable-cuvid, and --enable-nvenc options.

For instance, use the following command to configure FFmpeg for NVIDIA GPU support:

bash
./configure --enable-cuda-nvcc --enable-cuvid --enable-nvenc --extra-cflags="-I/usr/local/cuda/include" --extra-ldflags="-L/usr/local/cuda/lib64"

Confirm the CUDA toolkit is installed on your system to enable compilation and runtime access to necessary libraries.

3. Using GPU-Accelerated Filters

Once FFmpeg is properly configured, you can begin utilizing GPU-accelerated filters. For example, employ the h264_nvenc encoder to leverage NVIDIA GPU capabilities for video encoding.

A straightforward command-line example for GPU-accelerated video transcoding is:

bash
ffmpeg -hwaccel cuda -i input.mp4 -c:v h264_nvenc output.mp4

Here, -hwaccel cuda specifies using CUDA to accelerate the decoding process.

4. Performance Monitoring and Tuning

Monitoring GPU usage and performance during acceleration is essential. Utilize NVIDIA's nvidia-smi tool or AMD's radeontop for this purpose.

Based on monitoring results, fine-tune your FFmpeg command or filter configuration to optimize performance and resource utilization.

5. Testing and Validation

Finally, conduct thorough testing to validate video quality and encoding efficiency. Compare the differences between GPU-accelerated and non-GPU-accelerated processing, including processing speed and CPU/GPU load metrics.

Example

Suppose you need to scale a video file while accelerating the process using an NVIDIA GPU; use the following command:

bash
ffmpeg -hwaccel cuda -i input.mp4 -vf "scale_cuda=w=1280:h=720" -c:v h264_nvenc output.mp4

Here, scale_cuda is a CUDA-optimized filter that efficiently performs image scaling.

By following these steps and examples, you can effectively leverage GPU acceleration to enhance FFmpeg's video processing capabilities, significantly improving processing speed and efficiency.