Post Syndicated from Macey Neff original https://aws.amazon.com/blogs/compute/optimizing-video-encoding-with-ffmpeg-using-nvidia-gpu-based-amazon-ec2-instances/
This post is written by Alejandro Gil, Solutions Architect and Joseba Echevarría, Solutions Architect.
Introduction
The purpose of this blog post is to compare video encoding performance between CPUs and Nvidia GPUs to determine the price/performance ratio in different scenarios while highlighting where it would be best to use a GPU.
Video encoding plays a critical role in modern media delivery, enabling efficient storage, delivery, and playback of high-quality video content across a wide range of devices and platforms.
Video encoding is frequently performed solely by the CPU because of its widespread availability and flexibility. Still, modern hardware includes specialized components designed specifically to obtain very high performance video encoding and decoding.
Nvidia GPUs, such as those found in the P and G Amazon EC2 instances, include this kind of built-in hardware in their NVENC (encoding) and NVDEC (decoding) accelerator engines, which can be used for real-time video encoding/decoding with minimal impact on the performance of the CPU or GPU.
Figure 1: NVIDIA NVDEC/NVENC architecture. Source https://developer.nvidia.com/video-codec-sdk
Scenario
Two main transcoding job types should be considered depending on the video delivery use case, 1) batch jobs for on demand video files and 2) streaming jobs for real-time, low latency use cases. In order to achieve optimal throughput and cost efficiency, it is a best practice to encode the videos in parallel using the same instance.
The utilized instance types in this benchmark can be found in figure 2 table (i.e g4dn and p3). For hardware comparison purposes, the p4d instance has been included in the table, showing the GPU specs and total number of NVDEC & NVENC cores in these EC2 instances. Based on the requirements, multiple GPU instances types are available in EC2.
| Instance size | GPUs | GPU model | NVDEC generation | NVENC generation | NVDEC cores/GPU | NVENC cores/GPU |
| g4dn.xlarge | 1 | T4 | 4th | 7th | 2 | 1 |
| p3.2xlarge | 1 | V100 | 3rd | 6th | 1 | 3 |
| p4d.24xlarge | 8 | A100 | 4th | N/A | 5 | 0 |
Figure 2: GPU instances specifications
Benchmark
In order to determine which encoding strategy is the most convenient for each scenario, a benchmark will be conducted comparing CPU and GPU instances across different video settings. The results will be further presented using graphical representations of the performance indicators obtained.
The benchmark uses 3 input videos with different motion and detail levels (still, medium motion and high dynamic scene) in 4k resolution at 60 frames per second. The tests will show the average performance for encoding with FFmpeg 6.0 in batch (using Constant Rate Factor (CRF) mode) and streaming (using Constant Bit Rate (CBR)) with x264 and x265 codecs to five output resolutions (1080p, 720p, 480p, 360p and 160p).
The benchmark tests encoding the target videos into H.264 and H.265 using the x264 and x265 open-source libraries in FFmpeg 6.0 on the CPU and the NVENC accelerator when using the Nvidia GPU. The H.264 standard enjoys broad compatibility, with most consumer devices supporting accelerated decoding. The H.265 standard offers superior compression at a given level of quality than H.264 but hardware accelerated decoding is not as widely deployed. As a result, for most media delivery scenarios having more than one video format will be required in order to provide the best possible user experience.
Offline (batch) encoding
This test consists of a batch encoding with two different standard presets (ultrafast and medium for CPU-based encoding and p1 and medium presets for GPU-accelerated encoding) defined in the FFmpeg guide.
The following chart shows the relative cost of transcoding 1 million frames to the 5 different output resolutions in parallel for CPU-encoding EC2 instance (c6i.4xlarge) and two types of GPU-powered instances (g4dn.xlarge and p3.2xlarge). The results are normalized so that the cost of x264 ultrafast preset on c6i.4xlarge is equal to one.
Figure 3: Batch encoding performance for CPU and GPU instances.
The performance of batch encoding in the best GPU instance (g4dn.xlarge) shows around 73% better price/performance in x264 compared to the c6i.4xlarge and around 82% improvement in x265.
A relevent aspect to have in consideration is that the presets used are not exactly equivalent for each hardware because FFmpeg uses different operators depending on where the process runs (i.e CPU or GPU). As a consequence, the video outputs in each case have a noticeable difference between them. Generally, NVENC-based encoded videos (GPU) tend to have a higher quality in H.264, whereas CPU outputs present more encoding artifacts. The difference is more noticeable for lower quality cases (ultrafast/p1 presets or streaming use cases).
The following images compare the output quality for the medium motion video in the ultrafast/p1 and medium presets.
It is clearly seen in the following example, that the h264_nevenc (GPU) codec outperforms the libx264 codec (CPU) in terms of quality, showing less pixelation, especially in the ultrafast preset. For the medium preset, although the quality difference is less pronounced, the GPU output file is noticeably larger (refer to Figure 6 table).
Figure 4: Result comparison between GPU and CPU for h264, ultrafast
Figure 5: Result comparison between GPU and CPU for h264, medium
The output file sizes mainly depend on the preset, codec and input video. The different configurations can be found in the following table.
Figure 6: Sizes for output batch encoded videos. Streaming not represented because the size is the same (fixed bitrate)
Live stream encoding
For live streaming use cases, it is useful to measure how many streams a single instance can maintain transcoding to five output resolutions (1080p, 720p, 480p, 360p and 160p). The following results are the relative cost of each instance, which is the ratio of number of streams the instance was able to sustain divided by the cost per hour.
Figure 6: Streaming encoding performance for CPU and GPU instances.
The previous results show that a GPU-based instance family like g4dn is ideal for streaming use cases, where they can sustain up to 4 parallel encodings from 4K to 1080p, 720p, 480p, 360p & 160p simultaneously. Notice that the GPU-based p5 family performance is not compensating the cost increase.
On the other hand, the CPU-based instances can sustain 1 parallel stream (at most). If you want to sustain the same number of parallel streams in Intel-based instances, you’d have to opt for a much larger instance (c6i.12xlarge can almost sustain 3 simultaneous streams, but it struggles to keep up with the more dynamic scenes when encoding with x265) with a much higher cost ($2.1888 hourly for c6i.12xlarge vs $0.587 for g4dn.xlarge).
The price/performance difference is around 68% better in GPU for x264 and 79% for x265.
Conclusion
The results show that for the tested scenarios there can be a price-performance gain when transcoding with GPU compared to CPU. Also, GPU-encoded videos tend to have an equal or higher perceived quality level to CPU-encoded counterparts and there is no significant performance penalty for encoding to the more advanced H.265 format, which can make GPU-based encoding pipelines an attractive option.
Still, CPU-encoders do a particularly good job with containing output file sizes for most of the cases we tested, producing smaller output file sizes even when the perceived quality is simmilar. This is an important aspect to have into account since it can have a big impact in cost. Depending on the amount of media files distributed and consumed by final users, the data transfer and storage cost will noticeably increase if GPUs are used. With this in mind, it is important to weight the compute costs with the data transfer and storage costs for your use case when chosing to use CPU or GPU-based video encoding.
One additional point to be considered is pipeline flexibility. Whereas the GPU encoding pipeline is rigid, CPU-based pipelines can be modified to the customer’s needs, including additional FFmpeg filters to accommodate future needs as required.
The test did not include any specific quality measurements in the transcoded images, but it would be interesting to perform an analysis based on quantitative VMAF (or similar algorithm) metrics for the videos. We always recommend to make your own test to validate if the results obtained meet your requirements.
Benchmarking method
This blog post extends on the original work described in Optimized Video Encoding with FFmpeg on AWS Graviton Processors and the benchmarking process has been maintained in order to preserve consistency of the benchmark results. The original article analyzes in detail the price/performance advantages of AWS Graviton 3 compared to other processors.
Figure 7: Batch encoding workflow