FEVES: Framework for Efficient Parallel Video Encoding on Heterogeneous Systems

Lead by high performance computing potential of modern heterogeneous desktop systems and predominance of video content in general applications, we propose herein an autonomous unified video encoding framework for hybrid multi-core CPU and multi-GPU platforms. To fully exploit the capabilities of these platforms, the proposed framework integrates simultaneous execution control, automatic data access management, and adaptive scheduling and load balancing strategies to deal with the overall complexity of the video encoding procedure. These strategies consider the collaborative inter-loop encoding as a unified optimization problem to efficiently exploit several levels of concurrency between computation and communication. To support a wide range of CPU and GPU architectures, a specific encoding library is developed with highly optimized algorithms for all inter-loop modules. The obtained experimental results show that the proposed framework allows achieving a real-time encoding of full high-definition sequences in the state-of-the-art CPU+GPU systems, by outperforming individual GPU and quad-core CPU executions for more than 2 and 5 times, respectively.