Assessing Performance Portability of Hybrid Video Encoders

Abstract

While the Java Virtual Machine ensures functional code portability, achieving consistent execution efficiency across hardware generations remains a significant challenge for compute-intensive multimedia applications. This paper evaluates performance portability (PP) in a hybrid video encoder, focusing on the interaction between algorithmic complexity and parallelization granularity. We contrast a legacy fine-grained model, utilizing macroblock-level task scheduling, with a coarse-grained architecture based on Group of Pictures (GOP) parallelism. The study quantifies how these two paradigms respond to increasing core counts and generational shifts in server-grade hardware.The evaluation utilizes three configurations: an intra-frame baseline ($F_{B}$), a hybrid model with integer-pixel motion estimation ($F_{N}$), and a sub-pixel hybrid model ($F_{H}$). Stability is assessed across four server generations (C1-C4, with 12 to 32 cores per socket) using a framework that combines harmonic portability metrics $(\phi)$ with arithmetic efficiency averages $(\Phi)$ and cascade visualizations. This approach identifies the exact points where task synchronization and memory latency limit hardware utilization in managed execution environments.The results demonstrate that the GOP-parallel implementation significantly enhances performance portability, achieving architectural portability metrics $\left(\phi_{\text {arch }}\right)$ between 0.83 and 0.87. In contrast, the legacy macroblock-level model exhibits severe efficiency degradation on modern high-density processors, with portability scores dropping as low as 0.41. Visualization through efficiency cascades and platform fingerprints confirms that the coarse-grained GOP model effectively mitigates synchronization overheads and latency sensitivities. The study demonstrates that fine-grained parallelization strategies are insufficient for maintaining performance portability on modern many-core architectures within managed environments, whereas a coarsegrained GOP-level approach provides a more robust alternative for achieving consistent execution efficiency.