The increasing adoption of heterogeneous platforms that combine CPUs with accelerators such as GPUs in high-performance computing (HPC) introduces new challenges for performance analysis and optimization. Traditional efficiency metrics, such as those proposed by the Performance Optimization and Productivity (POP) Center of Excellence, were designed primarily for homogeneous CPU-based systems and therefore, do not capture the complex interactions between host and device resources. In this work, we extend the POP efficiency framework to heterogeneous architectures by introducing a new hierarchy of metrics that separately evaluate host and device efficiency. On the host side, we quantify the effectiveness of hybrid execution and offloading operations. On the device side, we propose a multiplicative hierarchy analogous to the host hierarchy and define its Parallel Efficiency branch. Beyond their definition and formulation, we present the implementation of these metrics in the TALP module of the DLB library. TALP is a lightweight monitoring library that provides measurements both post mortem and at runtime, with outputs available in textual and machine-readable formats. We validate the proposed framework through synthetic benchmarks and three production HPC applications, demonstrating how the metrics expose inefficiencies in offloading, load balance, and orchestration. Results show that the extended TALP metrics provide actionable insights to guide developers in optimizing heterogeneous HPC codes.

翻译：异构平台（将CPU与GPU等加速器结合）在高性能计算（HPC）领域的日益普及，为性能分析与优化带来了全新挑战。传统效率指标（如性能优化与生产力卓越中心提出的指标）主要针对同构CPU系统设计，因此无法捕捉主机与设备资源间的复杂交互。本研究将POP效率框架拓展至异构架构，引入新的指标层级体系，分别评估主机与设备效率。在主机端，我们量化混合执行与卸载操作的有效性；在设备端，提出与主机层级类似的可乘性层级结构，并定义其并行效率分支。除定义与公式外，我们还在DLB库的TALP模块中实现了这些指标。TALP作为轻量级监测库，支持事后与运行时测量，输出结果提供文本及机器可读格式。通过合成基准测试与三个生产级HPC应用验证该框架，证明这些指标能揭示卸载、负载均衡及编排中的低效问题。结果表明，扩展后的TALP指标可提供可操作的洞察，指导开发者优化异构HPC代码。