Hardware-Agnostic and Insightful Efficiency Metrics for Accelerated Systems: Definition and Implementation within TALP

TL;DR

This paper extends efficiency metrics for heterogeneous HPC systems, implementing them in TALP to provide insights into host and device performance and guide optimization.

Contribution

It introduces a new hierarchy of efficiency metrics tailored for heterogeneous architectures, implemented within the TALP monitoring library.

Findings

Metrics reveal inefficiencies in offloading and load balancing.

Validation on benchmarks and HPC applications demonstrates actionable insights.

Extended metrics improve understanding of host-device interactions.

Abstract

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.