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

Ghazal Rahimi (), Victor Lopez (), Marc Clasca (), Joan Vinyals-Ylla-Catala (), Jesus Labarta () and Marta Garcia-Gasulla ()
Additional contact information
Ghazal Rahimi: Barcelona Supercomputing Center
Victor Lopez: Barcelona Supercomputing Center
Marc Clasca: Barcelona Supercomputing Center
Joan Vinyals-Ylla-Catala: Barcelona Supercomputing Center
Jesus Labarta: Barcelona Supercomputing Center
Marta Garcia-Gasulla: Barcelona Supercomputing Center

A chapter in Tools for High Performance Computing 2023, 2026, pp 18-45 from Springer

Abstract: 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.

Keywords: High Performance Computing; Accelerators; Efficiency metrics; Performance analysis (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16397-4_2

Ordering information: This item can be ordered from
http://www.springer.com/9783032163974

DOI: 10.1007/978-3-032-16397-4_2

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().