Work

Dynamic Detection of Inefficient Data Mapping Patterns in Heterogeneous OpenMP Applications

Year: 2026

Type: conference-paper

Abstract: With the growing prevalence of heterogeneous computing, CPUs are increasingly being paired with accelerators to achieve new levels of performance and energy efficiency. However, data movement between devices remains a significant bottleneck, complicating application development. Existing performance tools require considerable programmer intervention to diagnose and locate data transfer inefficiencies. To address this, we propose dynamic analysis techniques to detect and profile inefficient data transfer and allocation patterns in heterogeneous applications. We implemented these techniques into OMPDataPerf, which provides detailed traces of problematic data mappings, source code attribution, and assessments of optimization potential in heterogeneous OpenMP applications. OMPDataPerf uses the OpenMP Tools Interface (OMPT) and incurs only a 5 % geometric‑mean runtime overhead.

Source: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

DOI: 10.1145/3774934.3786454

Authors: Luke Marzen, Junhyung Shim, Ali Jannesari

Institution: Iowa State University

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Iowa State University

Awards: CISE-ANR: SHF: Small: Generating Highly Performant Code via Polyhedral Compilation and Graph-based Learning, Collaborative Research: SHF: MEDIUM: Smart Integrated Tuning of Parallel Code for Multicore and Manycore Systems