Dynamic Detection of Inefficient Data Mapping Patterns in Heterogeneous OpenMP Applications

TL;DR

This paper introduces OMPDataPerf, a dynamic analysis tool that detects and profiles inefficient data transfer patterns in heterogeneous OpenMP applications, helping developers optimize performance with minimal overhead.

Contribution

It presents a novel dynamic analysis approach integrated into OMPDataPerf for identifying data mapping inefficiencies in heterogeneous OpenMP programs.

Findings

Detects inefficient data transfer patterns with 5% overhead

Provides detailed profiling and source code attribution

Assists in optimizing data movement in heterogeneous systems

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.