Data-Driven Analysis to Understand GPU Hardware Resource Usage of Optimizations
Tanzima Z. Islam, Aniruddha Marathe, Holland Schutte, Mohammad Zaeed

TL;DR
This paper presents a data-driven analysis of GPU hardware resource usage, characterizing how optimizations impact utilization and performance, and identifying opportunities to enhance efficiency in scientific applications.
Contribution
It introduces a multi-objective metric for analyzing application-device interactions and demonstrates optimization strategies that improve GPU utilization and performance.
Findings
Optimizations can improve GPU utilization and execution time.
A multi-objective metric effectively identifies key application-device interactions.
Applying identified optimizations enhances performance and power efficiency.
Abstract
With heterogeneous systems, the number of GPUs per chip increases to provide computational capabilities for solving science at a nanoscopic scale. However, low utilization for single GPUs defies the need to invest more money for expensive ccelerators. While related work develops optimizations for improving application performance, none studies how these optimizations impact hardware resource usage or the average GPU utilization. This paper takes a data-driven analysis approach in addressing this gap by (1) characterizing how hardware resource usage affects device utilization, execution time, or both, (2) presenting a multi-objective metric to identify important application-device interactions that can be optimized to improve device utilization and application performance jointly, (3) studying hardware resource usage behaviors of several optimizations for a benchmark application, and…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Embedded Systems Design Techniques
