What is it about?
This position paper challenges the current standards for evaluating parallel machine learning (ML) systems. As AI models and datasets grow exponentially, we rely on powerful parallel computing to keep up. To measure progress, the industry uses benchmarks like MLPerf, which are critical for comparing different solutions. However, these established benchmarks almost exclusively focus on quantitative metrics like speed (throughput) and accuracy. This narrow view often fails to capture the qualities that determine a solution's practical success. To address this gap, we introduce a new evaluation framework called NNOPP (Neural Network on-top-of Parallel Processing). NNOPP proposes a paradigm shift, advocating for a holistic approach that integrates critical qualitative assessments alongside traditional quantitative measures. It provides a structured method for evaluating seven key criteria essential for real-world viability: Quantitative Criteria: Performance, Scalability Qualitative Criteria: Portability, Complexity, Sustainability Scope-Related Criteria: Novelty, Usefulness By assessing factors like deployment complexity, adaptability across different hardware, and long-term sustainability, NNOPP provides a far more nuanced and comprehensive understanding of an ML solution's true value.
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Why is it important?
The current obsession with speed and accuracy is creating a critical blind spot in the field. We risk optimizing for solutions that excel in controlled lab settings but are impractical, brittle, or unsustainable in the real world. A system that is incredibly fast but impossible to deploy on different cloud platforms, requires heroic engineering effort to maintain, or is exorbitantly expensive to run is not a truly advanced solution. The paper is important because it calls for a fundamental re-evaluation of what we value in parallel ML. By embracing a more holistic and multi-dimensional evaluation framework, we can foster true innovation, bridge the Research-to-reality gap, and promote sustainability.
Perspectives
As a researcher working at the intersection of high-performance computing and machine learning, I've observed a growing disconnect. There's incredible ingenuity in the development of parallel algorithms, but their real-world adoption is often held back by practical challenges that standard benchmarks fail to capture. I wrote this paper because I believe it's time for our community to have a serious conversation about our priorities. Optimizing for a single number on a leaderboard may be satisfying, but it doesn't always translate to meaningful progress.
Abdulfatah Bahbouh
University of Texas at Arlington
Read the Original
This page is a summary of: Invited Paper: Rethinking Benchmarks for Parallel Machine Learning Techniques: Integrating Qualitative and Quantitative Evaluation Metrics, June 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3743642.3743649.
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