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<p>Effective resource allocation has become a critical issue in high-performance computing (HPC) systems. To effectively allocate resources (e.g., CPU/GPU cores), recent studies focus on predicting each workload’s runtime using machine learning and deep learning models. These methods in HPC often suffer from underestimation, as 33–64% of jobs terminate due to wallclock time limits, whereas user-provided estimates achieve 78–99% success. This failure stems from minimizing mean squared error, which biases predictions toward average-case performance and underestimates jobs in high-skewed (i.e., long-tail) runtime distributions. Specifically, HPC workloads exhibit long-tail runtime distributions, with most jobs completing quickly while a small fraction runs for extremely long durations. To overcome these challenges, we introduce an uncertainty-aware runtime prediction (UARP) method based on multi-quantile regression. Our method directly addresses the underestimation problem by quantifying uncertainty by modeling the conditional distribution without distributional assumptions. Our approach uses the highest-quantile (99th) model and the residual model from the median quantile. The 99th model primarily provides conservative bounds and protection against job underestimates, while the residual uncertainty model protects against unpredictable workloads by estimating prediction variance. In particular, the expected predicted error (i.e., uncertainty) from the residual model plays a critical role in our adaptive safety margin calculation. UARP adds a conservative prediction (99th quantile) and an additional safety margin from our formula, enabling our adaptive margin approach specifically tailored to each job’s characteristics. Evaluation on four production HPC systems (SDSC DataStar, KIT FH2, ANL Interpid, KISTI NURION), UARP achieves 92–99% job success rates while maintaining resource utilization within 1–2% of EASY backfilling. Our method deploys with identical parameters across all systems. This parameter-free deployment eliminates the per-system tuning that fixed-margin approaches require. In addition, our approach integrates with existing schedulers through minimal modification, utilizing uncertainty-aware predictions to prevent timeout-based job termination and preserve system efficiency.</p>

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UARP: uncertainty-aware runtime prediction for preventing scheduler termination under Wallclock constraints in HPC

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Jiheon Choi,
  • Sangyoon Oh

摘要

Effective resource allocation has become a critical issue in high-performance computing (HPC) systems. To effectively allocate resources (e.g., CPU/GPU cores), recent studies focus on predicting each workload’s runtime using machine learning and deep learning models. These methods in HPC often suffer from underestimation, as 33–64% of jobs terminate due to wallclock time limits, whereas user-provided estimates achieve 78–99% success. This failure stems from minimizing mean squared error, which biases predictions toward average-case performance and underestimates jobs in high-skewed (i.e., long-tail) runtime distributions. Specifically, HPC workloads exhibit long-tail runtime distributions, with most jobs completing quickly while a small fraction runs for extremely long durations. To overcome these challenges, we introduce an uncertainty-aware runtime prediction (UARP) method based on multi-quantile regression. Our method directly addresses the underestimation problem by quantifying uncertainty by modeling the conditional distribution without distributional assumptions. Our approach uses the highest-quantile (99th) model and the residual model from the median quantile. The 99th model primarily provides conservative bounds and protection against job underestimates, while the residual uncertainty model protects against unpredictable workloads by estimating prediction variance. In particular, the expected predicted error (i.e., uncertainty) from the residual model plays a critical role in our adaptive safety margin calculation. UARP adds a conservative prediction (99th quantile) and an additional safety margin from our formula, enabling our adaptive margin approach specifically tailored to each job’s characteristics. Evaluation on four production HPC systems (SDSC DataStar, KIT FH2, ANL Interpid, KISTI NURION), UARP achieves 92–99% job success rates while maintaining resource utilization within 1–2% of EASY backfilling. Our method deploys with identical parameters across all systems. This parameter-free deployment eliminates the per-system tuning that fixed-margin approaches require. In addition, our approach integrates with existing schedulers through minimal modification, utilizing uncertainty-aware predictions to prevent timeout-based job termination and preserve system efficiency.