Taylan Özden | Search arXiv

arXiv2026

Characterizing the I/O Behavior of HPC Applications through Modeling and Simulation

Parallel applications process large amounts of data, leading to intensive parallel I/O operations. These operations can exhibit different levels of complexity, including, among others, multiple I/O access patterns, data staging, and contention risks. Therefore, in order to exploit high-performance computing (HPC) systems efficiently and optimize the I/O performance, it is crucial to consider the I/O behaviour of the HPC applications. In this work, we have developed a framework that reproduces the I/O access pattern of real applications in a simulated environment provided by ElastiSim, a batch-system simulator for rigid, malleable, and evolving workloads. The simulated applications are generated based on I/O traces captured from real applications provided by the HPC Input/Output (HPCIO) analysis repository. The HPCIO analysis database includes traces combined with information about real applications' performance across different parallel I/O libraries and layers of the I/O stack. We have conducted detailed case studies of real-world applications' traces to demonstrate how the proposed modeling framework can provide insights into the performance characteristics of I/O applications, including the I/O congestion analysis based on the application's I/O access pattern.

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arXiv2026

Evaluating Malleable Job Scheduling in HPC Clusters using Real-World Workloads

Optimizing resource utilization in high-performance computing (HPC) clusters is essential for maximizing both system efficiency and user satisfaction. However, traditional rigid job scheduling often results in underutilized resources and increased job waiting times. This work evaluates the benefits of resource elasticity, where the job scheduler dynamically adjusts the resource allocation of malleable jobs at runtime. Using real workload traces from the Cori, Eagle, and Theta supercomputers, we simulate varying proportions (0-100%) of malleable jobs with the ElastiSim software. We evaluate five job scheduling strategies, including a novel one that maintains malleable jobs at their preferred resource allocation when possible. Results show that, compared to fully rigid workloads, malleable jobs yield significant improvements across all key metrics. Considering the best-performing scheduling strategy for each supercomputer, job turnaround times decrease by 37-67%, job makespan by 16-65%, job wait times by 73-99%, and node utilization improves by 5-52%. Although improvements vary, gains remain substantial even at 20% malleable jobs. This work highlights important correlations between workload characteristics (e.g., job runtimes and node requirements), malleability proportions, and scheduling strategies. These findings confirm the potential of malleability to address inefficiencies in current HPC practices and demonstrate that even limited adoption can provide substantial advantages, encouraging its integration into HPC resource management.

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