Speakers
Description
Scientific workflows are increasingly important in driving scientific discoveries, and future supercomputers must be designed and tuned to execute them efficiently. However, evaluating the performance of emerging computing systems using production-scale workflows is costly and energy-inefficient, especially at extreme scales. Moreover, application-level mini-apps do not capture workflows’ heterogeneity, sophistication, and end-to-end behavior. We propose workflow mini-apps, a modeling technique that faithfully reproduces the key performance characteristics of real workflows while remaining portable across systems and architectures and enabling low-cost, reproducible evaluation. In this work, we model the performance and scalability of several Simulation-with-ML workflows by generating workflow mini-apps from representative workflow instances and exposing adjustable parameters to emulate families of similar workflows. In addition to modeling workflow by their own directly, we introduce a workflow classification that groups workflows into classes based on their component task characteristics and performance traits. Using this classification, we provide class-level performance prediction and analysis, enabling users to reason about scaling behavior and system suitability beyond single instances. This supports informed HPC infrastructure choices, targeted performance optimization, and clearer expectations of scaling outcomes.