Mixed Modality Workflows in TaskVine

Modern scientific workflows desire to mix several different computing modalities: self-contained computational tasks, data-intensive transformations, and serverless function calls. To date, these modalities have required distinct system architectures with different scheduling objectives and constraints. In this paper, we describe how TaskVine, a new workflow execution platform, combines these modalities into an execution platform with shared abstractions. We demonstrate results of the system executing a machine learning workflow with combined standalone tasks and serverless functions.