Emerging and future computational workloads are combining traditional HPC applications with tools and techniques from the scale-out data analytics and machine learning community. Getting these technologies to co-exist and interoperate to advance scientific discovery is a daunting task with few known good solutions. In general, constructing these workflows has the potential to create pitfalls and incompatibilities that limit adoption.

Formalizing the steps necessary for an application or data processing pipeline is increasingly popular and necessary. Requirements for reproducibility artifacts for publishing venues are also driving this formalization. The processes and infrastructure to accomplish these requirements are frequently bespoke or custom for a particular research area. All of these formalization activities can be described as workflow systems. Existing off-the-shelf tools address a distributed environment fairly well, but are not complete solutions and do not address the scale-up community much, if at all.

Complicating managing workflows are the tasks of managing data both during workflow execution and then afterward as well as offering authentication and data security for shared data sets. With some data, such as climate simulation output, being subject to intense scrutiny, it becomes crucial to offer open data that can be verified as authentic by means of encrypted creator identities, and accessible only to people with a need to know. Sharing and analyzing the data knowing it is authentic while protecting the privacy of the creators is essential for reliable open science while protecting the identity of the scientists performing the work.

This workshop seeks to explore ideas and experiences on what kinds of infrastructure developments can improve upon the state of the art. Explorations of component packaging via containers and virtual machines, automation scripting, deployment,  portability builds, and system support for these and other relevant activities are key infrastructure. Provenance collection, exploration, and tracking are key for a well-documented scientific output. Using existing systems to achieve these goals via experiences is important for developing best practices that span application domains. Data privacy techniques such as multi-party encryption and differential privacy are important as well. Issues with managing large data sets and workflow intermediate data, particularly those intended to manage publicly accessed data for use and reuse are encouraged. New techniques and technologies that address reproducibility requirements are also requested. We seek work on all of these, and related, topics as well as position and experience papers looking to drive the conversation for practitioners and researchers in these spaces.

This workshop contributes by sharing experiences and exploring the various technological infrastructure needs to support effective, convenient workflow systems and application composition structures and approaches across a broad spectrum of HPC environments from clusters to supercomputers to cloud systems. 

Agenda: ReWorDS 24