1st Workshop on Reproducible Workflows, Data Management, and Security

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 afterwards 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 are 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 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.

Topics of Interest:

  • Position and Experience papers related to scientific applications and platforms on related topics (particularly the topics listed below)

  • Big Data or AI workflow systems like Spark, Hadoop, and Tensorflow in conjunction with data management and reproducibility efforts and techniques

  • Front end systems for configuring or controlling workflows

  • Data management tools and techniques

  • Privacy preserving methods to enable data sharing

  • Workflow engines designed to simplify workflow construction for end users

  • Provenance management and collection techniques and tools

  • Application-specific workflow implementations

  • Mechanisms to support combining multiple application components into a composite application or workflow

  • Software engineering tools and techniques to support workflow creation, execution, and use

  • Reproducibility supporting infrastructure

  • In situ analytics or visualization support for workflows

  • System software/OS features to enable workflow tools and application composition

  • Programming support for assembling workflows or connecting application components

  • Reusable components intended as either ``glue'' between workflow components or for analysis or other processing

  • Storage (both disk and in compute area) support for buffering between components

  • Programming support for addressing data format/contents mismatch

  • Programming support for resource management

Submissions accepted in EasyChair:


Papers should be formatted in IEEE format following eScience formatting rules and can be 5 pages not including references.

Important Dates:

  • Submission Deadline (firm): June 11, 2021 AoE

  • Responses to Authors: July 10, 2021

  • Camera Ready due: July 19, 2021

Proposed Program Committee:

  • Claire Bowen (Urban Institute)

  • Juliana Freire (Reprozip)

  • Loïc Pottier: (ISI)

  • Rafael Ferreira Da Silva (ISI)

  • Jakob Lüttgau: (DKRZ)

  • Tom Peterka (ANL)

  • Jay Jay Billings (ORNL)

  • Margo Seltzer (Harvard)

  • Hariharan Devarajan (LLNL)

  • Dmitry Duplyakin (NREL)

Organizing Committee:

  • Jay Lofstead (Sandia)

  • Silvina Caíno-Lores (UTK)

  • Anthony Kougkas (IIT)

  • Evercita Eugenio (Sandia)

  • Jai Dayal (Intel)

  • Paula Olaya (UTK)