Scientific exploration generates expanding volumes of data that commonly require High Performance Computing (HPC) systems to facilitate research. HPC systems are complex ecosystems of hardware and software that frequently are not user friendly. The Usable Data Abstractions (UDA) project set out to build usable software for scientific workflows in HPC environments by undertaking multiple rounds of qualitative user research. Qualitative research investigates how individuals accomplish their work and our interview-based study surfaced a variety of insights about the experiences of working in and with HPC ecosystems. This report examines multiple facets to the experiences of scientists and developers using and supporting HPC systems. We discuss how stakeholders grasp the design and configuration of these systems, the impacts of abstraction layers on their ability to successfully do work, and the varied perceptions of time that shape this work. Examining the adoption of the Cori HPC at NERSC we explore the anticipations and lived experiences of users interacting with this system’s novel storage feature, the Burst Buffer. We present lessons learned from across these insights to illustrate just some of the challenges HPC facilities and their stakeholders need to account for when procuring and supporting these essential scientific resources to ensure their usability and utility to a variety of scientific practices.

Scientific data volumes have been growing exponentially. This has resulted in the need for new tools that enable users to operate on and analyze data. Cyber infrastructure tools, including workflow tools, that have been developed in the last few years has often fallen short if user needs and suffered from lack of wider adoption. User-centered Design (UCD) process has been used as an effective approach to develop usable software with high adoption rates. However, UCD has largely been applied for user-interfaces and there has been limited work in applying UCD to application program interfaces and cyber infrastructure tools. We use an adapted version of UCD that we refer to as Scientist-Centered Design (SCD) to engage with users in the design and development of Tigres, a workflow application programming interface. Tigres provides a simple set of programming templates (e.g., sequence, parallel, split, merge) that can be can used to compose and execute computational and data transformation pipelines. In this paper, we describe Tigres and discuss our experiences with the use of UCD for the initial development of Tigres. Our experience-to-date is that the UCD process not only resulted in better requirements gathering but also heavily influenced the architecture design and implementation details. User engagement during the development of tools such as Tigres is critical to ensure usability and increase adoption.

Workflows are increasingly processing large volumes of data from scientific instruments, experiments and sensors. These workflows often consist of complex data processing and analysis steps that might include a diverse ecosystem of tools and also often involve human-in-the-loop steps. Sharing and reproducing these workflows with collaborators and the larger community is critical but hard to do without the entire context of the workflow including user notes and execution environment. In this paper, we describe Science Capsule, which is a framework to capture, share, and reproduce scientific workflows. Science Capsule captures, manages and represents both computational and human elements of a workflow. It automatically captures and processes events associated with the execution and data life cycle of workflows, and lets users add other types and forms of scientific artifacts. Science Capsule also allows users to create 'workflow snapshots' that keep track of the different versions of a workflow and their lineage, allowing scientists to incrementally share and extend workflows between users. Our results show that Science Capsule is capable of processing and organizing events in near real-time for high-throughput experimental and data analysis workflows without incurring any significant performance overheads.

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology — predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.