The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate on scientific data. The FAIR principles can be applied to the data being handled by a scientific workflow as well as the processes, software, and other infrastructure which are necessary to specify and execute a workflow. The FAIR principles were designed as guidelines, rather than rules, that would allow for differences in standards for different communities and for different degrees of compliance. There are many practical considerations which impact the level of FAIR-ness that can actually be achieved, including policies, traditions, and technologies. Because of these considerations, obstacles are often encountered during the workflow lifecycle that trace directly to shortcomings in the implementation of the FAIR principles. Here, we detail some cases, without naming names, in which data and workflows were Findable but otherwise lacking in areas commonly needed and expected by modern FAIR methods, tools, and users. We describe how some of these problems, all of which were overcome successfully, have motivated us to push on systems and approaches for fully FAIR workflows.

Spatially distributed environmental sampling generates highly complex and multidimensional datasets illuminating key insights into microbial diversity, evolutionary-coevolutionary processes, and host-pathogen interactions. While these sampling methods generate high value datasets, dataset size, the dataset integration, visualization, analysis, and provenance tracking present significant bottlenecks to scientific discovery. To address this bottleneck, we developed EcoKMER, an R-Shiny front-end application designed to streamline metagenomic data accessibility and provide geospatial context to data in support of hypothesis-driven investigations into environmental sampling, supported by DataFed as its back-end data management platform. EcoKMER enables interactive visualization and filtering of harmonized metagenomic data and metadata using an interoperable approach, allowing users to extract spatially distributed sample-based metadata on top of environmental parameters such as geolocation, temperature, pH, for investigating ecological changes across time and space enhancing sample processing methods. As an example, we deployed this tool to track analysis of metagenomes from the organisms in the Salish Sea Estuary, consistent with existing community-accepted standards. Built on top of DataFed, a flexible and robust scientific data management system built for data lakehouse architectures, EcoKMER is positioned as a powerful tool to improve sampling strategy decision making, accelerate new insights for collaborative biological and environmental research, and fostering AI-ready analyses designed to enhance discovery and guidance for bioeconomic engineering.

Cutting-edge science is increasingly data-driven due to the emergence of scientific machine learning models that can guide scientists toward fruitful areas of exploration. Experimental science facilities such as light and neutron sources, particle colliders, and radio astronomy telescopes are also producing raw measurement data at rates that exceed available data storage and computing capacity at those facilities. As a result, scientific workflows are being developed that concurrently couple experiments at science facilities with high-performance computing (HPC) facilities to enable analysis of experimental data while the experiment is ongoing, and where analysis results are potentially fed back to the experiment for use in experimental control and/or steering in a time-sensitive manner. Our goal is to design, prototype, and deploy a new capability for the Oak Ridge Leadership Computing Facility (OLCF) that enables such workflows through support for bidirectional, memory-based streaming of data from external experiments into and out of OLCF HPC systems. This high-level design document describes the related work and motivating use cases that inform our understanding of the technical requirements for this capability, and describes a proposed architectural solution that meets these requirements and our plans for demonstrating the capability.

This report highlights significant strides made by Oak Ridge National Laboratory’s Oak Ridge LeadershipComputing Facility (OLCF) in advancing computational research and infrastructure. Through the AdvancedComputing Ecosystem (ACE) strategic initiative, OLCF has been successfully integrated with DOE’sIntegrated Research Infrastructure (IRI) program, establishing itself as a critical framework for enhancingscientific computing capabilities across various domains. This report outlines the activities, accomplishments,and future directions of ACE, emphasizing its role in developing cutting-edge technologies, supportingscience pilots, and fostering collaborations that drive scientific innovation.One of the key achievements of ACE in FY24 has been the establishment of a robust testbed environmentthat facilitates the development and validation of new computing technologies. This testbed, integratedinto the larger IRI ecosystem through Energy Sciences Network (ESnet), allows for seamless collaborationamong DOE’s Advanced Scientific Computing Research (ASCR) facilities, including the Argonne LeadershipComputing Facility (ALCF), the National Energy Research Scientific Computing Center (NERSC), andJefferson Lab (JLab). The testbed has been instrumental in enabling science pilots to experiment withinnovative methodologies in a controlled environment, thereby accelerating the translation of research intopractical applications.ACE has been instrumental in advancing a series of science pilots using computational resources fromOLCF. These pilots span a wide range of scientific domains, from basic energy sciences to biological andenvironmental research, demonstrating the versatility and impact of the ACE framework. By integratingcutting-edge technologies, such as AI-driven analytics and real-time data processing, with HPC, these pilotshave enabled significant advancements in scientific workflows. For instance, projects like LCLStreamand DELERIA have showcased the potential of ACE in optimizing experimental designs, acceleratingdata analysis, and driving innovations that are critical for addressing some of the most complex scientificchallenges of IRI pathfinder projects.ACE has also made significant progress in foundational technology development, particularly in areas suchas data movement, interface design, and scheduling. These advancements have been key in addressingthe complex requirements of modern scientific workflows, ensuring that OLCF remains at the forefront ofhigh-performance computing (HPC). The report details several ongoing projects, including the developmentof the OLCF Facility API, Data Streaming technologies, and the Zambeze distributed orchestration system,which together provide a secure and flexible framework for managing computational resources across adistributed infrastructure.In addition to technical achievements, ACE has played a central role in outreach and engagement activi-ties. Through participation in conferences, hackathons, and training sessions, the ACE team has activelycontributed to the broader scientific community. These efforts have not only facilitated the exchange ofknowledge but have also strengthened collaborations with other DOE facilities and research institutions,further enhancing the impact of the ACE program.Looking ahead, the report outlines strategic plans for FY25, which include continued support for sciencepilots, further integration with ASCR facilities, and the exploration of new technologies. These initiatives aredesigned to ensure that ACE remains a dynamic and adaptable platform, capable of meeting the evolvingneeds of the scientific community. The ongoing collaboration with ORNL’s INTERSECT initiative and theplanned technology refreshes underscore ACE’s commitment to maintaining its leadership in HPC.

We present a collection of techniques for exploiting latent I/O asynchrony which can substantially improve performance in data-intensive parallel applications. Latent asynchrony refers to an application's tolerance for decoupling ancillary operations from its core computation, and is a property of HPC codes not fully explored by current HPC I/O systems. Decoupling operations such as buffering and staging, reorganization, and format conversion in space and in time from core codes can shorten I/O phases, preserving valuable MPP compute cycles. We describe in this paper DataTaps, IOgraphs, and Metabots, three tools which allow HPC developers to implement decoupled I/O operations. Using these tools, asynchrony can be exploited by data generators which overlap computation with communication, and by data consumers that perform data conversion and reorganization out-of-band and on-demand. In the context of a data-intensive fusion simulation, we show that exploiting latent asynchrony through decoupling of operations can provide significant performance benefits.