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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2026

REDI – Readiness Engine for Data Integration

The Readiness Engine for Data Integration (REDI) is an open-source framework for automating, standardizing, and assessing the process of preparing scientific data for AI training. REDI implements a five-stage pipeline (ingest, preprocess, transform, structure, output) with per-stage provenance instrumentation via Flowcept, domain-aware transformation logic (PII anonymization, regridding, graph encoding, and more), and built-in readiness assessment and validation modes. REDI has been evaluated across climate, proteomics, materials science, and nuclear fusion datasets, demonstrating near-ideal parallel scaling to 100 nodes on OLCF's Frontier system. REDI is deployable as an agent-callable skill in coding environments such as Claude Code and OpenAI Codex, and is complemented by SetGo for FAIR compliance and catalog publication.

Brewer, Wesley [Oak Ridge National Laboratory (ORN↗

DOE OSTI2025

Redis-Based Streaming Architecture for Accelerator Beam Instrumentation DAQ Systems

The Fermilab Acceleraor Division, Beam Instrumentation Department, is always adopting modern and current software methodologies for complex DAQ architectures. This paper highlights the Redis Adapter (RA) as the key software component enabling high performance, modular communication between digitizers and distributed control systems by leveraging Redis and containerization. The RA provides a unified, efficient interface between Redis based data streams and consumer systems. In the legacy architecture, digitized data flowed through the custom, UDP based Distributed Data Communication Protocol in the middle layer. In the current system, DDCP remains the ingestion path, while the RA serves as the decoupling layer. The proposed system replaces old VME digitizers with a SOM-based digitizer that communicates with Redis using the RA. The RA acts as both a performance-critical bridge and a protocol-agnostic adapter, ensuring compatibility with legacy control frameworks while enabling future scalability and modularity. This restructuring of the middle layer also helps the system achieve high throughput, reduce latency, and simplify the data path. Finally, we will demonstrate how RA is utilized in our two core products to deliver both legacy compatibility and future flexibility.

Joshi, S. [Fermilab]↗

DOE OSTI2026

redis-pvxs-ioc

redis-pvxs-ioc is a standalone PVAccess service for hot-reloadable, Redis-backed EPICS process variables (PV) defined by YAML. The core runtime is PVA-only. It does not load EPICS databases, call iocInit(), or serve Channel Access.

Steinkamp, Derek [Fermi National Accelerator Labor↗

DOE OSTI2026

redis-tui

A terminal UI client for Redis inspired by Redis Insight, built with Rust and ratatui.

Santucci, Bobby [Fermi National Accelerator Labora↗

DOE OSTI2025

Control-Agnostic Beam Instrumentation with Redis at the Core

Redis isn’t a database — it’s our protocol. Fermilab’s RedisAdapter provides a high-performance, control-system-agnostic bridge between digitized beam data and downstream consumers such as ACNET and EPICS. It forms the foundation of three new software components deployed across MicroTCA-based digitizers: GMMDM, a runtime for memory-mapped data movement from Zynq-based platforms; GRAFE, a front end for Redis-to-ACNET presentation; and GREFE, an EPICS IOC front end. Together, these tools enable modular, standardized instrumentation pipelines. Precision timing is handled via White Rabbit PPS distribution, allowing nanosecond-scale synchronization across crates. This architecture, originally prototyped in Booster BPM systems, is now deployed on modern hardware and designed to meet the performance, modularity, and scalability requirements of the PIP-II era.

Steinkamp, Derek [Fermilab] (ORCID:000900027228626↗

DOE OSTI2025

Toroid Signal Processing Container with Redis Integration

This report presents the research, design, and implementation of a Toroid Signal Processing Container with Redis Integration, conducted during my internship assignment. The project focused on developing a modular signal processing sys- tem capable of performing baseline correction, droop compensation, and real- time data communication for beam diagnostic signals. Using Redis as a message broker and configuration manager, the system ensures modularity, scalability, and efficient inter-process communication. Signal correction algorithms, such as Asymmetric Least Squares (ALS) baseline smoothing and a numerical droop correction approach, were implemented and tested using Linac beam data. The results confirm that this system improves signal fidelity and supports real-time analysis requirements, offering valuable contributions to the field of beam instrumentation and data acquisition systems.

Bowers, Elliot [Cabrillo Coll.]↗

DOE OSTI2025

Toroid Signal Processing Container with Redis Integration

Bowers, Elliot [Cabrillo Coll.; Fermilab]↗

DOE OSTI2024

Renewable Energy Discovery (REDi) Island

This poster explores the Renewable Energy Discovery (REDi) Island, a web-based educational application of a virtual world powered entirely by renewable energy.

marine energy↗

DOE OSTI2025

REDIS-BASED STREAMING ARCHITECTURE FOR ACCELERATOR BEAM INSTRUMENTATION DAQ SYSTEMS

Explore the source record for details and available documents.

Joshi, Shreya [Fermilab]↗

DOE OSTI2024

Massively scalable workflows for quantum chemistry: BigChem and ChemCloud

Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. Here we present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Strong and Efficient Consistency with Consistency-aware Durability

We introduce consistency-aware durability or C ad , a new approach to durability in distributed storage that enables strong consistency while delivering high performance. We demonstrate the efficacy of this approach by designing cross-client monotonic reads , a novel and strong consistency property that provides monotonic reads across failures and sessions in leader-based systems; such a property can be particularly beneficial in geo-distributed and edge-computing scenarios. We build O rca , a modified version of ZooKeeper that implements C ad and cross-client monotonic reads. We experimentally show that O rca provides strong consistency while closely matching the performance of weakly consistent ZooKeeper. Compared to strongly consistent ZooKeeper, O rca provides significantly higher throughput (1.8--3.3×) and notably reduces latency, sometimes by an order of magnitude in geo-distributed settings. We also implement C ad in Redis and show that the performance benefits are similar to that of C ad ’s implementation in ZooKeeper.

Computer Science↗

DOE OSTI2021

Can Applications Recover from fsync Failures?

We analyze how file systems and modern data-intensive applications react to fsync failures. First, we characterize how three Linux file systems (ext4, XFS, Btrfs) behave in the presence of failures. We find commonalities across file systems (pages are always marked clean, certain block writes always lead to unavailability) as well as differences (page content and failure reporting is varied). Next, we study how five widely used applications (PostgreSQL, LMDB, LevelDB, SQLite, Redis) handle fsync failures. Our findings show that although applications use many failure-handling strategies, none are sufficient: fsync failures can cause catastrophic outcomes such as data loss and corruption. Our findings have strong implications for the design of file systems and applications that intend to provide strong durability guarantees.

Computer Science↗

DOE OSTI2025

In-Transit Data Transport Strategies for Coupled AI-Simulation Workflow Patterns

Coupled AI-Simulation workflows are becoming the major workloads for HPC facilities, and their increasing complexity necessitates new tools for performance analysis and prototyping of new in-situ workflows. We present SimAI-Bench, a tool designed to both prototype and evaluate these coupled workflows. In this paper, we use SimAI-Bench to benchmark the data transport performance of two common patterns on the Aurora supercomputer: a one-to-one workflow with co-located simulation and AI training instances, and a many-to-one workflow where a single AI model is trained from an ensemble of simulations. For the one-to-one pattern, our analysis shows that node-local and DragonHPC data staging strategies provide excellent performance compared Redis and Lustre file system. For the many-to-one pattern, we find that data transport becomes a dominant bottleneck as the ensemble size grows. Our evaluation reveals that file system is the optimal solution among the tested strategies for the many-to-one pattern.

Tummalapalli, Harikrishna [Argonne National Labora↗

DOE OSTI2025

SlimIO: Lightweight I/O Path Design for Write Isolation in FDP-backed In-Memory Databases

In-Memory Databases (IMDBs) are widely used with HPC applications to manage transient data, often using snapshot-based persistence for backups. Redis, a representative IMDB, employs both snapshot and Write-Ahead Log (WAL) mechanisms, storing data on persistent devices via the traditional kernel I/O path. This method incurs syscall overhead, I/O contention between processes, and SSD garbage collection (GC) delays. To address these issues, we propose SlimIO, which adopts I/O passthru to minimize syscall overhead and inter-process I/O interference. Additionally, it leverages Flexible Data Placement (FDP) SSDs as backup storage to avoid performance degradation from SSD GC. Experimental results show that SlimIO reduces snapshot time by up to 25%, increases query throughput by up to 30% during non-snapshot periods, and lowers 99.9%-ile latency by up to 50%. Furthermore, it achieves a write amplification factor (WAF) of 1.00, indicating no redundant internal writes, thus extending SSD lifespan.

Lee, Sangyun [Sogang University]↗

DOE OSTI2026

SetGo: Metadata Readiness for Scientific AI Datasets

Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse. The Readiness Engine for Data Integration (REDI) addresses computational readiness, but no corresponding tool evaluates whether a dataset’s metadata are sufficiently complete, governed, and standards-compliant for publication and agent-based consumption. Existing FAIR assessors operate only on published repository records, and no single system covers FAIR compliance, licensing, provenance, governance, reproducibility, and catalog readiness together. We present SetGo, an open-source Python toolkit that assesses and repairs metadata readiness across these six dimensions before a dataset is published or archived. Applied to four scientific corpora, SetGo surfaces deficiencies that general-purpose tools do not detect: ERA5 climate metadata scores 4% on ACDD 1.3 compliance; materials datasets fail OPTIMADE species-definition requirements; and PDB-derived proteomics data carries licensing terms incompatible with standard SPDX identifiers. Guided enrichment raises overall FAIR scores from 52–57% to 81–91%, and a single setgo publish command pushes to Hugging Face Hub, CKAN, or OpenMetadata with ML Commons Croissant 1.0 metadata sidecars. To support interactive and automated workflows, SetGo integrates with coding agents powered by large language models (LLMs) through a /setgo skill that enables natural-language execution of the full assess–enrich–publish loop, with user involvement limited to supplying missing metadata values.

Wilkinson, Sean [ORNL] (ORCID:0000000214437479)↗

DOE OSTI2024

Arroyo Stream Processing Toolset (arroyopy) v0.1.0

Processing event or streaming data presents several technological challenges. A variety of technologies are often used by scientific user facilities. ZMQ is used to stream data and messages in a peer-to-peer fashion. Message brokers like Kafka, Redis Pubsub, EPICS PVA and RabbitMQ are often employed to route and pass messages from instruments to processing workflows. Arroyopy provides an API and structure to flexibly integrate with these tools and incorporate arbitrarily complex processing workflows, letting the hooks to the workflow code be independent of the connection code and hence reusable at a variety of instruments.

Chavez Esparza, Tanny Andrea [Lawrence Berkeley Na↗

DOE OSTI2026

tbt-monitor

Platform for synchrotron BPM turn-by-turn (TbT) tune analysis across multiple Redis digitizers.

Steinkamp, Derek [Fermi National Accelerator Labor↗

DOE OSTI2026

epics-alarm-push

Prototype IOC alarm pusher using Redis in a support module.

Bracey, Dave [Fermi National Accelerator Laborator↗