Computing Sciences

Understanding the appropriate ways cyberinfrastructure can be designed, implemented, and executed for scientific use cases requires a deep understanding of the way that researchers and educators interact with technology, and how it may be best implemented to suit their needs. The Engagement and Performance Operations Center (EPOC) has conducted a series of scientific “Deep Dives” of use cases at partner institutions to better understand the requirements for modern scientific innovation across the United States research complex. The results of these activities have revealed gaps in the way that technology has been used to foster research activities. This gap in cyberinfrastructure support has impacts for the overall productivity and innovation possibilities for scientific users.

Streaming data is data that is emitted at variable volumes in a continuous, incremental manner with the goal of low-latency processing often at a different physical location. Network infrastructure is used to facilitate the connection between data sources and sinks, and must be robust to handle the requirements of the workflow. The U.S. Department of Energy Office of Science (DOE SC) a federal agency supporting fundamental scientific research for energy and the Nation’s largest supporter of basic research in the physical sciences. DOE SC has the responsibility for operating $\mathbf{1 0}$ National Laboratories, and 28 scientific user facilities supporting advanced supercomputers, particle accelerators, large x-ray light sources, neutron scattering sources, and other specialized facilities for nanoscience and genomics. This paper investigates the state of streaming data workfows, and details some of the approaches to this challenging problem.

This article proposes an object layout regenerator called Regen which regenerates and removes the object layout dynamically to improve the read performance of applications. Regen first detects frequent access patterns from the I/O requests of the applications. Second, Regen reorganizes the objects and regenerates or preallocates new object layouts according to the identified access patterns. Finally, Regen removes or reuses the obsolete or regenerated object layouts as necessary. As a result, Regen accelerates access to objects by providing a flexible object layout. We implement Regen as a framework on top of Proactive Data Container (PDC) and evaluate it on Cori supercomputer, a production-scale HPC system, by using realistic HPC I/O benchmarks. The experimental results show that Regen improves the I/O performance by up to 16.92 × compared with an existing system.

The world-class research infrastructure at the US Department of Energy (DOE) Office of Science (SC) provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance the core DOE mission in science and technology for its SC program to stimulate rich scientific discoveries and enhance its innovation ecosystem. Research communities gather and flourish around each user facility, bringing together new and enhanced perspectives. The continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by the SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high-performance data, networking, and computing solutions. The ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements review methodology. Between July and December 2024, the Energy Sciences Network (ESnet) and the Office of High Energy Physics (HEP) of the DOE-SC organized an ESnet requirements review of HEP-supported program activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

As quantum networking experiments move from laboratory experiments to larger scale deployments, integrated control software becomes essential for managing complex interactions between the numerous distributed resources involved. While a number of laboratory-scale control systems have been developed for specific quantum platform demonstrations, an openly available and general solution for operating quantum networks has not emerged. With the QUANT-NET Control Plane (QNCP), we introduce a model-based, extensible control plane implementation that offers a framework for enabling network-wide orchestration in quantum information network environments. QCNP provides a quantum network data model, resource management, communication primitives, and a plugin interface for defining orchestration and protocol interactions across distributed quantum network devices and services. This paper describes the design and architecture of QNCP, its implementation, and opportunities for extensibility and deployment.

In February 2025, the DOE user facility Energy Sciences Network (ESnet) held a three-day Data and AI Workshop in Berkeley, California. The objective of the workshop was to identify challenges within ESnet that could be addressed through data-driven methods, to help define ESnet’s data-analysis requirements, and to shape its AI strategy, guiding data-stewardship efforts and the direction of AI research and AIOps exploration for ESnet7, the next iteration of ESnet’s network. This report summarizes the multi-faceted discussions and findings and presents a set of recommendations for next steps.

Atom-cavity systems offer unique advantages for building large-scale distributed quantum computers by providing strong atom-photon coupling while allowing for high-fidelity local operations of atomic qubits. However, in prevalent schemes where the photonic state is encoded in polarization, cavity birefringence introduces an energy splitting of the cavity eigenmodes and alters the polarization states, thus limiting the fidelity of remote entanglement generation. To address this challenge, we propose a scheme that encodes the photonic qubit in the frequency degree of freedom. The scheme relies on resonant coupling of multiple transverse cavity modes to different atomic transitions that are well separated in frequency. We numerically investigate the temporal properties of the photonic wave packet, two-photon interference visibility, and atom-atom entanglement fidelity under various cavity polarization-mode splittings and find that our scheme is less affected by cavity birefringence. Finally, we propose practical implementations in two trapped ion systems, using the fine-structure splitting in the metastable D state of 40Ca+ and the hyperfine splitting in the ground state of 225Ra+. Our study presents an alternative approach for cavity-based quantum networks that is less sensitive to birefringent effects and is applicable to a variety of atomic and solid-state emitter-cavity interfaces.

Quantum computers are evolving at a rapid pace and are considered next-generation computers with high computational capabilities. However, due to the unique characteristics of qubits, state-of-the-art quantum computers are vulnerable to noise caused by qubit instability. To overcome this, highperformance computing (HPC) systems are utilized for quantum circuit simulations to evaluate complex quantum algorithms with great accuracy. However, quantum circuit simulations have high computational demands, and the data volume increases exponentially as the number of qubits increases. In this paper, we propose SWIFTN, a quantum circuit simulation optimization framework for HPC systems with scalability. To achieve this, it enhances parallelism by dividing the tensor networks and distributing them across multiple GPUs and nodes. Additionally, it reduces computational costs by bypassing tasks through intermittent tensor contraction. Finally, to mitigate the degradation in accuracy due to intermittent tensor contraction,SWIFTNperforms amplitude adjustments. We implement and evaluateSWIFTNusing a Perlmutter supercomputer. Our evaluation results using popular quantum algorithm benchmark (i.e., QAOA) shows thatSWIFTNcan improve the performance by $7.85 \times$ with 99.997 % accuracy.

Monitoring data transfer performance is a crucial task in scientific computing networks. By predicting performance early in the communication phase, potentially sluggish transfers can be identified and selectively monitored, optimizing network usage and overall performance. A key bottleneck to improving the predictive power of machine learning (ML) models in this context is the issue of class imbalance. This project focuses on addressing the class imbalance problem to enhance the accuracy of performance predictions. In this study, we analyze and compare various augmentation strategies, including traditional oversampling methods and generative techniques. Additionally, we adjust the class imbalance ratios in training datasets to evaluate their impact on model performance. While augmentation may improve performance, as the imbalance ratio increases, the performance does not significantly improve. We conclude that even the most advanced technique, such as CTGAN, does not significantly improve over simple stratified sampling.

Efficient data transfer across scientific computing facilities is critical for enabling timely scientific discoveries. In this work, we explore the options of anticipating extremely slow data transfers to enable preventive actions. However, the dynamic nature of the large distributed scientific workflows driving these data transfers presents significant challenges for predicting network throughput. This study introduces a Conditional Recurrent Neural Network (CondRNN) model, specifically utilizing Conditional Long Short-Term Memory (CondLSTM), to integrate both static and dynamic features for enhanced throughput prediction. By leveraging historical transfers as proxy features, more than 60% of predictions achieved an absolute percentage error (APE) of less than 20%, and slow transfers were detected with a precision of 91.7% and recall of 100%, outperforming traditional RNN models. Implementing CondLSTM in scientific computing environments can optimize network resource utilization, ensuring efficient data transmission, thereby supporting the continuous progression of scientific research.