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Wafer-scale TaOx device variability and implications for neuromorphic computing applications.
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A Perspective on Quantum Computing Applications in Quantum Chemistry Using 25-100 Logical Qubits
The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating quantum systems, quantum chemistry has long been recognized as a natural candidate for quantum computation. This perspective focuses on identifying scientifically meaningful use cases where early fault-tolerant quantum computers, which are considered to be equipped with approximately 25-100 logical qubits, could deliver tangible impact. While recent advances in classical computing have pushed the boundaries of tractable simulations to unprecedented scales, this logical-qubit regime represents the first window where quantum devices can pursue qualitatively distinct strategies, such as polynomial-scaling phase estimation, direct simulation of quantum dynamics, and active-space embedding, that remain challenging for classical solvers, such as multireference charge-transfer and conical-intersection states central to photochemistry and materials design. We highlight near-term opportunities in algorithm and software design, discuss representative chemical problems suited for quantum acceleration, and propose strategic roadmaps and collaborative pathways for advancing practical quantum utility in quantum chemistry.
Toward a persistent event-streaming system for high-performance computing applications
High-performance computing (HPC) applications have traditionally relied on parallel file systems and file transfer services to manage data movement and storage. Alternative approaches have been proposed that use direct communications between application components, trading persistence and fault tolerance for speed. Event-driven architectures, as popularized in enterprise contexts, present a compelling middle ground, avoiding the performance cost and API constraints of parallel file systems while retaining persistence and offering impedance matching between application components. However, adapting streaming frameworks to HPC workloads requires addressing challenges unique to HPC systems. This paper investigates the potential for a streaming framework designed for HPC infrastructures and use cases. We introduce Mofka, a persistent event-streaming framework designed specifically for HPC environments. Mofka combines the capabilities of a traditional streaming service with optimizations tailored to the HPC context, such as support for massively multicore nodes, efficient scaling for large producer-consumer workflows, RDMA-enabled high-performance network communications, specialized network fabrics with multiple links per node, and efficient handling of large scientific data payloads. Built using the Mochi suite of HPC data service components, Mofka provides a lightweight, modular, and high-performance solution for persistent streaming in HPC systems. We present the architecture of Mofka and evaluate its performance against Kafka and Redpanda using benchmarks on diverse platforms, including Argonne's Polaris and Oak Ridge's Frontier supercomputers, showing up to 8× improvement in throughput in some scenarios. We then demonstrate its utility in several real-world applications: a tomographic reconstruction pipeline, a workflow for the discovery of metal-organic frameworks for carbon capture, and the instrumentation of Dask workflows for provenance tracking and performance analysis.
The Applicability of Unit Systems to High-Performance Computing Applications
Dimensional analysis is a key technique used to verify the soundness of scientific models. Most experts agree that engineering and scientific software would be made more reliable by integrating dimensional analysis in their type system. We explored how High Performance Computing (HPC) applications could integrate compile-time dimensional analysis. We started by investigating various implementation of unit systems for C++. Eventually, selecting the latest (and most advanced) one to apply to our test codes. We worked with code of increasing complexity, from a projectile trajectory calculation to the proxy-application Lulesh. This included our code, Springs-3D, which focuses on demonstrating language features while performing simple physic computations. Finally, our main contribution is a source-code analysis which extracts constraints on the dimension of all variables, functions, and constants in an application. This resulting system of equations is solved using the dimensions of a few of these objects. This analysis has the potential to greatly reduce the time spent performing dimensional analysis when refactoring application to use a representation of units.
Auto-HPCnet: An Automatic Framework to Build Neural Network-based Surrogate for High-Performance Computing Applications
High-performance computing communities are increasingly adopt- ing Neural Networks (NN) as surrogate models in their applications to generate scientific insights. Replacing an execution phase in the application with NN models can bring significant performance im- provement. However, there is a lack of tools that can help domain scientists automatically apply NN-based surrogate models to HPC applications. We introduce a framework, named Auto-HPCnet, to democratize the usage of NN-based surrogates. Auto-HPCnet is the first end-to-end framework that makes past proposals for the NN-based surrogate model practical and disciplined. Auto-HPCnet introduces a workflow to address unique challenges when apply- ing the approximation, such as feature acquisition and meeting the application-specific constraint on the quality of final computation outcome. We show that Auto-HPCnet can leverage NN for a set of HPC applications and achieve 5.50× speedup on average (up to 16.8× speedup and with data preparation cost included) while meeting the application-specific constraint on the final computation quality.
Fundamental Superconductivity of Nb films for Quantum Computing Application
Niobium is a widely accepted material for quantum computing device as well as superconducting radio frequency (SRF) technology. Superconducting niobium is a marginal type II superconductor which has a very narrow gap (~20-30 mT) of the mixed state at 2K, even showing the intermediate state (IMS) at the early stage of magnetic vortex penetration. Tremendous progress has been made in understanding the impact of the Nb surface and bulk superconductivities on SRF resonator performance. However, the effect of thin superconducting Nb films for quantum computing applications requires further examination. In this study, we explore the fundamental superconducting properties of various thin Nb films and compare them with respect to the energy relaxation time, T1, measured from superconducting qubit fabricated with these films. The Nb films are fabricated both with or without a surface protective layer that prevents the formation of lossy Nb2O5. Electromagnetic properties are characterized by means of bulk magnetization, electromagnetic transport, and dynamics of surface superconductivity. In addition, analytical electron microscopy is implemented to further connect the superconducting properties to microstructure.
Fundamental Superconductivity of Nb films for Quantum Computing Application
Niobium is a widely accepted material for quantum computing device as well as superconducting radio frequency (SRF) technology. Superconducting niobium is a marginal type II superconductor which has a very narrow gap (~20-30 mT) of the mixed state at 2K, even showing the intermediate state (IMS) at the early stage of magnetic vortex penetration. Tremendous progress has been made in understanding the impact of the Nb surface and bulk superconductivities on SRF resonator performance. However, the effect of thin superconducting Nb films for quantum computing applications requires further examination. In this study, we explore the fundamental superconducting properties of various thin Nb films and compare them with respect to the energy relaxation time, T1, measured from superconducting qubit fabricated with these films. The Nb films are fabricated both with or without a surface protective layer that prevents the formation of lossy Nb2O5. Electromagnetic properties are characterized by means of bulk magnetization, electromagnetic transport, and dynamics of surface superconductivity. In addition, analytical electron microscopy is implemented to further connect the superconducting properties to microstructure.
Minimizing Error Potential Using Computer Applications - 18498
Many Savannah River Site (SRS) liquid waste facility operations depend upon repetitive engineering calculations and operator actions. Although these calculations and actions are formalized in technical reports and paper procedures, they can still be vulnerable to data entry errors, calculation errors, equipment alignment errors, and occasional changes caused by different people performing these activities. Savannah River Remediation (SRR) is using computer automation to reduce these error precursors by standardizing repetitive calculations and by performing plant alignment checks for repetitive operation processes. Data entry can be automated, calculation methodologies can be standardized, and computers can check to see that predefined plant conditions exist before equipment operation. In addition to improving quality, the time required for these activities to generate technical reports, to perform independent reviews, and to perform equipment alignment checks can be significantly reduced. In the case of operator-performed calculations, error precursors involve distractions that can occur over a multi-page paper procedure, confusing displays and controls from equipment, mathematical errors, complacency, and unfamiliarity with the procedure among many others. Repetitive engineering calculations suffer from similar error precursors and are often compounded by high workloads, lack of proficiency, lack of standard methodology, mental shortcuts, and biases. Web-based computer applications can eliminate many of these precursors through standardization. Calculation methods are fixed, inputs are fixed, and computer checks can highlight potentially wrong inputs and outputs. Automated calculations are performed almost instantaneously once input is provided. Operators and engineers can spend more time focusing on the results than on the process to achieve the results. Similarly, when operators use repetitive procedures involving equipment manipulation, the same error precursors apply. Automated operation sequences and equipment checks can be programmed into the distributed control system to provide standard operating processes that reduce and eliminate error precursors. This paper describes how SRR incorporates Human Performance Improvement (HPI) principles when automating calculations and operation processes. Computer applications will be reviewed in detail, highlighting the software development life-cycle that includes requirements gathering, prototyping, design, development, testing, and implementation. (authors)
High Performance Computing Application I/O Traces
The dataset comprises trace files from high performance computing (HPC) simulations. The trace files contain records of every I/O operation executed by a simulation application run, including I/O operations from HDF5, MPI-IO, and POSIX and all of the parameters supplied to those operations, e.g. file name, offset, and flags. The traces are generated by executing a simulation application that is linked with the Recorder tracing tool (https://github.com/uiuc-hpc/Recorder). The Recorder trace tool intercepts the I/O calls made by the application, records the I/O trace record, and then calls the intended I/O call so that the operation executes.
Autotuning in High-Performance Computing Applications
Autotuning refers to the automatic generation of a search space of possible implementations of a computation that are evaluated through models and/or empirical measurement to identify the most desirable implementation. Autotuning has the potential to dramatically improve the performance portability of petascale and exascale applications. To date, autotuning has been used primarily in high-performance applications through tunable libraries or previously tuned application code that is integrated directly into the application. This paper draws on the authors' extensive experience applying autotuning to high-performance applications, describing both successes and future challenges. If autotuning is to be widely used in the HPC community, researchers must address the software engineering challenges, manage configuration overheads, and continue to demonstrate significant performance gains and portability across architectures. In particular, tools that configure the application must be integrated into the application build process so that tuning can be reapplied as the application and target architectures evolve.
Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures
Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-ow and irregular memory accesses. Furthermore, these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-ow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-ow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps. In this dissertation, we present novel machine learning based optimization techniques to address the parallel implementation challenges of such irregular applications on different HPC architectures. In particular, we use supervised learning to predict the computation structure and use it to address the control-ow and memory access irregularities in the parallel implementation of such applications on GPUs, Xeon Phis, and heterogeneous architectures composed of multi-core CPUs with GPUs or Xeon Phis. We use numerical simulation of charged particles beam dynamics simulation as a motivating example throughout the dissertation to present our new approach, though they should be equally applicable to a wide range of irregular applications. The machine learning approach presented here use predictive analytics and forecasting techniques to adaptively model and track the irregular memory access pattern at each time step of the simulation to anticipate the future memory access pattern. Access pattern forecasts can then be used to formulate optimization decisions during application execution which improves the performance of the application at a future time step based on the observations from earlier time steps. In heterogeneous architectures, forecasts can also be used to improve the memory performance and resource utilization of all the processing units to deliver a good aggregate performance. We used these optimization techniques and anticipation strategy to design a cache-aware, memory efficient parallel algorithm to address the irregularities in the parallel implementation of charged particles beam dynamics simulation on different HPC architectures. Experimental result using a diverse mix of HPC architectures shows that our approach in using anticipation strategy is effective in maximizing data reuse, ensuring workload balance, minimizing branch and memory divergence, and in improving resource utilization.
A Visual Analytics Framework for the Detection of Anomalous Call Stack Trees in High Performance Computing Applications
Anomalous runtime behavior detection is one of the most important tasks for performance diagnosis in High Performance Computing (HPC). Most of the existing methods find anomalous executions based on the properties of individual functions, such as execution time. However, it is insufficient to identify abnormal behavior without taking into account the context of the executions, such as the invocations of children functions and the communications with other HPC nodes. We improve upon the existing anomaly detection approaches by utilizing the call stack structures of the executions, which record rich temporal and contextual information. With our call stack tree (CSTree) representation of the executions, we formulate the anomaly detection problem as finding anomalous tree structures in a call stack forest. The CSTrees are converted to vector representations using our proposed stack2vec embedding. Structural and temporal visualizations of CSTrees are provided to support users in the identification and verification of the anomalies during an active anomaly detection process. Furthermore, three case studies of real-world HPC applications demonstrate the capabilities of our approach.
Implementation and analysis of quantum computing application to Higgs boson reconstruction at the large Hadron Collider
With the advent of the High-Luminosity Large Hadron Collider (HL-LHC) era, high energy physics (HEP) event selection will require new approaches to rapidly and accurately analyze vast databases. The current study addresses the enormity of HEP databases in an unprecedented manner—a quantum search using Grover’s Algorithm (GA) on an unsorted database, ATLAS Open Data, from the ATLAS detector. A novel method to identify rare events at 13 TeV in CERN’s LHC using quantum computing (QC) is presented. As indicated by the Higgs boson decay channel H→ZZ*→4l , the detection of four leptons in one event may be used to reconstruct the Higgs boson and, more importantly, evince Higgs boson decay to some new phenomena, such as H→ZZ d →4l . Searching the dataset for collisions resulting in detection of four leptons using a Jupyter Notebook, a classical simulation of GA, and several quantum computers with multiple qubits, the current application was found to make the proper selection in the unsorted dataset. Quantum search efficacy was analyzed for the incoming HL-LHC by implementing the QC method on multiple classical simulators and IBM’s quantum computers with the IBM Qiskit Open Source Software. The current QC application provides a novel, high-efficiency alternative to classical database searches, demonstrating its potential utility as a rapid and increasingly accurate search method in HEP.
VnV: A Self Documenting Testing Framework for In-situ Verification and Validation in High Performance Computing Applications
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A large millikelvin platform at Fermilab for quantum computing applications
The need for larger mK cooling platforms is being driven by the desire to host ever growing numbers of cryogenic qubits in quantum computing platforms. As part of the Superconducting Quantum Materials and Systems Center at Fermilab funded through the Department of Energy under the National Quantum Initiative, we are developing a cryogenic platform capable of reaching millikelvin temperatures in an experimental volume of 2 meters diameter by approximately 1.5 meters in height. The platform is intended to host a three-dimensional qubit architecture based on superconducting radiofrequency accelerator cavity technologies. This paper describes the baseline design of the platform, along with the expected key performance parameters.
Revealing conductivity of p-type delta layer systems for novel computing applications
This project uses a quantum simulation technique to reveal the true conducting properties of novel atomic precision advanced manufacturing materials. With Moore's law approaching the limit of scaling for the CMOS technology, it is crucial to provide the best computing power and resources to National Security missions. Atomic precision advanced manufacturing-based computing systems can become the key to the design, use, and security of modern weapon systems, critical infrastructure, and communications. We will utilize the state-of-the-art computational methodology to create a predictive simulator for p-type atomic precision advanced manufacturing systems, which may also find applications in counterfeit detection and anti-tamper.
A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications
In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.