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This presentation details the design and use of a rudimentary example API. The example API reads JSON; eventually it needs to operate with the database. It demonstrates ease in applying powerful, publicly available tools.
The Sanibel Symposium series is renowned amongst materials theorists, quantum chemists, and condensed matter physicists as meetings driving progress on theory, mod eling, and simulation of materials and their molecular and nano-scale constituents. The Symposia are highly unusual (perhaps unique) in their priority emphasis on theory and computation, in having no parallel sessions, in cultivating well-attended Hot-Topic contributed oral sessions, and accessible poster sessions. These provide highly visible, influential platforms for cross-fertilization among specialist investigators, hence are strong contributors to the advance of quantum information sciences (QIS) research of strategic importance to the Office of Basic Energy Sciences (BES). As part of a five-year plan to highlight QIS challenges and opportunities and foster progress on them, each of the pre ceding three Sanibel Symposia had a thematic focus, Quantum Theory, Quantum Materi als, Quantum Computing, as a major program component. Emphasis was on quantum materials and their molecular constituents. The award for 2024 was for year four of that sustained thematic focus.
SYCL is a portable programming model for heterogeneous computing, so it is important to obtain reasonable performance portability of SYCL. Towards the goal of better understanding and improving performance portability of SYCL for machine learning workloads, we have been developing benchmarks for basic operators in deep neural networks (DNNs). These operators could be offloaded to heterogeneous computing devices such as graphics processing units (GPUs) to speed up computation. In this work, we introduce the benchmarks, evaluate the performance of the operators on GPU-based systems, and describe the causes of the performance gap between the SYCL and Compute Unified Device Architecture (CUDA) kernels. We find that the causes are related to the utilization of the texture cache for read-only data, optimization of the memory accesses with strength reduction, shared local memory accesses, and register usage per thread. We hope that the efforts of developing benchmarks for studying performance portability will stimulate discussion and interactions within the community.
This project increased research capacity in Southern Appalachian wetlands by facilitating new collaborations between the PI (UNC Asheville) and DOE and academic research partners. Mountain wetlands are among the rarest natural communities in the Southern Appalachians; most of the sites contain rare or endangered species and have been incorporated into or are being considered for the new Mountain Bogs National Wildlife Refuge (established in 2015). PI Wilcox’s research since 2010 has focused on wetland hydrology at over a dozen priority field sites in western North Carolina, eastern Tennessee, and northern Georgia. This project provided time and funding to learn about DOE programs and resources, including meteorological data, watershed modeling programs, analytical capabilities, and computing facilities. PI Wilcox was able to meet potential research partners at professional conferences and the DOE-BER-ESS PI meeting and invite several of those potential partners to visit UNC Asheville and nearby field sites in Western North Carolina. These connections have resulted in three new collaborative research proposals, and a new cooperative agreement with the US Fish and Wildlife Service that includes two new field sites.
I have been working on something called clutter, which is an amount of “noise” caused by the surrounding environment in a radar system photo or image. The radar system is called “synthetic aperture radar” or SAR. Our group is trying to clarify the photo and hence data of an SAR image by removing clutter (and noise) from the photograph. This is done by utilizing advanced signal processing techniques and the MATLAB programming language on our computer.
Software models that use design-level input variables and physics-based engineering analysis for estimating the mass and geometrical properties of components in large-scale machinery can be very useful for analyzing design trade-offs in complex systems. This study uses DriveSE, an OpenMDAO-based drivetrain model that uses stress and deflection criteria to size drivetrain components within a geared, upwind wind turbine. Because a full lifetime fatigue load spectrum can only be defined using computationally-expensive simulations in programs such as FAST, a parameterized fatigue loads spectrum that depends on wind conditions, rotor diameter, and turbine design life has been implemented. The parameterized fatigue spectrum is only used in this paper to demonstrate the proposed fatigue analysis approach. This paper details a three-part investigation of the parameterized approach and a comparison of the DriveSE model with and without fatigue analysis on the main shaft system. It compares loads from three turbines of varying size and determines if and when fatigue governs drivetrain sizing compared to extreme load-driven design. It also investigates the model's sensitivity to shaft material parameters. The intent of this paper is to demonstrate how fatigue considerations in addition to extreme loads can be brought into a system engineering optimization.
The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy for the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the previous European Strategy for Particle Physics. The construction of DUNE Phase I is well underway. DUNE Phase II consists of a third and fourth far detector module, an upgraded near detector complex, and an enhanced > 2 MW beam. The fourth FD module is conceived as a 'Module of Opportunity', aimed at supporting the core DUNE science program while also expanding the physics opportunities with more advanced technologies. The DUNE collaboration is submitting four main contributions to the 2026 Update of the European Strategy for Particle Physics process. This submission to the 'Detector instrumentation' stream focuses on technologies and R&D for the DUNE Phase II detectors. Additional inputs related to the DUNE science program, DUNE software and computing, and European contributions to Fermilab accelerator upgrades and facilities for the DUNE experiment, are also being submitted to other streams.
The LANL Geodynamics Team has been applying Abaqus modeling to achieve increasingly complex simulations. Advancements in Abaqus model building and simulation tools allows this progress. We use Lab-developed constitutive models, the fully coupled CEL Abaqus and general contact to simulate response of realistic sites to explosively driven shock.
We provide a set of prescriptions for implementing a quantum circuit model algorithm as measurement based quantum computing (MBQC) algorithm1, 2 via a large cluster state. As means of illustration we draw upon our numerical modeling experience to describe a large graph state capable of searching a logical 8 element list (a non-trivial version of Grover's algorithm3 with feedforward). We develop several prescriptions based on analytic evaluation of cluster states and graph state equations which can be generalized into any circuit model operations. Such a resulting cluster state will be able to carry out the desired operation with appropriate measurements and feed forward error correction. We also discuss the physical implementation and the analysis of the principal 3-qubit entangling gate (Toffoli) required for a non-trivial feedforward realization of an 8-element Grover search algorithm.
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Microreactor Research, Development and Demonstrations at Idaho National Laboratory presentation for the RIC conference, covering the vision for advanced reactor demonstrations and deployment, NRIC, DOE Microreactor Program, GAIN, and Nuclear Computational Resources Center.
The U.S. Department of Energy's Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has been developing computational analysis tools for nuclear fuel simulation. These codes include BISON and Marmot for investigation of fuel behavior at the continuum and microstructural scales, respectively. In 2014, NEAMS introduced what it calls High Impact Problems (HIPs) into its development plan. These HIPs are intended to make significant advancements in a particular area of nuclear power research in a short period of time (3 years or less). NEAMS has chosen an accident tolerant fuel (ATF) project, which emphasizes utilizing BISON and Marmot to model proposed materials, as its first HIP. Since the events that occurred at the Fukushima-Daiichi nuclear power plant in 2011, the U.S. Department of Energy's Office of Nuclear Energy accelerated research in nuclear fuel concepts with enhanced accident tolerance as part of its Fuel Cycle Research and Development (FCRD) Advanced Fuels Campaign (AFC). Accident tolerant fuels are expected to perform at least as well as conventional fuel during normal conditions and provide significantly increased response time in the event of an accident. Given the many unknowns associated with proposed ATF concepts, the research and development being undertaken by AFC in this area is a challenge, particularly considering the desire to have a lead test rod or assembly in a commercial plant by 2022. With the AFC as its primary stakeholder, the NEAMS ATF HIP is developing material models (using a multi-scale approach) for multiple candidate materials. Through a variety of analyses employing these models, the ATF HIP is providing insight and support to the AFC as it strives to understand these materials and prioritize further development. Use of computational tools such as BISON and Marmot are a vital component of the AFC's strategy since experimental data will not be available broadly and quickly enough to understand the proposed materials fully by the time the AFC must make further development decisions. This summary reviews the overall approach of the NEAMS ATF HIP. First, we give an overview of the current materials chosen for investigation. We also review the overall multi-scale approach to model development. We briefly review the work underway at multiple national laboratories to develop mechanistic engineering scale models based on lower length scale insight. Note that coordinated work is also underway at multiple universities, but those efforts are not detailed here. The NEAMS ATF HIP is assisting the AFC in assessing two accident tolerant materials, uranium silicide fuel and FeCrAl cladding. A multi-scale material model development approach is being undertaken to formulate the models necessary to investigate the materials over a range of normal and off-normal scenarios. This approach includes molecular dynamics, density functional theory, mesoscale modeling, and engineering scale modeling. Sensitivity studies are underway to understand the most critical scenarios and material model input parameters. The information generated is being given to the AFC for their use in selecting promising ATF concepts. (authors)
The Regional Energy Deployment System (ReEDS) is an open-source, spatially explicit, long-term capacity expansion model for the bulk electric power system of the contiguous United States, encompassing multiple scenarios with technological and political assumptions (see https://github.com/NREL/ReEDS-2.0). With the increased needs for capabilities, higher temporal and spatial resolutions to model the evolution of the power system with modern technologies and low-carbon pathways, ReEDS' model solution times have increased significantly from 4-6 hours in 2018 to 18-48+ hours in 2023 . Also, the model size for commonly-run ReEDS scenarios reached 22 and 28 million equations and variables, respectively. These runtimes can be especially challenging under certain scenario settings (e.g., very high temporal or spatial resolution) or with limited computational power. In this presentation, we will discuss several methods we used to improve model runtime, including data preparation, model modification, and solver tuning. The implementation of these methods shrank the model size to 7.2 and 7.3 million equations and variables, respectively. Furthermore, this led to a 77% reduction in the model's run time for commonly-run ReEDS scenarios. We will discuss the process of identifying areas for solve time improvements and how the specific enhancements for the ReEDS model might be applied to other similar large-scale models.
As both CPU and GPU become employed in a wide range of applications, it has been acknowledged that both of these processing units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this paper, we survey heterogeneous computing techniques (HCTs) such as workload-partitioning which enable utilizing both CPU and GPU to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler and application level. Further, we review both discrete and fused CPU-GPU systems; and discuss benchmark suites designed for evaluating heterogeneous computing systems (HCSs). Furthermore, we believe that this paper will provide insights into working and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.
The mission of the Computing Research Association’s Committee on Widening Participation in Computing Research (CRA-WP) is to widen the participation and improve the access, opportunities, and positive experiences of individuals from groups underrepresented in computing research and education. CRA-WP programs serve this overarching goal at all career stages; in addition, CRA-WP, through the formation of the CRA Center for Evaluating the Research Pipeline (CERP), has developed a methodology for thoroughly evaluating the success of its programs by comparing a nationwide sample of students, researchers, and faculty (non-participants) to program participants. Achieving these objectives requires that an increasing number of individuals from populations underrepresented in computing start and progress to the next stage while understanding and supporting the myriad computing pathways. CRA-WP offers programs for participants from undergraduate to senior professional levels. Different career stages need different types of interventions, and the goal of all Alliance program activities can be described within the unifying framework of Social Cognitive Career Theory, which finds that interest in and choice of a particular career path will be increased by interventions that improve one or more of the following: (1) outcome expectations (understanding and valuing the rewards of a particular outcome), (2) self-efficacy (a belief that one can successfully achieve an outcome), and (3) social supports that help one persist and overcome obstacles.
This work bridges the gap between multi-GPU computing and high-productivity, performance-portable programming solutions. Our goal is to enhance scientific applications with a productive and portable solution—program once, deploy everywhere—for multi-GPU programming with no cost to programmability. To accomplish this, we implemented JACC.Multi, which is part of the Julia for ACCelerators (JACC) performance-portable framework. JACC. Multi is the only high-level, portable metaprogramming solution that targets multi-GPU environments and is integrated in a readily accessible programming language (e.g., Julia language). With transparent GPU-to-GPU communication, JACC. Multi is optimized for scientific application workloads and is portable for NVIDIA and AMD accelerators. For the evaluation, we use two modern multi-GPU systems: Hudson, which features two NVIDIA H100 Hopper GPUs per node, and Frontier, which features four AMD MI250X GPUs per node, each with two Graphics Compute Dies (GCDs) for a total of eight GCDs per node. Additionally, as part of the evaluation, we use JACC (one GPU), MPI+JACC, and JACC. Multi codes that implement well-known and widely used scientific algorithms/kernels such as the conjugate gradient algorithm and an explicit forward Euler solver that requires GPU-to-GPU communication. Overall, JACC. Multi codes achieve better performance than MPI+JACC codes and significant speedups over JACC (one GPU), with up to 1.9× on Hudson and 6× on Frontier.
On the path to exascale the landscape of computer device architectures and corresponding programming models has become much more diverse. While various low-level performance portable programming models are available, support at the application level lacks behind. To address this issue, we present the performance portable block-structured adaptive mesh refinement (AMR) framework Parthenon, derived from the well-tested and widely used Athena++ astrophysical magnetohydrodynamics code, but generalized to serve as the foundation for a variety of downstream multi-physics codes. Parthenon adopts the Kokkos programming model, and provides various levels of abstractions from multidimensional variables, to packages defining and separating components, to launching of parallel compute kernels. Parthenon allocates all data in device memory to reduce data movement, supports the logical packing of variables and mesh blocks to reduce kernel launch overhead, and employs one-sided, asynchronous MPI calls to reduce communication overhead in multi-node simulations. Using a hydrodynamics miniapp, we demonstrate weak and strong scaling on various architectures including AMD and NVIDIA GPUs, Intel and AMD x86 CPUs, IBM Power9 CPUs, as well as Fujitsu A64FX CPUs. At the largest scale on Frontier (the first TOP500 exascale machine), the miniapp reaches a total of 1.7 × 10 13 zone-cycles/s on 9216 nodes (73,728 logical GPUs) at [Formula: see text] weak scaling parallel efficiency (starting from a single node). In combination with being an open, collaborative project, this makes Parthenon an ideal framework to target exascale simulations in which the downstream developers can focus on their specific application rather than on the complexity of handling massively-parallel, device-accelerated AMR.
PSI4 is a free and open-source ab initio electronic structure program providing implementations of Hartree–Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient, thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of PSI4 's core functionalities via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSCHEMA data format, facilitating interoperability. We announce a rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCARCHIVE INFRASTRUCTURE project, makes the latest version of PSI4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs.