The Beamline 15 Computer Program
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Piccioni, J.↗
SEARCH · Engineering Papers
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DARMA: A Model for Next-gen Software Stacks in the Exascale Computing Program (ECP).
Abstract not provided.
Wilke, Jeremiah J↗
Systems, methods and computer program products for self-tuning sensor data processing
Systems and methods are disclosed that include tools that utilize Dynamic Detector Tuning (DDT) software that identifies near-optimal parameter settings for each sensor using a neuro-dynamic programming (reinforcement learning) paradigm. DDT adapts parameter values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The DDT algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest. The disclosed systems and methods reduce the number of missed legitimate detections and the number of false detections, resulting in improved event detection.
Draelos, Timothy J.↗
Systems, methods and computer program products for charging autonomous wireless sensors in subsurface environments
Autonomous wireless sensors in subsurface environments can be charged while present in the subsurface environment to allow the sensors to measure and wirelessly transmit measurements. The sensors rely upon a contrast agent to provide a power flow path to the sensors.
Heath, Jason E.↗
Computer program product for classifying materials
Systems and methods for classifying materials utilizing one or more sensor systems, which may implement a machine learning system in order to identify or classify each of the materials, which may then be sorted into separate groups based on such an identification or classification. The machine learning system may utilize a neural network, and be previously trained to recognize and classify certain types of materials.
Kumar, Nalin↗
Gene Expression Programming for Quantum Computing
Here, we introduce QuantumGEP, a scientific computer program that uses gene expression programming (GEP) to find a quantum circuit that either (1) maps a given set of input states to a given set of output states or (2) transforms a fixed initial state to minimize a given physical quantity of the output state. QuantumGEP is a driver program that uses evendim, a generic computational engine for GEP, both of which are free and open source. We apply QuantumGEP as a powerful solver for MaxCut in graphs and for condensed matter quantum many-body Hamiltonians.
97 MATHEMATICS AND COMPUTING↗
Scalable Programming Workflows for Validation of Quantum Computers
Hybrid quantum-classical workflows have become standard methods for executing variational algorithms and other quantum simulation techniques, which are key applications for noisy intermediate scale quantum (NISQ) computers. Validating these simulations is an important task which helps gauge the progress of quantum computer development, and classical simulation can serve as a tool to this end. Both exact and more scalable approximate methods with quantifiable error bounds can be used in validation tasks where the applicable metrics include the distance from a calculable ground truth, the quality of an error model fit to data, etc. Here we present a library extension that includes methods for validation of quantum simulations based on scalable hybrid workflows executable on high performance computers. We provide examples that use approximate methods based on tensor networks and stabilizer simulators to bound the error of quantum simulations on NISQ hardware.
Nguyen, Thien↗
Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing
Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.
bio-inspired, energy storage (including batteries ↗
Functional Programming in Computer Science
We explore functional programming through a 16-week internship at Los Alamos National Laboratory. Functional programming is a branch of computer science that has exploded in popularity over the past decade due to its high-level syntax, ease of parallelization, and abundant applications. First, we summarize functional programming by listing the advantages of functional programming languages over the usual imperative languages, and we introduce the concept of parsing. Second, we discuss the importance of lambda calculus in the theory of functional programming. Lambda calculus was invented by Alonzo Church in the 1930s to formalize the concept of effective computability, and every functional language is essentially some implementation of lambda calculus. Finally, we display the lasting products of the internship: additions to a compiler and runtime system for the pure functional language STG, including both a set of tests that indicate the validity of updates to the compiler and a compiler pass that checks for illegal instances of duplicate names.
97 MATHEMATICS AND COMPUTING↗
Radiological Safety Analysis Computer (RSAC) Program Version 7.4 Users’ Manual
The Radiological Safety Analysis Computer (RSAC) Program Version 7.4 (RSAC-7) is the newest version of the RSAC legacy code. RSAC-7 calculates the consequences of a release of radionuclides to the atmosphere. Users generates a fission product inventory from either reactor operating history or a nuclear criticality event. RSAC-7 models the effects of high-efficiency particulate air filters or other cleanup systems and calculates the decay and ingrowth during transport through processes, facilities, and the environment. Doses are calculated for inhalation, air immersion, ground surface, ingestion, and cloud gamma pathways. RSAC-7 is used as a tool to evaluate accident conditions in emergency response scenarios, radiological sabotage events, and safety basis accident consequences. This users’ manual contains the mathematical models and operating instructions for RSAC-7. Instructions, screens, and examples are provided to guide the user through the functions provided by RSAC-7. This program is designed for users who are familiar with radiological dose assessment methods.
73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs
We report that community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Low latency and persistent data storage
Persistent data storage is provided by a computer program product that includes computer program code configured for receiving a low latency store command that includes write data. The write data is written to a first memory device that is implemented by a nonvolatile solid-state memory technology characterized by a first access speed. It is acknowledged that the write data has been successfully written to the first memory device. The write data is written to a second memory device that is implemented by a volatile memory technology. At least a portion of the data in the first memory device is written to a third memory device when a predetermined amount of data has been accumulated in the first memory device. The third memory device is implemented by a nonvolatile solid-state memory technology characterized by a second access speed that is slower than the first access speed.
97 MATHEMATICS AND COMPUTING↗
Higgs boson mass in the standard model at two-loop order and beyond
We calculate the mass of the Higgs boson in the standard model in terms of the underlying Lagrangian parameters at complete 2-loop order with leading 3-loop corrections. A computer program implementing the results is provided. The program also computes and minimizes the standard model effective potential in Landau gauge at 2-loop order with leading 3-loop corrections.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
2015 HPC Annual Report
Center 9300 is very proud to provide a wide range of computing and networking resources ranging from desktop support to high performance computing that enable Sandia’s national security mission needs. Our goal is to provide these services in the most efficient, secure, user friendly, and agile means necessary. High performance computing is one example of how we are delivering on this goal. Thanks to investments from NNSA’s Advanced Simulation and Computing program, coupled with Sandia’s Institutional Computing program, a wide variety of computing resources are available and used to support national security needs. This year we are expanding our high performance computing infrastructure with the procurement of institutional resources for information computing or analytics. Big data analytics has been the buzzword in the computing industry for over a decade now and industries in finance, food and drug, business, and security organizations have been leading the charge. Much like how scientific computing has changed the way science and engineering are performed, data analytics is changing just about every aspect of our lives and more especially our ability to contribute to the national security mission of Sandia. This third edition of our HPC Annual Report continues our commitment to communicate the details and impact of Sandia’s large-scale computing resources that support missions work across Sandia. The Laboratories’ and NNSA’s strategic investments in high performance computing demonstrate our commitment to keeping mission-related research on the cutting edge of science, at pace with technology advances, and at a scale that meets the toughest demands of the nation we so proudly serve.
97 MATHEMATICS AND COMPUTING↗
The Institute for Sustained Performance, Energy, and Resilience Final Project Report
Over the course of the five years of the Scientific Discovery through Advanced Computation (SciDAC-3) program, computational scientists working on behalf of the Department of Energy's Office of Science (DOE SC) exploited a new generation of petascale computing resources to make previously inaccessible discoveries in a broad range of disciplines including physics, chemistry, and materials science. The computing systems underpinning this work increased in performance potential from tens to hundreds of PFlop/s, but in the process evolving significantly from architectures in 2011, when SciDAC-3 began. Although Moore's law continues unabated, the end of Dennard scaling has necessitated a fundamental shift in computer architecture focused on power efficiency. To that end, processors are increasingly varied as they strive to satisfy performance, productivity, reliability, and energy efficiency in the face of divergent computational requirements. The diversity among these machines presents a number of challenges to merely porting today's scientific applications, much less achieving good performance. Therefore, at the beginning of SciDAC-3, USC and its collaborators anticipated that both vastly increased scale (e.g., more chips, more cores per chip, wider SIMD) and heterogeneity would exacerbate performance optimization challenges while simultaneously promoting the issues of energy and heterogeneity will exacerbate performance optimization challenges while simultaneously promoting the issues of energy consumption and resilience to the forefront. To ensure that DOE's computational scientists could successfully exploit emerging high performance computing (HPC) systems, DOE SC’s Advanced Scientific Computing Research Program (ASCR) funded the SciDAC-3 Institute for Sustained Performance, Energy, and Resilience (SUPER). Led by USC, this was a broadly based project with expertise in compilers and system tools, performance engineering, energy management, and resilience. SUPER followed the successful model developed in the previous SciDAC-2 Performance Engineering Research Institute (PERI) of leveraging the research investments DOE and others have made and integrating the results to create new capabilities beyond the reach of any one group. To accomplish this, SUPER was organized into multi-institution teams conducting the research activities in performance portability, energy efficiency, exascale resilience, and overall optimization. SUPER researchers also collaborated with other SciDAC-3 institutes, 13 of the 19 Scientific Computation Application Partnerships (SAP), as well as the broader DOE computational sciences community. These collaborations both focused our research on the real challenges facing petascale scientific computing as well as ensured the broad and immediate impact of the results of our research.
97 MATHEMATICS AND COMPUTING↗
Comparing High Performance Computing Accelerator Programming Models
Accelerator devices are becoming a norm in High Performance Computing (HPC). With more systems opting for heterogeneous architectures, portable programming models like OpenMP and OpenACC are becoming increasingly important. The SPEC ACCEL 1.2 benchmark suite consists of comparable benchmarks in OpenCL, OpenMP 4.5, and OpenACC 2.5 that can be used to evaluate the performance and support for programming models and frameworks on heterogeneous platforms. In this paper we go beneath the normative metric of performance times and look at the individual kernels to study the usage, strengths, and weaknesses of the two prevalent portable heterogeneous programming models, OpenMP and OpenACC. From our analysis we identify that benchmarks like MRI-Q, SP and BT have better performance using OpenACC, while benchmarks like MiniGhost, LBM and LBDC do consistently better with the OpenMP programming model across supercomputers like Titan, and Summit.We deep dive into the kernels of select four benchmarks to answer questions like: Where does the benchmark spend most of its cycles? What is the parallelization strategy used? Why is one programming model more performant than the other? By identifying the similarities and dierences we want to contrast between the benchmark implementation strategies in the SPEC ACCEL 1.2 benchmarks and provide more insights into the OpenMP and OpenACC programming models.
Pophale, Swaroop S.↗
A Grand Canonical Monte Carlo simulation program for computing ion distributions around biomolecules in hard sphere solvents
The GIBS software program is a Grand Canonical Monte Carlo (GCMC) simulation program (written in C++) that can be used for 1) computing the excess chemical potential of ions and the mean activity coefficients of salts in homogeneous electrolyte solutions; and, 2) for computing the distribution of ions around fixed macromolecules such as, nucleic acids and proteins. The solvent can be represented as neutral hard spheres or as a dielectric continuum. The ions are represented as charged hard spheres that can interact via Coulomb, hard-sphere, or Lennard-Jones potentials. In addition to hard-sphere repulsions, the ions can also be made to interact with the solvent hard spheres via short-ranged attractive square-well potentials.
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