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DOE OSTI2021

The Role of Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology: Proceedings of a Workshop

Quantum concepts hold the potential to enable significant advances in sensing and imaging technologies that could be vital to the study of biological systems. The workshop Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology, held online March 8–10, 2021, was organized to examine the research and development needs to advance biological applications of quantum technology. Hosted by the National Academies of Sciences, Engineering, and Medicine, the event brought together experts working on state-of-the-art, quantum-enabled technologies and scientists who are interested in applying these technologies to biological systems. Through talks, panels, and discussions, the workshop facilitated a better understanding of the current and future biological applications of quantum-enabled technologies in fields such as microbiology, molecular biology, cell biology, plant science, mycology, and many others. The workshop was organized around three main themes. The first, quantum in biology, examined quantum concepts that are hypothesized to be important for life processes and that researchers are working to observe through biological imaging and sensing. The second, quantum for biology, addressed ways to use quantum concepts to enhance technologies for biological imaging and sensing. The third, biology for quantum, offered a wider discussion of how the frontiers of biological imaging and sensing could enable future study using quantum concepts, tools, or technologies. Throughout the workshop, participants identified a wide range of emerging approaches and opportunities at the intersection of quantum physics and biological sensing and imaging. During the workshop, there were some differences in how each speaker defined the term quantum. During one of the panels, Prem Kumar offered thoughts on what phenomena are classical versus quantum, explaining that techniques get progressively more quantum as you move from just having superposition to having superposition with measurement and entanglement. Another explanation from Clarice Aiello delineates the definition into several levels. This includes a base level of “quantum-ness,” which reflects that all matter is made of atoms, and when these particles are isolated they behave based on quantum mechanical principles. A second level is related to quantum coherence, where a single quantum object might be found in a coherent superposition state. A final level, which she described as the quantum-entangled level, involves multiple quantum systems which are entangled among themselves. Overall, the workshop touched on concepts such as superposition, entanglement, and squeezing and their potential implications for communication, computing, and simulation, in addition to the workshop’s main focal area, biological sensing and imaging. At the opening of the workshop, Thorsten Ritz of the University of California, Irvine, identified two questions at the heart of quantum biology: Is the machinery of life quantum mechanical, and can quantum mechanics be used to study the machinery of life in new ways? Participants highlighted systems in which researchers have explored these questions, from the vast array of molecular interactions involved in biological processes such as photosynthesis, to the mechanics involved in cellular functions such as differentiation and aggregation, to the role of oscillating magnetic fields in flight orientation among birds. Sensing and imaging technologies are crucial to biological research; these technologies could both enhance the study of quantum effects and be enhanced by quantum concepts. A critical challenge in biological research is to develop imaging and sensing tools that do not damage or interfere with the often fragile and fleeting systems being studied. Attendees discussed a variety of established and emerging technologies that could enhance noninvasive biological imaging, including single- and two-photon spectroscopy, single-molecule spectroscopy, quantum illumination, ghost imaging, and cryo-electron microscopy. One example came from Marlan Scully who gave a keynote address on the first day of the workshop. Scully emphasized the use of different laser technologies, which exhibit coherence and other quantum properties, in moving toward real-world biological applications, such as the detection of SARS-CoV-2. Both tools and theory will play an important role in advancing quantum biology research and applications. Several participants suggested theorists and experimentalists should work in tandem to understand and model biological processes. While physics often reduces systems to their simplest forms for fundamental insights, participants also noted the value of observing and understanding biological systems in all their “messiness,” capturing both the inner workings of biological systems and the complex interactions that occur within and between organisms. In discussions among participants, several attendees stressed the need to match emerging tools with the right scientific questions. Rather than developing quantum technologies as “a hammer looking for a nail,” participants emphasized a focus on exploring the problems these technologies are best suited to address. For example, it is important to consider the size of the phenomenon being studied, the timescales that are important in answering the scientific question, and other relevant considerations. Every tool along the spectrum from classical to quantum involves its own set of trade-offs. For example, Ted Laurence of the Lawrence Livermore National Laboratory said that quantum measurements, such as single photon counting, fluorescence transitions, and lasers, can take longer to produce the same results as classical measurements. These quantum measurements, however, do not require calibration and can enable new research questions to be answered. Prem Kumar, Northwestern University, noted that, despite their promise, quantum approaches should not be used simply for the sake of using quantum, especially in situations where classical approaches better meet the needs of the researcher. Understanding and applying quantum concepts could enable advances in a wide range of application areas including energy, synthetic biology, medicine, and sustainability. For example, Michelle O’Malley, University of California, Santa Barbara, described how improved noninvasive imaging approaches could help capture the complex interactions and functions involved in the breakdown of organic matter by microbial communities and lead to new technologies for capturing valuable products from plant waste. Several other participants discussed needs in tracking the movement of metabolites and molecules in microbial communities for insights into nutrient cycling in environments such as soil. Margaret Ahmad, Sorbonne University, discussed potential opportunities to leverage the magnetic properties of cryptochromes to advance new treatment approaches for diseases such as COVID-19 and cancer. Looking toward the future development of the field, participants discussed challenges to advancing quantum biology that arise from disciplinary disconnects between physicists and biologists. The siloing of academic research disciplines represents a significant barrier to progress. Disconnects in terminology, motivations and priorities, and structural barriers to collaborative work underscore the need for concerted efforts to bridge these divides. Attendees and speakers offered suggestions for resolving these divergences, establishing a shared language, moving the field forward, and fostering meaningful feedback between disciplines. Overall, participants stressed a need for balance, open communication, collaboration, unity, and clear dialogue on trade-offs between quantum and classical approaches. Keiko Torii, The University of Texas at Austin, said that the best collaborations happen when the project provides mutual advantages that can show off everyone’s talents, each team finds the work interesting, and partners develop a camaraderie to pursue new knowledge. To enable near- and long-term opportunities in this space, participants suggested that exploratory, high-risk funding could improve existing instrumentation to explore quantum enhancement collaboratively. They also emphasized the need for collaboration between quantum physicists and sensing/imaging scientists, which could be advanced through a dedicated quantum biology investigator program. People, even more than technology, will be crucial to the future of quantum biology. Participants explored training, education, and workforce needs to further develop this burgeoning field and cultivate the next generation of scientists. While many programs are still in their nascent stages, participants highlighted examples of approaches and programs being developed at various types of institutions to engage students and professional scientists in quantum biology research. Several participants stressed the need for an inclusive approach, spanning disciplines as well as communities to foster a diverse field fueled by the intellectual contributions of a wide range of people, including historically under-resourced schools and students. To increase awareness and excitement about quantum physics and related areas of biology, attendees suggested capitalizing on the “buzz” around quantum. Several participants emphasized the need to start early, introducing students to quantum concepts and their appealing “weirdness” in K–12 education. Engaging students early—before they become entrenched in traditional disciplinary siloes as typically happens in graduate school—could help to galvanize interest in the area and foster a generation of scientists with the interdisciplinary mindset and skills needed to advance this interdisciplinary field. While these efforts could be advanced at many levels and across multiple sectors, several participants suggested a national quantum biology center could be a valuable hub to coordinate and support quantum biology education and workforce development across academia, industry, and government.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A Program Analysis Tool to Query Static and Dynamic Characteristics of HPC Applications

Emerging HPC platforms are becoming extraordinarily difficult to program as a result of complex, deep and heterogeneous memory hierarchies, heterogeneous cores, and the need to divide work and data among them. New programming models and libraries are being developed to aid in porting efforts of application developers, but substantial code restructuring is still necessary to fully make use of these new technologies. To do this effectively, these developers need information about their source code characteristics, including static and dynamic (e.g. performance) information to direct their optimisation efforts and make key decisions. On the other hand, system administrators need to understand how users are using the software stack and resources on their systems to understand what software they need to provide to improve the productivity of the users on a platform.In this paper we describe a tool that combines compiler and profiler information to query performance characteristics within an application or across multiple applications on a given platform. Static and dynamic data about applications are collected and stored together in an SQL database that can be queried by either a developer or a system administrator. We will demonstrate the capabilities of this tool via a real-world example from application-driven case studies that aims at understanding the use of scientific libraries on a routine from the molecular simulation application CP2K.

Welch, Aaron↗
DOE OSTI2016

The application of MAMMOTH for a detailed tightly coupled fuel pin simulation with a station blackout

The accurate calculation of desired quantities to predict fuel behavior in a nuclear reactor requires the solution of inter linked equations representing different physics phenomena. Traditional fuels performance codes often rely on internal empirical models for the pin power density and a simplified boundary condition on the cladding outer surface. These simplifications are performed because of the difficulty of coupling applications or codes on differing domains and mapping the required data. In this work the MAMMOTH application is employed in the demonstration of an approach that more consistent with first principles, in a simulation that couples the neutronics application Rattlesnake, the thermal fluids application RELAP-7 and the fuels performance application BISON. A single fuel pin was modeled based on the dimensions of a fuel rod from a Westinghouse 17*17 optimized PWR fuel assembly. The simulation consisted of a depletion period of 1343 days, representing three full operating cycles, followed by a station blackout (SBO) event. The fuel rod was depleted with a total pin power of 65.81 kW. After 1343 days the fission power was reduced to zero (simulating a reactor shutdown). Decay heat calculations provided the time-varying energy source after this time. For this problem, Rattlesnake, BISON, and RELAP-7 are coupled under MAMMOTH in a tightly coupled approach. Each system solves its physics on a separate mesh and, for RELAP-7 and BISON, on only a subset of the full problem domain. Rattlesnake solves the neutronics over the whole domain, which includes the fuel, cladding, gaps, water, and top and bottom rod holders. Here BISON is applied to the fuel and cladding with a 2-D axisymmetric domain, and RELAP-7 is applied to the flow of the circular outer water channel with a set of 1-D flow equations. For this work, the mesh on the Rattlesnake side is either be 3-D (for low order PN transport or diffusion) or 2-D axisymmetric (for diffusion). BISON has a matching ring structure mesh for the fuel so both the power density and local burnup are transferred accurately from Rattlesnake. At each depletion time step, Rattlesnake calculates a power density, fission density rate, burnup distribution and fast flux based on the current water density and fuel temperature. These are then mapped to the BISON mesh to perform a fuels performance solution. BISON calculates the fuel temperature and cladding surface temperature based upon the current power density and bulk fluid temperature. RELAP-7 then calculates the fluid temperature, water density fraction and water phase velocity based upon the cladding surface temperature. The fuel temperature and the fluid density are then passed back to Rattlesnake for another neutronics calculation. For this paper, sets of results from the detailed calculation are provided for both depletion and the SBO event. We demonstrate that a detailed simulation akin to first principles can be achieved using MAMMOTH and the various MOOSE application on differing domains. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Evaluating Application Characteristics for GPU Portability Layer Selection

GPUs have become the dominant source of computing power for high performance computing and are increasingly being used across the High Energy Physics computing landscape for a wide variety of tasks. Though NVIDIA is currently the main provider of GPUs, AMD and Intel are rapidly increasing their market share. As a result, programming using a vendor-specific language such as CUDA can significantly reduce deployment choices. There are a number of portability layers such as Kokkos, Alpaka, SYCL, OpenMP and std::par that permit execution on a broad range of GPU and CPU architectures, significantly increasing the flexibility of application programmers. However, each of these portability layers has its own characteristics, performing better at some tasks and worse at others, or placing limitations on aspects of the application. In this presentation, we report on a study of application and kernel characteristics that can influence the choice of a portability layer and show how each layer handles these characteristics. We have analyzed representative heterogeneous applications from CMS (patatrack and p2r), DUNE (Wire-Cell Toolkit), and ATLAS (FastCaloSim) to identify key application characteristics that have different behaviors for the various portability technologies. Using these results, developers can make more informed decisions on which GPU portability technology is best suited to their application.

Atif, Mohammad [Brookhaven]↗
DOE OSTI2024

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging. We propose a low-overhead autotuning framework to autotune performance and energy for various hybrid MPI/OpenMP scientific applications at large scales and to explore the tradeoffs between application runtime and power/energy for energy efficient application execution, then use this framework to autotune four ECP proxy applications—XSBench, AMG, SWFFT, and SW4lite. Our approach uses Bayesian optimization with a Random Forest surrogate model to effectively search parameter spaces with up to 6 million different configurations on two large-scale HPC production systems, Theta at Argonne National Laboratory and Summit at Oak Ridge National Laboratory. The experimental results show that our autotuning framework at large scales has low overhead and achieves good scalability. Using the proposed autotuning framework to identify the best configurations, we achieve up to 91.59% performance improvement, up to 21.2% energy savings, and up to 37.84% EDP (energy delay product) improvement on up to 4096 nodes.

Autotuning↗
DOE OSTI2023

A Fine-grained Asynchronous Bulk Synchronous parallelism model for PGAS applications

The Partitioned Global Address Space (PGAS) model is well suited for executing irregular applications on cluster-based systems, due to its efficient support for short, one-sided messages. Separately, the actor model has been gaining popularity as a productive asynchronous message-passing approach for distributed objects in enterprise and cloud computing platforms, typically implemented in languages such as Erlang, Scala or Rust. To the best of our knowledge, there has been no past work on using the actor model to deliver both productivity and scalability to irregular PGAS applications with large number of small messages. In this paper, we introduce a new programming system for PGAS applications, in which point-to-point remote operations can be expressed as fine-grained asynchronous actor messages. In our approach, the programmer does not need to worry about programming complexities related to message aggregation and termination detection. Our approach can be viewed as extending the classical Bulk Synchronous Parallelism model with fine-grained asynchronous communications within a phase or superstep. Here, we believe that our approach offers a desirable point in the productivity-performance space for PGAS applications, with more scalable performance and higher productivity relative to past approaches. Specifically, for seven irregular mini-applications from the Bale Kernels and three graph kernels executed using 2048 cores in the NERSC Cori system, our approach shows geometric mean performance improvements of ≥ 20X relative to standard PGAS versions (UPC and OpenSHMEM) while maintaining comparable productivity to those versions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

PARIS: Predicting application resilience using machine learning

The traditional method to study application resilience to errors in HPC applications uses fault injection (FI), a time-consuming approach. Furthermore, while analytical models have been built to overcome the inefficiencies of FI, they lack accuracy. In this paper, we present PARIS, a machine-learning method to predict application resilience that avoids the time-consuming process of random FI and provides higher prediction accuracy than analytical models. PARIS captures the implicit relationship between application characteristics and application resilience, which is difficult to capture using most analytical models. We overcome many technical challenges for feature construction, extraction, and selection to use machine learning in our prediction approach. Our evaluation on 16 HPC benchmarks shows that PARIS achieves high prediction accuracy. PARIS is up to 450x faster than random FI (49x on average). Compared to the state-of-the-art analytical model, PARIS is at least 63% better in terms of accuracy and has comparable execution time on average.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Global Transformations for Legacy Parallel Applications via Structural Analysis and Rewriting

Performance and scalability optimization of large HPC applications is currently a labor-intensive, manual process with very low productivity. Major difficulties come from the disaggregated environment for HPC application development: the compiler is only involved in local decisions (core or multithreaded domain), while a library-based, communication-oriented programming model realizes whole-machine parallelism. Realizing any major global change in such a disaggregated environment is very difficult and involves changing large portions of the source code. We present semi-automated techniques, based on structural analysis and rewriting, for performing global transformations on an HPC application source code. We present two case studies using the Self-Consistent Field (SCF) standalone benchmark as well as the Coupled Cluster (CCSD) module (2.9 million lines of Fortran code), a key module of the NWChem computational chemistry application. We demonstrate how structural rewriting techniques can be used to automate transformations that affect multiple sections of the application's source code. We show that the transformations can be applied in a systematic fashion across the source code bases with minimal manual effort. These transformations improve the scalability of the SCF benchmark by more than two orders of magnitude and the performance of the full CCSD module by a factor of 4.0.

legacy parallel applications↗
DOE OSTI2023

Medium grain niobium SRF cavity production technology for science frontiers and accelerator applications

Herein we present the cost-effective production of superconducting radio frequency (SRF) cavities made of medium grain (MG) niobium (Nb) discs directly sliced from forged and annealed billet. This production method provides clean surface conditions and reliable mechanical characteristics with sub-millimeter average grain size resulting in stable SRF cavity production. We propose to apply this material to particle accelerator in the science and industrial applications. The science applications require high field gradients(≥ 30 MV/m) particularly in pulsed mode. The industrial applications require high $Q_0$ values with moderate gradients (~ 20 MV/m) in CW mode operation. This paper describes the MG Nb disc production recently demonstrated and discusses future prospects for application in advanced particle accelerators in the science and industrial applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Scaling Irregular Applications through Data Aggregation and Software Multithreading

Bioinformatics, data analytics, semantic databases, knowledge discovery are emerging high performance application areas that exploit dynamic, linked data structures such as graphs, unbalanced trees or unstructured grids. These data structures usually are very large, requiring significantly more memory than available on single shared memory systems. Additionally, these data structures are difficult to partition on distributed memory systems. They also present poor spatial and temporal locality, thus generating unpredictable memory and network accesses. The Partitioned Global Address Space (PGAS) programming model seems suitable for these applications, because it allows using a shared memory abstraction across distributed-memory clusters. However, current PGAS languages and libraries are built to target regular remote data accesses and block transfers. Furthermore, they usually rely on the Single Program Multiple Data (SPMD) parallel control model, which is not well suited to the fine grained, dynamic and unbalanced parallelism of irregular applications. In this paper we present {\bf GMT} (Global Memory and Threading library), a custom runtime library that enables efficient execution of irregular applications on commodity clusters. GMT integrates a PGAS data substrate with simple fork/join parallelism and provides automatic load balancing on a per node basis. It implements multi-level aggregation and lightweight multithreading to maximize memory and network bandwidth with fine-grained data accesses and tolerate long data access latencies. A key innovation in the GMT runtime is its thread specialization (workers, helpers and communication threads) that realize the overall functionality. We compare our approach with other PGAS models, such as UPC running using GASNet, and hand-optimized MPI code on a set of typical large-scale irregular applications, demonstrating speedups of an order of magnitude.

Aggregation↗
DOE OSTI2016

Fault Modeling of Extreme Scale Applications Using Machine Learning

Faults are commonplace in large scale systems. These systems experience a variety of faults such as transient, permanent and intermittent. Multi-bit faults are typically not corrected by the hardware resulting in an error. Here, this paper attempts to answer an important question: Given a multi-bit fault in main memory, will it result in an application error — and hence a recovery algorithm should be invoked — or can it be safely ignored? We propose an application fault modeling methodology to answer this question. Given a fault signature (a set of attributes comprising of system and application state), we use machine learning to create a model which predicts whether a multibit permanent/transient main memory fault will likely result in error. We present the design elements such as the fault injection methodology for covering important data structures, the application and system attributes which should be used for learning the model, the supervised learning algorithms (and potentially ensembles), and important metrics. Lastly, we use three applications — NWChem, LULESH and SVM — as examples for demonstrating the effectiveness of the proposed fault modeling methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Application Characterization at Scale: Lessons learned from developing a distributed Open Community Runtime system for High Performance Computing

Since 2012, the U.S. Department of Energy’s X-Stack program has been developing solutions including runtime systems, programming models, languages, compilers, and tools for the Exascale system software to address crucial performance and power requirements. Fine grain programming models and runtime systems show a great potential to efficiently utilize the underlying hardware. Thus, they are essential to many X-Stack efforts. An abundant amount of small tasks can better utilize the vast parallelism available on current and future machines. Moreover, finer tasks can recover faster and adapt better, due to a decrease in state and control. Nevertheless, current applications have been written to exploit old paradigms (such as Communicating Sequential Processor and Bulk Synchronous Parallel processing). To fully utilize the advantages of these new systems, applications need to be adapted to these new paradigms. As part of the applications’ porting process, in-depth characterization studies, focused on both application characteristics and runtime features, need to take place to fully understand the application performance bottlenecks and how to resolve them. This paper presents a characterization study for a novel high performance runtime system, called the Open Community Runtime, using key HPC kernels as its vehicle. This study has the following contributions: one of the first high performance, fine grain, distributed memory runtime system implementing the OCR standard (version 0.99a); and a characterization study of key HPC kernels in terms of runtime primitives running on both intra and inter node environments. Running on a general purpose cluster, we have found up to 1635x relative speed-up for a parallel tiled Cholesky Kernels on 128 nodes with 16 cores each and a 1864x relative speed-up for a parallel tiled Smith-Waterman kernel on 128 nodes with 30 cores.

Application characterization↗
DOE OSTI2016

Exploring versioned distributed arrays for resilience in scientific applications: Global view resilience

Exascale studies project reliability challenges for future HPC systems. We present the Global View Resilience (GVR) system, a library for portable resilience. GVR begins with a subset of the Global Arrays interface, and adds new capabilities to create versions, name versions, and compute on version data. Applications can focus versioning where and when it is most productive, and customize for each application structure independently. This control is portable, and its embedding in application source makes it natural to express and easy to maintain. The ability to name multiple versions and “partially materialize” them efficiently makes ambitious forward-recovery based on “data slices” across versions or data structures both easy to express and efficient. Using several large applications (OpenMC, preconditioned conjugate gradient (PCG) solver, ddcMD, and Chombo), we evaluate the programming effort to add resilience. The required changes are small (< 2% lines of code (LOC)), localized and machine-independent, and perhaps most important, require no software architecture changes. We also measure the overhead of adding GVR versioning and show that overheads < 2% are generally achieved. This overhead suggests that GVR can be implemented in large-scale codes and support portable error recovery with modest investment and runtime impact. Our results are drawn from both IBM BG/Q and Cray XC30 experiments, demonstrating portability. We also present two case studies of flexible error recovery, illustrating how GVR can be used for multi-version rollback recovery, and several different forward-recovery schemes. GVR’s multi-version enables applications to survive latent errors (silent data corruption) with significant detection latency, and forward recovery can make that recovery extremely efficient. Lastly, our results suggest that GVR is scalable, portable, and efficient. GVR interfaces are flexible, supporting a variety of recovery schemes, and altogether GVR embodies a gentle-slope path to tolerate growing error rates in future extreme-scale systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Air Dispersion Modeling for the INL Application for a Synthetic Minor Sitewide Air Quality Permit to Construct with a Facility Emission Cap Component

The Department of Energy Idaho Operations Office (DOE-ID) is applying for a synthetic minor, Sitewide, air quality permit to construct (PTC) with a facility emission cap (FEC) component from the Idaho Department of Environmental Quality (DEQ) for Idaho National Laboratory (INL) to limit its potential to emit to less than major facility limits for criteria air pollutants (CAPs) and hazardous air pollutants (HAPs) regulated under the Clean Air Act. This document is supplied as an appendix to the application, Idaho National Laboratory Application for a Synthetic Minor Sitewide Air Quality Permit to Construct with a Facility Emissions Cap Component, hereafter referred to as “permit application” (DOE-ID 2015). Air dispersion modeling was performed as part of the permit application process to demonstrate pollutant emissions from the INL will not cause a violation of any ambient air quality standards. This report documents the modeling methodology and results for the air dispersion impact analysis. All CAPs regulated under Section 109 of the Clean Air Act were modeled with the exception of lead (Pb) and ozone, which are not required to be modeled by DEQ. Modeling was not performed for toxic air pollutants (TAPs) as uncontrolled emissions did not exceed screening emission levels for carcinogenic and non-carcinogenic TAPs. Modeling for CAPs was performed with the EPA approved AERMOD dispersion modeling system (Version 14134) (EPA 2004a) and five years (2000-2004) of meteorological data. The meteorological data set was produced with the companion AERMET model (Version 14134) (EPA 2004b) using surface data from the Idaho Falls airport, and upper-air data from Boise International Airport supplied by DEQ. Onsite meteorological data from the Grid 3 Mesonet tower located near the center of the INL (north of INTEC) and supplied by the local National Oceanic and Atmospheric Administration (NOAA) office was used for surface wind directions and wind speeds. Surface data (i.e., land use data that defines roughness, albedo, Bowen ratio, and other parameters) were processed using the AERSURFACE utility (Version 13016) (EPA 2013). Emission sources were modeled as point sources using actual stack locations and dimensions. Emissions, flow rates and exit temperatures were based on the design operating capacity of each source. All structures close enough to produce an area of wake effect were included for all sources. For multi-tiered structures, the heights of the tiers were included or the entire building height was assumed to be equal to the height of the tallest tier. Concentrations were calculated at 1,352 receptor locations provided by DEQ. All receptors were considered for each pollutant and averaging period. Maximum modeled CAP concentrations summed with average background concentration values were presented and compared to National Ambient Air Quality Standards (NAAQS). The background concentration values used were obtained using the Washington State University’s Laboratory for Atmospheric Research North West Airquest web-based retrieval tool (http://lar.wsu.edu/nw airquest/lookup.html). The air dispersion modeling results show the maximum impacts for CAPs are less than applicable standards and demonstrate the INL will not cause a violation of any ambient air quality standards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

25th Anniversary Article: Semiconductor Nanowires - Synthesis, Characterization, and Applications

Semiconductor nanowires (NWs) have been studied extensively for over two decades for their novel electronic, photonic, thermal, electrochemical and mechanical properties. In this comprehensive review article we summarize major advances in the synthesis, characterization, and application of these materials in the past decade. Developments in the understanding of the fundamental principles of “bottom-up” growth mechanisms are presented, with an emphasis on rational control of the morphology, stoichiometry, and crystal structure of the materials. This is followed by a discussion of the application of nanowires in i) electronic, ii) sensor, iii) photonic, iv) thermoelectric, v) photovoltaic, vi) photoelectrochemical, vii) battery, viii) mechanical, and ix) biological applications. Throughout the discussion, a detailed explanation of the unique properties associated with the one-dimensional nanowire geometry will be presented, and the benefits of these properties for the various applications will be highlighted. The review concludes with a brief perspective on future research directions, and remaining barriers which must be overcome for the successful commercial application of these technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Genetic algorithm based task reordering to improve the performance of batch scheduled massively parallel scientific applications

The growth in size of networked high performance computers along with novel accelerator-based node architectures has further emphasized the importance of communication efficiency in high performance computing. The world's largest high performance computers are usually operated as shared user facilities due to the costs of acquisition and operation. Applications are scheduled for execution in a shared environment and are placed on nodes that are not necessarily contiguous on the interconnect. Furthermore, the placement of tasks on the nodes allocated by the scheduler is sub-optimal, leading to performance loss and variability. Here, we investigate the impact of task placement on the performance of two massively parallel application codes on the Titan supercomputer, a turbulent combustion flow solver (S3D) and a molecular dynamics code (LAMMPS). Benchmark studies show a significant deviation from ideal weak scaling and variability in performance. The inter-task communication distance was determined to be one of the significant contributors to the performance degradation and variability. A genetic algorithm-based parallel optimization technique was used to optimize the task ordering. This technique provides an improved placement of the tasks on the nodes, taking into account the application's communication topology and the system interconnect topology. As a result, application benchmarks after task reordering through genetic algorithm show a significant improvement in performance and reduction in variability, therefore enabling the applications to achieve better time to solution and scalability on Titan during production.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Graphitic Carbon Nitride Quantum Dots (g‐C 3 N 4 QDs): From Chemistry to Applications

Since their emergence in 2014, graphitic carbon nitride quantum dots (g-C 3 N 4 QDs) have attracted much interest from the scientific community due to their distinctive physicochemical features, including structural, morphological, electrochemical, and optoelectronic properties. Owing to their desirable characteristics, such as non-zero band gap, ability to be chemically functionalized or doped, possessing tunable properties, outstanding dispersibility in different media, and biocompatibility, g-C 3 N 4 QDs have shown promise for photocatalysis, energy devices, sensing, bioimaging, solar cells, optoelectronics, among other applications. As these fields are rapidly evolving, it is very strenuous to pinpoint the emerging challenges of the g-C 3 N 4 QDs development and application during the last decade, mainly due to the lack of critical reviews of the innovations in the g-C 3 N 4 QDs synthesis pathways and domains of application. Herein, an extensive survey is conducted on the g-C 3 N 4 QDs synthesis, characterization, and applications. Scenarios for the future development of g-C 3 N 4 QDs and their potential applications are highlighted and discussed in detail. In conclusion, the provided critical section suggests a myriad of opportunities for g-C 3 N 4 QDs, especially for their synthesis and functionalization, where a combination of eco-friendly/single step synthesis and chemical modification may be used to prepare g-C 3 N 4 QDs with, for example, enhanced photoluminescence and production yields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Profiling Production OpenSHMEM Applications, In: OpenSHMEM 2016: OpenSHMEM and Related Technologies. Enhancing OpenSHMEM for Hybrid Environments

Developing high performance OpenSHMEM applications routinely involves gaining a deeper understanding of software execution, yet there are numerous hurdles to gathering performance metrics in a production environment. Most OpenSHMEM performance profilers rely on the PSHMEM interface but PSHMEM is an optional and often unavailable feature. We present a tool that generates direct measurement performance profiles of OpenSHMEM applications even when PSHMEM is unavailable. The tool operates on dynamically linked and statically linked application binaries, does not require debugging symbols, and functions regardless of compiler optimization level. Integrated in the TAU Performance System, the tool uses automatically-generated wrapper libraries that intercept OpenSHMEM API calls to gather performance metrics with minimal overhead. Dynamically linked applications may use the tool without modifying the application binary in any way.

Computer Science↗