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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

From Data to Models and Analytics: An Integrated Scientific Computing Platform to Accelerate Environmental Research SBIR Phase I (Final Technical Report)

Phase I research was conducted to investigate the technical feasibility of an integrated open source platform GeoLib to provide turnkey solutions for model-data integration for environmental systems science (ESS), using modern web technologies, advances in HPC and cloud computing and high-performance visualization. A key feature of our platform is that it minimizes and automates data movement between data storage and computing systems. Another critical feature of the design is that it enables users to perform high-fidelity post-processing by performing high-performance visualization server-side and in-situ on the compute cluster close to the data. The investigation, which was performed over a 12-month period ending April 2019, centered on developing prototype software to demonstrate the key enabling technologies required for this system. This included: (i) rapid access to data from multiple remote sources, via a unified data interface; (ii) software for scalable geospatial visualization of raster, vector, and point cloud data on the web; (iii) development of an easy-to-use API for creating simulation and modeling workflows; (iv) remote data visualization and processing on middle-tier servers; and (v) remote data processing on high-performance computing systems. A critical part of this work is that all of these capabilities be accessible to end users through standard desktop tools such as Project Jupyter, which is widely adopted for ESS simulation and modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fair rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Thermal Management for FPGA Nodes in HPC Systems

The integration of FPGAs into large-scale computing systems is gaining attention. In these systems, real-time data handling for networking, tasks for scientific computing, and machine learning can be executed with customized datapaths on reconfigurable fabric within heterogeneous compute nodes. At the same time, thermal management, particularly battling the cooling cost and guaranteeing the reliability, is a continuing concern. The introduction of new heterogeneous components into HPC nodes only adds further complexities to thermal modeling and management. The thermal behavior of multi-FPGA systems deployed within large compute clusters is less explored. Here, we first show that the thermal behaviors of different FPGAs of the same generation can vary due to their physical locations in a rack and process variation, even though they are running the same tasks. We present a machine learning–based model to capture the thermal behavior of each individual FPGA in the cluster. We then propose two thermal management strategies guided by our thermal model. First, we mitigate thermal variation and hotspots across the cluster by proactive thermal-aware task placement. Under the tested system and benchmarks, we achieve up to 26.4° C and on average 13.3° C system temperature reduction with no performance penalty. Second, we utilize this thermal model to guide HLS parameter tuning at the task design stage to achieve improved thermal response after deployment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Ocelot: An Interactive, Efficient Distributed Compression-As-a-Service Platform With Optimized Data Compression Techniques

Large volumes of data generated by scientific simulations, genome sequencing, and other applications need to be moved among clusters for data collection/analysis. Data compression techniques have effectively reduced data storage and transfer costs. However, users' requirements on interactively controlling both data quality and compression ratios are non-trivial to fulfill. Here, we propose a novel Compression-as-a-Service (CaaS) platform called Ocelot with four important contributions: (1) It offers real-time visualization, interactive compression, and transfer of scientific datasets. (2) It incorporates new strategies for compressing diverse types of datasets more effectively than traditional methods. (3) It provides an effective method for estimating the compression ratio and execution time of compression tasks. (4) Experiments on multiple real-world datasets on geographically distributed computers show that Ocelot can significantly improve data transfer efficiency with a performance gain of more than 10x in computing clusters with relatively slow networks.

compression as a service (CaaS)↗
DOE OSTI2018

PATCHWORK: A Multipatch Infrastructure for Multiphysics/Multiscale/Multiframe Fluid Simulations

Here, we present a "multipatch" infrastructure for the numerical simulation of fluid problems in which subregions require different grid scales, different grid geometries, different physical equations, or different reference frames. Its key element is a sophisticated client–router–server framework for efficiently linking processors supporting different regions ("patches") that must exchange boundary data. This infrastructure may be used with a wide variety of fluid dynamics codes; the only requirement is that their primary dependent variables be the same in all patches, e.g., fluid mass density, internal energy density, and velocity. Its structure can accommodate either Newtonian or relativistic dynamics. The overhead imposed by this system is both problem and computer cluster architecture dependent. Compared to a conventional simulation using the same number of cells and processors employed on a problem not requiring multipatch methods, the cell update per processor rate decreases by an amount that can range from negligible to a factor of a few; however, even in these problems, the infrastructure can permit substantial decreases in the total number of cell updates required.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Simulated evolution of fluorophores for light emitting diodes

Organic light emitting diodes based on fluorophores with a propensity for thermally activated delayed fluorescence (TADF) are able to circumvent limitations imposed on device efficiency by spin statistics. Molecules with a propensity for TADF necessarily have two properties: a small gap between the lowest lying singlet and triplet excited states and a large transition dipole moment for fluorescence. In this work, we demonstrate the use of a genetic algorithm to search a region of chemical space for molecules with these properties. This algorithm is based on a flexible and intuitive representation of the molecule as a tree data structure, in which the nodes correspond to molecular fragments. Our implementation takes advantage of hybrid parallel graphics processing unit accelerated computer clusters to allow efficient sampling while retaining a reasonably accurate description of the electronic structure (in this case, CAM-B3LYP/6-31G{sup ∗∗}). In total, we have identified 3792 promising candidate fluorophores from a chemical space containing 1.26 × 10{sup 6} molecules. This required performing electronic structure calculations on only 7518 molecules, a small fraction of the full space. Several novel classes of molecules which show promise as fluorophores are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

GPU-acceleration of the ELPA2 distributed eigensolver for dense symmetric and hermitian eigenproblems

The solution of eigenproblems is often a key computational bottleneck that limits the tractable system size of numerical algorithms, among them electronic structure theory in chemistry and in condensed matter physics. Large eigenproblems can easily exceed the capacity of a single compute node, thus must be solved on distributed-memory parallel computers. We here present GPU-oriented optimizations of the ELPA two-stage tridiagonalization eigensolver (ELPA2). On top of cuBLAS-based GPU offloading, we add a CUDA kernel to speed up the back-transformation of eigenvectors, which can be the computationally most expensive part of the two-stage tridiagonalization algorithm. Furthermore, we benchmark the performance of this GPU-accelerated eigensolver on two hybrid CPU–GPU architectures, namely a compute cluster based on Intel Xeon Gold CPUs and NVIDIA Volta GPUs, and the Summit supercomputer based on IBM POWER9 CPUs and NVIDIA Volta GPUs. Consistent with previous benchmarks on CPU-only architectures, the GPU-accelerated two-stage solver exhibits a parallel performance superior to the one-stage counterpart. Finally, we demonstrate the performance of the GPU-accelerated eigensolver developed in this work for routine semi-local KS-DFT calculations comprising thousands of atoms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

MCR Container Tools

MathWorks' MATLAB is widely used in academia and industry for prototyping, data analysis, data processing, etc. Many users compile their programs using the MATLAB Compiler to run on workstations/computing clusters via the free MATLAB Compiler Runtime (MCR). The MCR facilitates the execution of code calling Application Programming Interfaces (API) functions from both base MATLAB and MATLAB toolboxes. In a Linux environment, a sizable number of third-party runtime dependencies (i.e. shared libraries) are necessary. Unfortunately, to the MTLAB community's knowledge, these dependencies are not documented, leaving system administrators and/or end-users to find/install the necessary libraries either as runtime errors resulting from them missing or by inspecting the header information of Executable and Linkable Format (ELF) libraries of the MCR to determine which ones are missing from the system. To address various shortcomings, Docker Images based on Community Enterprise Operating System (CentOS) 7, a derivative of Redhat Enterprise Linux (RHEL) 7, containing recent (2015-2017) MCR releases and their dependencies were created. These images, along with a provided sample Docker Compose YAML Script, can be used to create a simulated computing cluster where MATLAB Compiler created binaries can be executed using a sample Slurm Workload Manager script.

Haas, NicholasQ↗
DOE OSTI2015

2015 IC Report: w14_biosurveillance [Slides]

Our primary use of the institutional computing clusters has been for Wikipedia-based disease surveillance and forecasting work. We've expanded breadth: the number of diseases and locations that can be surveilled using internet data. We've expanded forecasting: surprisingly little work towards forecasting disease spread with quantified uncertainty. We've expanded big data processing techniques: the Wikipedia access logs are 5TB of compressed text; we're working on efficient ways to process and store time series. Our work received significant media coverage and was highlighted as LANL's best science story of 2014.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Enabling Exascale Hardware and Software Design through Scalable System Virtualization (Final Report)

The purpose of this project has been to extend the state of the art of systems software for high-end computing (HEC) platforms, and to use systems software to better enable the evaluation of potential future HEC platforms, for example exascale platforms. Such platforms, and their systems software, have the goal of providing scientific computation at new scales, thus enabling new research in the physical sciences and engineering. Over time, the innovations in systems software for such platforms also become applicable to more widely used computing clusters, data centers, and clouds. This was a five-institution project, centered on the Palacios virtual machine monitor (VMM) systems software, a project begun at Northwestern, and originally developed in a previous collaboration between Northwestern University and the University of New Mexico. In this project, Northwestern (including via our subcontract to the University of Pittsburgh) contributed to the continued development of Palacios, along with other team members. We took the leadership role in (1) continued extension of support for emerging Intel and AMD hardware, (2) integration and performance enhancement of overlay networking, (3) connectivity with architectural simulation, (4) binary translation, and (5) support for modern Non-Uniform Memory Access (NUMA) hosts and guests. We also took a supporting role in support for specialized hardware for I/O virtualization, profiling, configurability, and integration with configuration tools. The efforts we led (1-5) were largely successful and executed as expected, with code and papers resulting from them. The project demonstrated the feasibility of a virtualization layer for HEC computing, similar to such layers for cloud or datacenter computing. For effort (3), although a prototype connecting Palacios with the GEM5 architectural simulator was demonstrated, our conclusion was that such a platform was less useful for design space exploration than anticipated due to inherent complexity of the connection between the instruction set architecture level and the microarchitectural level. For effort (4), we found that a code injection approach proved to be more fruitful. The results of our efforts are publicly available in the open source Palacios codebase and published papers, all of which are available from the project web site, v3vee.org. Palacios is currently one of the two codebases (the other being Sandia’s Kitten lightweight kernel) that underlies the node operating system for the DOE Hobbes Project, one of two projects tasked with building a systems software prototype for the national exascale computing effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Implementation, capabilities, and benchmarking of Shift, a massively parallel Monte Carlo radiation transport code

This paper discusses the implementation, capabilities, and validation of Shift, a massively parallel Monte Carlo radiation transport package developed and maintained at Oak Ridge National Laboratory. It has been developed to scale well from laptop to small computing clusters to advanced supercomputers. Special features of Shift include hybrid capabilities for variance reduction such as CADIS and FW-CADIS, and advanced parallel decomposition and tally methods optimized for scalability on supercomputing architectures. Shift has been validated and verified against various reactor physics benchmarks and compares well to other state-of-the-art Monte Carlo radiation transport codes such as MCNP5, CE KENO-VI, and OpenMC. Some specific benchmarks used for verification and validation include the CASL VERA criticality test suite and several Westinghouse AP1000 ® problems. These benchmark and scaling studies show promising results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

TU-AB-BRC-12: Optimized Parallel MonteCarlo Dose Calculations for Secondary MU Checks

Purpose: Secondary MU checks are an important tool used during a physics review of a treatment plan. Commercial software packages offer varying degrees of theoretical dose calculation accuracy, depending on the modality involved. Dose calculations of VMAT plans are especially prone to error due to the large approximations involved. Monte Carlo (MC) methods are not commonly used due to their long run times. We investigated two methods to increase the computational efficiency of MC dose simulations with the BEAMnrc code. Distributed computing resources, along with optimized code compilation, will allow for accurate and efficient VMAT dose calculations. Methods: The BEAMnrc package was installed on a high performance computing cluster accessible to our clinic. MATLAB and PYTHON scripts were developed to convert a clinical VMAT DICOM plan into BEAMnrc input files. The BEAMnrc installation was optimized by running the VMAT simulations through profiling tools which indicated the behavior of the constituent routines in the code, e.g. the bremsstrahlung splitting routine, and the specified random number generator. This information aided in determining the most efficient compiling parallel configuration for the specific CPU’s available on our cluster, resulting in the fastest VMAT simulation times. Our method was evaluated with calculations involving 10{sup 8} – 10{sup 9} particle histories which are sufficient to verify patient dose using VMAT. Results: Parallelization allowed the calculation of patient dose on the order of 10 – 15 hours with 100 parallel jobs. Due to the compiler optimization process, further speed increases of 23% were achieved when compared with the open-source compiler BEAMnrc packages. Conclusion: Analysis of the BEAMnrc code allowed us to optimize the compiler configuration for VMAT dose calculations. In future work, the optimized MC code, in conjunction with the parallel processing capabilities of BEAMnrc, will be applied to provide accurate and efficient secondary MU checks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Illustration of VERA Capabilities to Model a Typical iPWR SMR

Small modular reactors (SMRs) are being considered by the commercial nuclear power industry as an option for more distributed generation and for replacement of older fossil fuel generating facilities. SMRs are more compact than operating pressurized water reactors (PWRs), producing about 200 MWe as compared to 1000 MWe or higher for their full-sized cousins, and are offered as “expandable” units; that is, their modular design allows the utility to add more units progressively at the same site. In Summer 2014, CASL reported on a progression of demonstrations illustrating VERA’s ability to simulate a typical SMR of the iPWR type, while developing a simple core cycle design for cycle economic studies. The work included development of fuel assembly lattices, 3D single assembly and full core (quarter symmetry) simulations of the proposed core cycle design using the CASL Virtual Environment for Reactor Applications (VERA). The report concluded that it would be difficult for the SMR to operate for the target 1400 EFPD cycle length and that the fuel cost would be on the order of 50% higher as compared with a PWR. Unfortunately, the Reference 4 core cycle design did not include control component management. The iPWR is intended to be operated using the control rods for reactivity control, as opposed to using soluble boron (like most currently operating PWRs), and the introduction of the control rods is expected to affect the length of the cycle. Therefore, this work continues the previous CASL demonstration of VERA capability to simulate a typical iPWR core, including the development of a control component management strategy using the Reference 4 core design and utilizing the soon-to-be released VERA3.3 tools. Note that this study is not intended as a validation study; rather it is merely an illustration of VERA’s capabilities to simulate a typical iPWR. Simulations included two variations on control rod banks – a two-bank scheme and a ten-bank scheme. Both schemes were modelled using the VERA Common Input, and more than six iterations on each control rod scheme were simulated to arrive at a combination of rod banks and insertion depths that provided a core reactivity near the target k eff of 1.000 ± 0.001 (more than 50 cases run) over two calendar months. In particular, VERA was used to determine the hot excess reactivity for the core design with all rods out (ARO), and then iterative simulations with the rod banks inserted to determine the critical control rod bank positions were completed. VERA options to simplify the process were exercised as possible, particularly restart functionality, coupled thermal-hydraulics using the internal T-H solver, xenon, and depletion capability. The fuel shuffling functionality was not exercised, since the iPWR SMR features a once-through cycle. The VERA method of characteristics (MOC) neutronics subcomponent was used to predict power and reactivity with control rod maneuvering, both with and without thermal-hydraulic feedback. The problem was scaled from 35 CPU cores to 3815, effectively demonstrating the solution time with respect to computing capacity. Computer clusters at Oak Ridge National Laboratory, Idaho National Laboratory and Tennessee Valley Authority were used. These studies provide an additional illustration of the application of VERA to a typical iPWR SMR. Reference 4 previously demonstrated that the codes could handle the differences in geometry for the iPWR, and this study expanded the illustration to include control rod management. Further, the work illustrates the need for in-depth simulations to support effective control rod maneuvering for an iPWR and highlights the need for control rod reactivity search capability within VERA. Additionally, the development of a control rod bank reactivity search capability will be extremely useful for not only this application, but also for BWRs as CASL develops BWR capability in Phase 2.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Porting a 3D seismic modeling code (SW4) to CORAL machines

Seismic waves fourth order (SW4) solves the seismic wave equations on Cartesian and curvilinear grids using large compute clusters with O (100,000) cores. In this article we discuss the porting of SW4 to run on the CORAL architecture using the RAJA performance portability abstraction layer. The performances of key kernels using RAJA and CUDA are compared to estimate the performance penalty of using the portability abstraction layer. Code changes required for efficiency on GPUs and minimizing time spent in Message Passing Interface (MPI) are discussed. This article describes a path for efficiently porting large code bases to GPU-based machines while avoiding the pitfalls of a new architecture in the early stages of its deployment. Current bottlenecks in the code are discussed along with possible architectural or software mitigations. SW4 runs 28× faster on one 4-GPU CORAL node than on a CTS-1 node (Dual Intel Xeon E5-2695 v4). SW4 is now in routine use on problems of unprecedented resolution (203 billion grid points) and scale on 1,200 nodes of Summit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Parallel Application Performance on Two Generations of Intel Xeon HPC Platforms

Two next-generation node configurations hosting the Haswell microarchitecture were tested with a suite of microbenchmarks and application examples, and compared with a current Ivy Bridge production node on NREL" tm s Peregrine high-performance computing cluster. A primary conclusion from this study is that the additional cores are of little value to individual task performance--limitations to application parallelism, or resource contention among concurrently running but independent tasks, limits effective utilization of these added cores. Hyperthreading generally impacts throughput negatively, but can improve performance in the absence of detailed attention to runtime workflow configuration. The observations offer some guidance to procurement of future HPC systems at NREL. First, raw core count must be balanced with available resources, particularly memory bandwidth. Balance-of-system will determine value more than processor capability alone. Second, hyperthreading continues to be largely irrelevant to the workloads that are commonly seen, and were tested here, at NREL. Finally, perhaps the most impactful enhancement to productivity might occur through enabling multiple concurrent jobs per node. Given the right type and size of workload, more may be achieved by doing many slow things at once, than fast things in order.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Automated pipeline framework for processing of large-scale building energy time series data

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

36 MATERIALS SCIENCE↗