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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Near-optimal routing of noisy quantum states

Placement, routing, and scheduling are essential tasks for near-optimal performance of programs for noisy quantum processors. Reliable execution of an arbitrary quantum circuit on current devices requires routing methods that overcome connectivity limitations while meeting data locality requirements. However, current devices also express highly variable noise levels in both the quantum gates and quantum registers. This requires any routing algorithm to be adaptive to both the circuit and the operating conditions. We demonstrate near-optimal routing methods of noisy quantum states that minimize the overall error of data movement while also limiting the computational complexity of routing decisions. We evaluate our methods against the noise characteristics of a 20-qubit superconducting quantum processor.

Sadlier III, Ronald↗
DOE OSTI2018

Final Technical Report: Commercial Advanced Lighting Control (ALC) Demonstration and Deployment

This three-year demonstration and deployment project sought to address market barriers to accelerating the adoption of Advanced Lighting Controls (ALCs), an underutilized technology with low market penetration. ALCs are defined as networked, addressable lighting control systems that utilize software or intelligent controllers to combine multiple energy-saving lighting control strategies in a single space (e.g., smart-time scheduling, daylight harvesting, task tuning, occupancy control, personal control, variable load-shedding, and plug-load control). The networked intelligent aspect of these systems allows applicable lighting control strategies to be combined in a single space, layered over one another, maximizing overall energy-savings. The project included five real building demonstrations of ALCs across the Northeast US region. The demonstrations provided valuable data and experience to support deployment tasks that are necessary to overcome market barriers. These deployment tasks included development of training resources for building designers, installers, and trades, as well as development of new energy efficiency rebates for the technology from Efficiency Forward’s utility partners. Educating designers, installers, and trades on ALCs is a critical task for reducing the cost of the technology that is currently inflated due to perceived complexity and unfamiliarity with how to design and install the systems. Further, utility and non-utility energy efficiency programs continue to relegate the technology to custom or ill-suited prescriptive program designs that do not effectively deploy the technology at scale. This project developed new, scalable rebate approaches for the technology. Efficiency Forward utilized their DesignLights Consortium® (DLC) brand and network of 81 DLC member utilities to develop and deploy the results of the project. The outputs of the project have included five published case studies, a six-hour ALC technology training curriculum that has already been deployed in five US states, and new rebates offered for the technology that have been deployed by a dozen utilities across the US. Widespread adoption of ALC technology in commercial buildings would provide tremendous benefits. The current market penetration of ALC systems is estimated at <0.1% in commercial buildings. If ALC systems were installed in all commercial buildings, approximately 1,051 TBtu of energy could be saved. This would translate into customer cost savings of approximately $10.7 billion annually.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Reported Energy and Cost Savings from the DOE ESPC IDIQ Program: FY2021

The objective of this work was to determine the realization rate of energy and cost savings from the U.S. Department of Energy’s (DOE’s) Energy Savings Performance Contract (ESPC) program based on information reported by the energy services companies (ESCOs) that are carrying out ESPC projects at federal sites. Information was extracted from 201 measurement and verification (M&V) reports covering 193 projects to determine reported, estimated, and guaranteed cost savings and the associated reported and estimated energy savings for the previous contract performance year. This report covers projects that had a performance year ending in fiscal year 2021, between October 1, 2020 and September 30, 2021, and had an M&V report issued. Additionally, the annual cost to perform M&V was extracted from the individual project Task Order (TO) Schedules.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Reported Energy and Cost Savings from the DOE ESPC IDIQ Program: FY 2022

The objective of this work was to determine the realization rate of energy and cost savings from the U.S. Department of Energy’s (DOE’s) Energy Savings Performance Contract (ESPC) program based on information reported by the energy services companies (ESCOs) that are carrying out ESPC projects at federal sites. Information was extracted from 190 measurement and verification (M&V) reports covering 182 projects to determine reported, estimated, and guaranteed cost savings and the associated reported and estimated energy savings for the previous contract performance year. This report covers projects that had a performance year ending in fiscal year 2021, between October 1, 2021 and September 30, 2022, and had an M&V report issued. Additionally, the annual cost to perform M&V was extracted from the individual project Task Order (TO) Schedules.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Reported Energy and Cost Savings from the DOE ESPC IDIQ Program: FY 2023

The objective of this work was to determine the realization rate of energy and cost savings from the U.S. Department of Energy’s (DOE’s) Energy Savings Performance Contract (ESPC) program based on information reported by the energy services companies (ESCOs) that are carrying out ESPC projects at federal sites. Information was extracted from 201 measurement and verification (M&V) reports covering 191 projects to determine reported, estimated, and guaranteed cost savings and the associated reported and estimated energy savings for the previous contract performance year. This report covers projects that had a performance year ending in fiscal year 2023, between October 1, 2022 and September 30, 2023, and had an M&V report issued. Additionally, the annual cost to perform M&V was extracted from the individual project Task Order (TO) Schedules.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

MARS: Malleable Actor-Critic Reinforcement Learning Scheduler

In this paper, we introduce MARS, a new scheduling system for HPC-cloud infrastructures based on a cost-aware, flexible reinforcement learning approach, which serves as an intermediate layer for next generation HPC-cloud resource manager. MARS ensembles the pre-trained models from heuristic workloads and decides on the most cost-effective strategy for optimization. A whole workflow application would be split into several optimizable dependent sub-tasks, then based on the pre- defined resource management plan, a reward will be generated after executing a scheduled task. Lastly, MARS updates the Deep Neural Network (DNN) model based on the reward. MARS is designed to optimize the existing models through reinforcement mechanisms. MARS adapts to the dynamics of workflow applications, selects the most cost-effective scheduling solution among pre-built scheduling strategies (backfilling, SJF, etc.) and self- learning deep neural network model at run-time. We evaluate MARS with different real-world workflow traces. MARS can achieve 5%-60% increased performance compare to state-of-the- art approaches.

Baheri, Betis↗
DOE OSTI2018

Symmetric Indefinite Linear Solver Using OpenMP Task on Multicore Architectures

Recently, the Open Multi-Processing (OpenMP) standard has incorporated task-based programming, where a function call with input and output data is treated as a task. At run time, OpenMP's superscalar scheduler tracks the data dependencies among the tasks and executes the tasks as their dependencies are resolved. On a shared-memory architecture with multiple cores, the independent tasks are executed on different cores in parallel, thereby enabling parallel execution of a seemingly sequential code. With the emergence of many-core architectures, this type of programming paradigm is gaining attention-not only because of its simplicity, but also because it breaks the artificial synchronization points of the program and improves its thread-level parallelization. In this paper, we use these new OpenMP features to develop a portable high-performance implementation of a dense symmetric indefinite linear solver. Obtaining high performance from this kind of solver is a challenge because the symmetric pivoting, which is required to maintain numerical stability, leads to data dependencies that prevent us from using some common performance-improving techniques. To fully utilize a large number of cores through tasking, while conforming to the OpenMP standard, we describe several techniques. Our performance results on current many-core architectures-including Intel's Broadwell, Intel's Knights Landing, IBM's Power8, and Arm's ARMv8-demonstrate the portable and superior performance of our implementation compared with the Linear Algebra PACKage (LAPACK). The resulting solver is now available as a part of the PLASMA software package.

Computer Science↗
DOE OSTI2023

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning

Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimization problem that remains unsolved. The effectiveness of Reinforcement Learning (RL) as a valuable paradigm for training autonomous systems has been well-demonstrated, and it may provide the basis for self-driving telescopes capable of optimizing the scheduling for astronomy campaigns. Simulated datasets containing examples of interactions between a telescope and a discrete set of sky locations on the celestial sphere can be used to train an RL model to sequentially gather data from these several locations to maximize a cumulative reward as a measure of the quality of the data gathered. We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO). We combine multiple improvements on the DQN and adjustments to the dataset, showing that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Well-Controlled Hydro-thermo-chemical Stimulation of a Coso Geothermal Well to Enhance Productivity

The goal of this project was to demonstrate controlled fracturing and permeability increase to enhance resource production and power generation at Coso. It was planned to stimulate at least two intervals in the Well 73-18RD with zonal isolation using advanced high-temperature packer technology. The anticipation was that a hydraulic fracture would form in one zone and in the second zone natural fractures would reopen, shear and extend, thereby increasing fluid communication in the field. In both cases, the intent was to carefully design the stimulation to take advantage of the relative roles of hydraulic pressure, thermal stress and chemical (“hydro-thermo-chemical”) dissolution. The project had planned to use the Welltec Annular Barrier (WAB) system developed as part of another DOE-funded project which was on schedule to be ready for field deployment. If successful, the Coso Operating Company would incorporate this approach into their ongoing well maintenance in other wells increasing the total MWs produced. The project had multiple tasks and the objective of Task 1 was to develop a workover plan, to build a geologic model, to design a stimulation program, and to work with the operating and services companies to develop a detailed budget and obtain the necessary permits and sundry notices. In Phase 2 the workover plan would be implemented, the designed stimulation would be carried out, and subsequently the well would logged and tested to measure the impact of stimulation. Per sponsor request, a Pre-Task 1 activity with a Go/No Go decision point was scheduled in Task 1.

02 PETROLEUM↗
DOE OSTI2022

NARAC Meteorological Monitoring Tower Maintenance Report: Submarine Base Group Two, Sub Base New London NEWLBASE2 Tower

Comments: The roof-based tower is located on Building 17 on Naval Submarine Base New London. Tower installation date is unknown. A second GFCI outlet in-line with the standard GFCI in the logger enclosure is mounted on conduit out of the tower base pedestal. The roof surface has been replaced and is now safe to walk on. The tower was not climbed during this maintenance visit. After the maintenance visit in 2018, the tower was deemed unsafe to climb due to age, corrosion, and deterioration of the tower components. The wind, air temperature, and relative humidity sensors located on the upper part of the tower require climbing of the tower for servicing and could not be accessed for inspection and maintenance. The tower is scheduled for replacement in FY2023. Tasks to be Completed: Tower system replacement / relocation. Consider replacement or bypassing of the suspect precipitation gauge signal connector.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

URSI ORISE Final Reflection

When I joined the US ITER project for my internship, the ITER Organization (IO) in France had started working on rebaselining – changing major aspects of the scope, schedule, and/or cost. My main task at US ITER was to interview US ITER, ORNL, ITER Organization (IO), ASDEX, WEST, and Sandia National Laboratory (SNL) staff. I reviewed with them the proposed changes in the rebaselining, listened to their concerns and comments, took notes, and asked questions. To become familiar with the IO’s proposed rebaseline, I analyzed reports, studies, presentations, and graphs. I also attended conferences and webinars, both domestic and international. I compiled all the information and contrasting opinions into one presentation to present to US ITER staff.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

IRIS: Exploring Performance Scaling of the Intelligent Runtime System and its Dynamic Scheduling Policies

High-Performance Computing is becoming increasingly heterogeneous, relying on a diverse mix of hardware to achieve good performance. Paradoxically, current drivers and frameworks for these devices typically require separate languages and implementations for each vendor. Furthermore, there are few tools and little support to schedule codes between these devices in a truly heterogeneous manner-partly because of this fragmentation between vendors and the languages each supports. To overcome both limitations, the Intelligent Runtime System (IRIS) was developed. It allows a common task abstraction to automatically be shared among contemporary vendors and is run from a single host-side API. At runtime, IRIS queries the host system and registers which frameworks and drivers are available, these determine which kernels can be used by the scheduler-CPUs via OpenMP, Nvidia GPUs (CUDA), AMD GPUs (HIP), and Intel and Xilinx FPGAs with OpenCL. IRIS enables tasks to be scheduled to any heterogeneous device and resolves to the appropriate kernel binary at runtimeit only uses the devices supported by the system on which it is run. IRIS supports single-task and graph-based expressions of dependencies of tasks. Additionally, IRIS features a range of dynamic scheduling policies, allowing complex chains of tasks and interactions to be executed, relieving the programmer/user from considering the system to assign tasks to devices optimally. This paper presents the peak performance attainable by IRIS over a range of systems-each with different numbers and types of accelerator devices, it highlights the flexibility of IRIS since these devices are truly heterogeneous, relying on different backends (drivers, frameworks, and languages) which historically required unique implementations to utilize them. We then use this peak performance as a baseline to compare increasingly complex chains of tasks (with increasingly complex task dependencies) and evaluate how IRIS copes. Finally, we consider the performance of different IRIS scheduling policies on this range of task graphs.

Johnston, Beau↗
DOE OSTI2019

Extracting SIMD Parallelism from Recursive Task-Parallel Programs

The pursuit of computational efficiency has led to the proliferation of throughput-oriented hardware, from GPUs to increasingly wide vector units on commodity processors and accelerators. This hardware is designed to efficiently execute data-parallel computations in a vectorized manner. However, many algorithms are more naturally expressed as divide-and-conquer, recursive, task-parallel computations. In the absence of data parallelism, it seems that such algorithms are not well suited to throughput-oriented architectures. This paper presents a set of novel code transformations that expose the data parallelism latent in recursive, task- parallel programs. These transformations facilitate straightforward vectorization of task-parallel programs on commodity hardware. We also present scheduling policies that maintain high utilization of vector resources while limiting space usage. Across several task-parallel benchmarks, we demonstrate both efficient vector resource utilization and substantial speedup on chips using Intel’s SSE4.2 vector units, as well as accelerators using Intel’s AVX512 units. We then show through rigorous sampling that, in practice, our vectorization techniques are effective for a much larger class of programs.

Recursive Programs, task parallelism, vectorizatio↗
DOE OSTI2017

Task-based Parallel Computation of the Density Matrix in Quantum-based Molecular Dynamics using Graph Partitioning

Quantum-based molecular dynamics (QMD) is a highly accurate and transferable method for material science simulations. However, the time scales and system sizes accessible to QMD are typically limited to picoseconds and a few hundred atoms. These constraints arise due to expensive self-consistent ground-state electronic structure calculations that can often scale cubically with the number of atoms. Linearly scaling methods depend on computing the density matrix P from the Hamiltonian matrix H by exploiting the sparsity in both matrices. The second-order spectral projection (SP2) algorithm is an O(N) algorithm that computes P with a sequence of 40 - 50 matrix-matrix multiplications. In this paper, we present task-based implementations of a recently developed data-parallel graph-based approach to the SP2 algorithm, G-SP2. We represent the density matrix P as an undirected graph and use graph partitioning techniques to divide the computation into smaller independent tasks. The partitions thus obtained are generally not of equal size and give rise to undesirable load imbalances in standard MPI-based implementations. This load-balancing challenge can be mitigated by dynamically scheduling parallel computations at runtime using task-based programming models. We develop task-based implementations of the data-parallel G-SP2 algorithm using both Intel's Concurrent Collections (CnC) as well as the Charm++ programming model and evaluate these implementations for future use. Scaling and performance results of our implementations are investigated for representative segments of QMD simulations for solvated protein systems containing more than 10,000 atoms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Wood-to-Fuel for California's Transportation Sector using Autothermal Pyrolysis (Progress Report)

We sent our updated draft Site License Agreement back to SPI on June 4 th , with redlines. Gary Blanc of SPI said he would circulate it internally to appropriate parties and get back to us. He did not return an accepted or redlined version to us before the end of the month of July, nor did he send us any questions/comments or updates, despite them being requested. He is currently traveling around visiting SPI’s various forestry locations and mills, so is difficult to reach. In working towards the commercialization strategy, during the month of July, further research was completed on understanding and evaluating the availability and potential availability of fire clearing forestry residue in California. In July, a meeting with Stephen Kaffka and Rob Williams who are both authors of the report “An Assessment of Biomass Resource in California, 2013” was attended. These individuals provided us with guidance and information about how to identify the availability and potential availability of forestry/wood resource biomass in California. Also, in the month of July, a meeting with Cal-Fire was attended in order to understand what Cal-Fire plans to do in terms of reducing the risk of wildfires and how much potential fire clearing forestry residue biomass this will produce for California. Additionally, the potential of a novel pathway of producing H2 from pyrolysis oil, maximizing the products’ value, as well as maximizing the carbon emission reduction potential by incorporating carbon capture onsite, was investigated. We continued with the process of subcontracting with Iowa State University and Frontline BioEnergy. We have our Contract Administrators working with their counterparts at each partner to get the details in place. Draft contracts with statements of work were provided to each partner, and updated schedules and budgets, broken down by task in the statements of work, have been requested from each.

09 BIOMASS FUELS↗
DOE OSTI2019

Improving the Scheduling Efficiency of a Global Multi-Core HTCondor Pool in CMS

Scheduling multi-core workflows in a global HTCondor pool is a multi-dimensional problem whose solution depends on the requirements of the job payloads, the characteristics of available resources, and the boundary conditions such as fair share and prioritization imposed on the job matching to resources. Within the context of a dedicated task force, CMS has increased significantly the scheduling efficiency of workflows in reusable multi-core pilots by various improvements to the limitations of the GlideinWMS pilots, accuracy of resource requests, efficiency and speed of the HTCondor infrastructure, and job matching algorithms.

Bockelman, Brian Paul↗
DOE OSTI2024

Quantum/AI Topology-Aware Latency-Adaptive HPC Workflow Scheduling Optimization

The growing demand for more powerful high-performance computing (HPC) systems has led to a steady rise in energy consumption by supercomputing worldwide. This study is focused on comparing our Application-Topology Mapper (ATMapper) to the popular Simple Linux Utility for Resource Management (SLURM) for the purpose of exploring methods that can further optimize job-scheduling within HPC systems. ATMapper is an Artificial-Intelligence based approach to job-scheduling that is currently being enhanced with quantum annealing (QA) to generate optimal schedules faster. We are applying QA to speedup our ATMapper process to achieve higher computing efficiency, thereby reducing HPC energy consumption. Here, we examine how four job-scheduling approaches perform in processor node assignment when using an example network architecture of 4 interconnected nodes. Using a specialized script, we are assessing the schedule of a computation flow with 11 interdependent tasks. The data movements among nodes were tracked to count for the number of interactions (network hops) between nodes needed to complete the tasks. The total number of hops and the job completion time were then used to quantify the efficiency of the different mapping approaches. In addition to SLURM, we also compare our ATMapper to the QA-enabled LBNL TIGER and the D-Wave Distributed Computing processor assignment approaches. The preliminary results showed that our topology-aware, latency-adaptive ATMapper is significantly more efficient when compared to the other scheduling approaches due to its load-imbalance network allocation. The scheduler displayed a computing efficiency of 53% by performing significantly fewer network hops than its alternatives. By reducing the number of hops, ATMapper was able to perform all 11 tasks by using only 3 nodes out of given 4. This research indicates the potential to use QA/AI for HPC job-scheduling. Later, we will test a SLURM simulator program to draw further comparisons on the effectiveness of ATMapper's scheduling approach. The results of this comparison will serve as a baseline for later improving SLURM's performance using a QA-enhanced ATMapper approach.

Caraveo, Braulio [University of Huston - Clear Lak↗