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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Workshop Report: 2021 International Symposium of Quantitative Codesign of Supercomputers

This report provides general information about the First International Symposium on the Quantitative Design of Supercomputers (SQCS). This new workshop was held in conjunction with Supercomputing ’21 on November 19, 2021, in St Louis Missouri. The symposium aims at combining two methodologies—collaborative codesign and data-driven analysis—to realize the potential of supercomputing more fully. We refer to the design solutions that rely on intelligence from data-driven insights across applications, systems, system software, workflows, and facilities as quantitative codesign of supercomputers.

Jones, Terry↗
DOE OSTI2022

Workshop Report: 2022 International Symposium of Quantitative Codesign of Supercomputers

This report provides general information about the Second International Symposium on the Quantitative Design of Supercomputers (SQCS). This new workshop was held in conjunction with Supercomputing ’22 on November 13, 2022, in Dallas Texas. The symposium aims at combining two methodologies—collaborative codesign and data-driven analysis—to realize the potential of supercomputing more fully. We refer to the design solutions that rely on intelligence from data-driven insights across applications, systems, system software, workflows, and facilities as quantitative codesign of supercomputers.

Jones, Terry↗
DOE OSTI2024

Thermo-Fluid Modeling Framework for Supercomputing Digital Twins: Part 2, Automated Cooling Models

The development of digital twins for the purpose of improving the energy efficiency of supercomputing facilities is a non-trivial endeavor that is complicated by the difficulty of creating physics-based thermo-fluid cooling system models (CSMs). Within ExaDigit---an open-source framework for liquid-cooled supercomputing digital twins---a thermo-fluid modeling framework is being developed. This effort has been segmented into two with two companion papers describing each portion of the overall effort. Part 1 focuses on the development of a cooling system library in Dymola for the Frontier supercomputer at Oak Ridge National Laboratory {\cite{Kumar2024}. Part 2, this paper, describes an effort to create a template-based auto-generation methodology for CSMs, called \textit{AutoCSM}. In this paper, an overview of the initial AutoCSM architecture and workflow is provided, along with a practical example using the Oak Ridge Leadership Computing Facility's (OLCF) Frontier supercomputer CSM. AutoCSM will (1) improve ExaDigiT's user accessibility by providing a flexible workflow for modularizing the creation of the CSM system and control logic, (2) decrease the development time of CSMs, and (3) standardize the method for incorporating CSMs into the ExaDigiT framework.

Greenwood, Scott↗
DOE OSTI2024

Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. Here in this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Qualifying for the Green500: Experience with the newest generation of supercomputers at LANL

The High Performance Computing Division of Los Alamos National Laboratory recently brought four new supercomputing platforms on line: Trinity with separate partitions built around the Haswell and Knights Landing CPU architectures for capability computing and Grizzly, Fire, and Ice for capacity computing applications. The power monitoring infrastructure of these machines is significantly enhanced over previous supercomputing generations at LANL and all were qualified at the highest level of the Green500 benchmark. Here, this paper discusses supercomputing at LANL, the Green500 benchmark, and notes on our experience meeting the Green500's reporting requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Spatiotemporal modeling of node temperatures in supercomputers

Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power (~500-2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the supercomputer becomes a critical and expensive endeavor. Recently a project was initiated to investigate the effect that changes to the cooling system in a machine room had on three large machines that were housed there. Coupled with this goal was the aim to develop a general good-practice for characterizing the effect of cooling changes and monitoring machine node temperatures in this and other machine rooms. This paper focuses on the statistical approach used to quantify the effect that several cooling changes to the room had on the temperatures of the individual nodes of the computers. The largest cluster in the room has 1,600 nodes that run a variety of jobs during general use. Since extremes temperatures are important, a Normal distribution plus generalized Pareto distribution for the upper tail is used to model the marginal distribution, along with a Gaussian process copula to account for spatio-temporal dependence. A Gaussian Markov random field (GMRF) model is used to model the spatial effects on the node temperatures as the cooling changes take place. This model is then used to assess the condition of the node temperatures after each change to the room. The analysis approach was used to uncover the cause of a problematic episode of overheating nodes on one of the supercomputing clusters. Lastly, this same approach can easily be applied to monitor and investigate cooling systems at other data centers, as well.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Integration of PanDA workload management system with Titan supercomputer at OLCF

The PanDA (Production and Distributed Analysis) workload management system (WMS) was developed to meet the scale and complexity of LHC distributed computing for the ATLAS experiment. While PanDA currently distributes jobs to more than 100,000 cores at well over 100 Grid sites, the future LHC data taking runs will require more resources than Grid computing can possibly provide. To alleviate these challenges, ATLAS is engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF). The current approach utilizes a modified PanDA pilot framework for job submission to Titan’s batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on Titan’s multi-core worker nodes. It also gives PanDA new capability to collect, in real time, information about unused worker nodes on Titan, which allows precise definition of the size and duration of jobs submitted to Titan according to available free resources. This capability significantly reduces PanDA job wait time while improving Titan’s utilization efficiency. This implementation was tested with a variety of Monte-Carlo workloads on Titan and is being tested on several other supercomputing platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Porting Ordinary Applications to Blue Gene/Q Supercomputers

Efficiently porting ordinary applications to Blue Gene/Q supercomputers is a significant challenge. Codes are often originally developed without considering advanced architectures and related tool chains. Science needs frequently lead users to want to run large numbers of relatively small jobs (often called many-task computing, an ensemble, or a workflow), which can conflict with supercomputer configurations. In this paper, we discuss techniques developed to execute ordinary applications over leadership class supercomputers. We use the high-performance Swift parallel scripting framework and build two workflow execution techniques-sub-jobs and main-wrap. The sub-jobs technique, built on top of the IBM Blue Gene/Q resource manager Cobalt's sub-block jobs, lets users submit multiple, independent, repeated smaller jobs within a single larger resource block. The main-wrap technique is a scheme that enables C/C++ programs to be defined as functions that are wrapped by a high-performance Swift wrapper and that are invoked as a Swift script. We discuss the needs, benefits, technicalities, and current limitations of these techniques. We further discuss the real-world science enabled by these techniques and the results obtained.

Maheshwari, Ketan C.↗
DOE OSTI2020

Resource Management for Processing Wide Area Data Streams on Supercomputers

Modern scientific instruments generate enormous amount of data. Typically, the data collected from the instruments are stored in one or more files that are then moved to a distant supercomputer for processing. The final results are sent back to the user. In order to make effective use of the time on expensive instruments, experimenters want to process the data as they are generated. They want to stream the data from instruments' memory directly to a supercomputer's memory for analysis. Since the compute nodes in a supercomputer are not connected directly to the wide area network, the data streams need to be passed through intermediate gateway nodes. As opposed to the best effort file transfers, data streaming applications require resources at a specific time for a specific period. In this paper, we present a system model for enabling data streaming through gateway nodes and an algorithm to efficiently allocate gateway node resources along with compute nodes. We evaluate the algorithm using real-world traces on the Chameleon Cloud. The results show that our system can schedule compute and gateway resources efficiently for streaming analysis.

data streaming↗
DOE OSTI2021

SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.

42 ENGINEERING↗
DOE OSTI2021

SMC 2021 Data Challenge: Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU: RUR dataset is the job scheduler traces collected from the Titan supercomputer from 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected using resource Utilization Report (RUR), a Cray-developed resource-usage data collection and reporting system. It contains the usage information of its critical resources (CPU, Memory, GPU, and I/O) of each running job on Titan during that period (https://ieeexplore.ieee.org/abstract/document/8891001). It includes ProjectAreas as additional information, every job is associated with a project ID. TheProjectAreas.csv dataset provides a mapping of the project ID to its domain science. GPU dataset has information regarding GPU failure on Titan. There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has seven attributes, we provided a short description of these attributes in the ReadMe file. To learn more about this dataset, please refer to the git repository https://github.com/olcf/TitanGPULife and the related publication (https://ieeexplore.ieee.org/abstract/document/9355319).

42 ENGINEERING↗
DOE OSTI2019

The Origins of Matter on Near-Exascale Supercomputing

This LDRD addressed one of the central questions in physics today: the lack of antimatter relative to matter in the universe. If antimatter and matter existed in equal amounts they would have annihilated into radiation and we would not exist today. DOE is funding experiments to investigate processes that could lead to lepton number violation and the resulting neutrino-less double beta decay (0nbb) signal that may be observed (lepton number is a property of elementary particles similar to electric charge and lepton number violation would explain the matter-antimatter imbalance). However, even if the 0nbb decay is detected in these experiments, the physics mechanism that violates lepton number would not be known, as there are several candidates. Determining the appropriate mechanism from the experimental data requires theoretical calculations. Theoretical effort in the field has focused on long-range 0nbb decay mechanisms associated with the exchange of light neutrinos between two nucleons. An equally important set of mechanisms are associated with the exchange of heavy particles and short-range physics, including the exchange of massive Majorana neutrinos. Calculating the event rate for 0nbb decays for candidate theories involving heavy particle exchange requires large-scale Lattice simulations of the underlying fundamental theory of Quantum Chromodynamics (QCD). We have performed these simulations on the LLNL supercomputers and have calculated the necessary matrix elements of short-range operators that arise from the exchange of heavy mediators that contribute to 0nbb decay via the leading pion exchange diagrams. Our published results can now be used as input to effective field theory calculations to calculate the 0nbb decay rate for the nuclei involved in the various experimental detectors. In the process of this work we entered the 2018 Gordon Bell Prize competition (the highest prize in scientific supercomputing) with our innovative and highly optimized code and methods and were selected as one of the six international finalists. During this effort we helped with the bring-up of the new LLNL Sierra and Lassen supercomputers by identifying hardware and software problems. Aiming towards the next step of this research, involving the sub-leading nucleon-nucleon direct vertex, we have developed algorithms and codes that will play a significant role in future Lattice QCD calculations involving nucleon-nucleon interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

36 MATERIALS SCIENCE↗
NASA NTRS1985

A color graphics environment in support of supercomputer systems

An initial step in the integration of an upgrade of a VPS-32 supercomputer to 16 million 64-bit words, to be closely followed by a further upgrade to 32 million words, was to develop a graphics language commonality with other computers at the Langley Center. The power of the upgraded supercomputer is to users at individual workstations, who will aid in defining the direction for future expansions in both graphics software and workstation requirements for the supercomputers. The LAN used is an ETHERNET configuration featuring both CYBER mainframe and PDP 11/34 image generator computers. The system includes a film recorder for image production in slide, CRT, 16 mm film, 35 mm film or polaroid film images. The workstations have screen resolutions of 1024 x 1024 with each pixel being one of 256 colors selected from a palette of 16 million colors. Each screen can have up to 8 windows open at a time, and is driven by a MC68000 microprocessor drawing on 4.5 Mb RAM, a 40 Mb hard disk and two floppy drives. Input is from a keyboard, digitizer pad, joystick or light pen. The system now allows researchers to view computed results in video time before printing out selected data.

Smith-Taylor, R.↗
NASA NTRS1989

Computational models and resource allocation for supercomputers

There are several different architectures used in supercomputers, with differing computational models. These different models present a variety of resource allocation problems that must be solved. The computational needs of a program must be cast in terms of the computational model supported by the supercomputer, and this must be done in a way that makes effective use of the machine's resources. This is the resource allocation problem. The computational models of available supercomputers and the associated resource allocation techniques are surveyed. It is shown that many problems and solutions appear repeatedly in very different computing environments. Some case studies are presented, showing concrete computational models and the allocation strategies used.

Mauney, Jon↗
NASA NTRS1990

NASA's supercomputing experience

A brief overview of NASA's recent experience in supercomputing is presented from two perspectives: early systems development and advanced supercomputing applications. NASA's role in supercomputing systems development is illustrated by discussion of activities carried out by the Numerical Aerodynamical Simulation Program. Current capabilities in advanced technology applications are illustrated with examples in turbulence physics, aerodynamics, aerothermodynamics, chemistry, and structural mechanics. Capabilities in science applications are illustrated by examples in astrophysics and atmospheric modeling. Future directions and NASA's new High Performance Computing Program are briefly discussed.

Bailey, F. Ron↗
NASA NTRS1991

Input/output behavior of supercomputing applications

The collection and analysis of supercomputer I/O traces and their use in a collection of buffering and caching simulations are described. This serves two purposes. First, it gives a model of how individual applications running on supercomputers request file system I/O, allowing system designer to optimize I/O hardware and file system algorithms to that model. Second, the buffering simulations show what resources are needed to maximize the CPU utilization of a supercomputer given a very bursty I/O request rate. By using read-ahead and write-behind in a large solid stated disk, one or two applications were sufficient to fully utilize a Cray Y-MP CPU.

Miller, Ethan L.↗
NASA NTRS1992

File servers, networking, and supercomputers

One of the major tasks of a supercomputer center is managing the massive amount of data generated by application codes. A data flow analysis of the San Diego Supercomputer Center is presented that illustrates the hierarchical data buffering/caching capacity requirements and the associated I/O throughput requirements needed to sustain file service and archival storage. Usage paradigms are examined for both tightly-coupled and loosely-coupled file servers linked to the supercomputer by high-speed networks.

Moore, Reagan W.↗