Programming and Performance Visualization Tools
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Computer science↗
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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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Accelerating kinetic plasma simulations with machine-learning-generated initial conditions
Computational models of plasma technologies often solve for the system operating conditions by time-stepping an initial value problem to a quasi-steady solution. However, the strongly nonlinear and multi-timescale nature of plasma dynamics often necessitate millions, or even hundreds of millions, of steps to reach convergence, reducing the effectiveness of these simulations for computer-aided engineering. We consider acceleration of kinetic plasma simulations via data-driven machine-learning-generated initial conditions, which initialize the simulations close to their final quasi-steady-state, thereby reducing the number of steps to reach convergence. Three machine-learning models are developed to predict the density and ion kinetic profiles of capacitively coupled plasma discharges relevant to the microelectronics industry. The models are trained on kinetic simulations over a range of device operating frequencies and pressures. Best performance was observed when simulations were initialized with ion kinetic profiles generated by a convolutional neural network, reducing the mean number of steps to reach convergence by 17.1× when compared to initialization with a zero-dimensional global model. We also outline a workflow for continuous data-driven model improvement and simulation speedup, with the aim of generating sufficient data for full device digital twins.
Artificial neural networks↗
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↗
GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability
George Ostrouchov, Don Maxwell, Rizwan Ashraf, Mallikarjun Shankar, and James Rogers. 2020. GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20). Association for Computing Machinery, New York, NY, USA. Data and code for SC20 paper about Titan GPU reliability analysis: https://github.com/olcf/TitanGPULife. Includes R code to generate graphics for paper and additional analyses. See code/README for instructions. Includes original Titan GPU reliability data on over 100,000 collective hours of operation: data/titan.gpu.history.txt - history data, data/titan.service.txt - service nodes for exclusion. Includes output data files produced by code/TitanGPUmodel.Rmd: data/gc_full.csv - cleaned up data (see paper and R code); data/gc_summary_loc.csv - one record per GPU (variables: SN, time, nlife, nloc, last, col, row, cage, slot, node, max_loc_events, time_max_loc, dbe, dbe_loc, otb, otb_loc, out, batch, days, years, dead, dead_otb, dead_dbe) (see paper and R code). Includes .Rmd analysis document as TitanGPUmode.html. Includes Python code to process data/gc_full.csv into graphics from time-between-failure analyses: See code/tbf-analyses/README for instructions.
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TBAA20: Task-Based Algorithms and Applications
The new challenges posed by Exascale system architectures have resulted in difficulty achieving a desired scalability using traditional distributed memory runtimes. Task-based programming models show promise in addressing these challenges, providing application developers with a productive and performant approach to programming on next generation systems. Empirical studies show that task-based models can overcome load balancing issues that are inherent to traditional distributed memory runtimes, and that task-based runtimes perform comparably to those systems when balanced. This panel is designed to explore the advantages of task-based programming models on modern and future HPC systems from an industry, university, and national lab perspective. It aims at gathering application experts and proponents of these models to present concrete and practical examples of using task-based runtimes to overcome the challenges posed by Exascale system architectures. This report describes the objectives, activities, and outcomes of the panel TBAA: Task-Based Algorithms and Applications which was held at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 20) on November 18, 2020.
97 MATHEMATICS AND COMPUTING↗
Large-Scale Intelligent Intrusion Detection System
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42 ENGINEERING↗
NERSC & JGI Seek Elusive Proteins for Gene Editing
A video just released by the Supercomputing 2017 (SC17) conference shows how biologists at UC Berkeley and the Joint Genome Institute (JGI) – a Department of Energy Office of Science User Facility – are using NERSC, the Office of Science’s primary supercomputing center, to identify a class of enzymes capable of editing out genes responsible for disease. This revolutionary work requires petaflops of computing power to sift through billions of DNA sequences in the JGI data portals to identify proteins like Cas9 that, combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), can “edit” a genome. First discovered by researchers at UC Berkeley, the CRISPR-Cas9 system has the power to permanently correct disease-causing mutations in cells, animal models, and eventually human patients to cure certain types of cancer, cardiovascular disease, sickle cell disease, and retinal degeneration. Since the CRISPR system was first identified in the early 2000s, it has become the most promising technology for treating genetic disease in humans and enabling plants to survive stress caused by pests and the environment. The annual SC17 conference will take place November 12-17 in Denver, Colorado. The over 12,000 attendees from academia, government and industry are leaders in researching and delivering high performance computing, networking, data storage and analysis in support of scientific discovery. http://sc17.supercomputing.org
Berkeley Lab, LBNL↗
NERSC & JGI Seek Elusive Proteins for Gene Editing
A video just released by the Supercomputing 2017 (SC17) conference shows how biologists at UC Berkeley and the Joint Genome Institute (JGI) – a Department of Energy Office of Science User Facility – are using NERSC, the Office of Science’s primary supercomputing center, to identify a class of enzymes capable of editing out genes responsible for disease. This revolutionary work requires petaflops of computing power to sift through billions of DNA sequences in the JGI data portals to identify proteins like Cas9 that, combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), can “edit” a genome. First discovered by researchers at UC Berkeley, the CRISPR-Cas9 system has the power to permanently correct disease-causing mutations in cells, animal models, and eventually human patients to cure certain types of cancer, cardiovascular disease, sickle cell disease, and retinal degeneration. Since the CRISPR system was first identified in the early 2000s, it has become the most promising technology for treating genetic disease in humans and enabling plants to survive stress caused by pests and the environment. The annual SC17 conference will take place November 12-17 in Denver, Colorado. The over 12,000 attendees from academia, government and industry are leaders in researching and delivering high performance computing, networking, data storage and analysis in support of scientific discovery. http://sc17.supercomputing.org Producer: Jennifer Boyd, Boyd Productions DP: Damon Hennessey Audio: Richard K. Pooler Score: Michael Bacon Music
59 BASIC BIOLOGICAL SCIENCES↗
GASNet-EX Specification Collection (Rev. 2024.5.0)
GASNet-EX is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models in emerging exascale systems. It provides network-independent, high-performance communication primitives including Remote Memory Access (RMA) and Active Messages (AM). GASNet-EX is an evolution of the popular GASNet communication system, building upon over 20 years of lessons learned, and the primary goals are high performance, interface portability, and expressiveness. The library has been used to implement parallel programming models and libraries such as UPC, UPC++, Fortran coarrays, Legion, Chapel, and many others. This anthology collects together the four separate volumes that currently comprise the GASNet-EX specification, as of the 2024.5.0 release of GASNet-EX.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Trace Replay and Network Simulation Tool
TraceR is a trace reply tool built upon the ROSS-based CODES simulation framework. TraceR can be used for predicting network performances and understanding network behavior by simulating messaging in High Performance Computing applications on interconnection networks.
Acun, Bilge↗
Trace Replay and Network Simulation Tool
TraceR Is a trace replay tool built upon the ROSS-based CODES simulation framework. TraceR can be used for predicting network performance and understanding network behavior by simulating messaging In High Performance Computing applications on interconnection networks.
Jain, Nikhil↗
Trace Replay and Network Simulation Tool
TraceR is a trace reply tool built upon the ROSS-based CODES simulation framework. TraceR can be used for predicting network performances and understanding network behavior by simulating messaging in High Performance Computing applications on interconnection networks.
Acun, Bilge↗
SWNT Networks with Polythiophene Carboxylate Links for High-Performance Silicon Monoxide Electrodes
Carboxylated polythiophenes, such as poly[3-(potassium-4-butanoate) thiophene] (PPBT), play a critical role in securely connecting single-walled carbon nanotube (SWNT) electrical networks onto the surface of carbon-coated silicon monoxide (c-SiO x ). These connections are a function of the materials’ surface chemistries and resultant physical/chemical bonding through favorable molecular interactions. Specifically, the PPBT $π$-conjugated backbone and alkyl side chain carboxylate moieties (COO-), respectively, physically interact with the SWNT and c-SiO x carbon layer $π$-electron-rich surfaces, and chemically bind to surface hydroxyl (-OH) species of the c-SiO x electroactive materials to form a carboxylate bond. This approach effectively captured pulverized particles that form during battery operation and beneficially suppressed the thickness change that electrodes typically undergo. The resultant electrodes exhibited superior electrochemical performance, which was ascribed to stable SEI layer formation, reduced electrode resistance, and improved electrode kinetics. Moreover, electrodes fabricated by blending 30 wt % of c-SiO x with graphite using <3 wt % binder exhibited remarkable performance in both coin-type half-cell and pouch-type full-cell systems. Finally, the concept that introduces robust SWNT electrical networks with PPBT carboxylate linkages suggests a feasible approach for the design of practical, high-performance, and high-capacity anodes for battery applications.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Optimized Fibril Network Morphology by Precise Side‐Chain Engineering to Achieve High‐Performance Bulk‐Heterojunction Organic Solar Cells
Abstract A polymer fibril assembly can dictate the morphology framework, in forming a network structure, which is highly advantageous in bulk heterojunction (BHJ) organic solar cells (OSCs). A fundamental understanding of how to manipulate such a fibril assembly and its influence on the BHJ morphology and device performance is crucially important. Here, a series of donor–acceptor polymers, PBT1‐O, PBT1‐S, and PBT1‐C, is used to systematically investigate the relationship between molecular structure, morphology, and photovoltaic performance. The subtle atom change in side chains is found to have profound effect on regulating electronic structure and self‐assembly of conjugated polymers. Compared with PBT1‐O and PBT1‐S, PBT1‐C‐based OSCs show much higher photovoltaic performance with a record fill factor (FF) of 80.5%, due to the formation of optimal interpenetrating network morphology. Such a fibril network strategy is further extended to nonfullerene OSCs using a small‐molecular acceptor, which shows a high efficiency of 12.7% and an FF of 78.5%. The results indicate the formation of well‐defined fibrillar structure is a promising approach to achieving a favorable morphology in BHJ OSCs.
Liu, Tao↗
xGFabric: Coupling Sensor Networks and HPC Facilities with Private 5G Wireless Networks for Real-Time Digital Agriculture
Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time coupling of edge sensor networks with HPC simulations. This work presents xGFabric, an end-to-end system coupling sensor networks with HPC facilities through Private 5G networks. The prototype connects remote sensors via 5G network slicing to HPC systems, enabling real-time digital agriculture simulation.
Digital Agriculture↗
Game-Theoretic Strategies for Cyber-Physical Infrastructures Under Component Disruptions
In this work, networked infrastructures of recursively defined systems composed of discrete cyber and physical components are considered. The components of basic systems at the finest levels can be disrupted by cyber or physical means, and can be reinforced to survive at certain costs. A problem of ensuring the infrastructure performance is formulated as a game between a provider and an attacker, who probabilistically choose components to reinforce and attack, respectively. The disruptions of this infrastructure are characterized using the aggregate failure correlation function that specifies the conditional failure probability of the infrastructure given the failure of an individual system at that level. The survival probabilities of basic systems satisfy simple product-form, first-order differential equations expressed in terms of the multiplier functions. The utility functions of the provider and attacker are composed of the reward and cost terms, both expressed in terms of the component reinforcement and attack probabilities. The Nash equilibrium of this game is characterized, along with the sensitivity functions of the survival probabilities of basic systems that highlight their dependence individually on the cost-benefit terms, the correlation functions, and the multiplier functions. These results are illustrated using simplified models of a distributed cloud servers infrastructure, a 5G data network infrastructure, a high performance computing federation, and a smart energy grid infrastructure.
42 ENGINEERING↗