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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 OSTI2012

Simplifying Performance Analysis of Large-scale Adaptive Scientific Applications

Performance analysis of parallel scientific codes is becoming increasingly difficult due to the rapidly growing complexity of applications and architectures. Existing tools fall short in providing intuitive views that facilitate the process of performance debugging and tuning. In this paper, we exploit a recent idea of projecting and visualizing performance data on the communication and hardware domain for faster, more intuitive analysis of applications. We leverage several performance analysis and visualization tools to showcase the discovery of scalability bottlenecks in a structured AMR library. Using novel techniques to project per-phase timing data, application data, and communication data on a communication graph, we identify a previously elusive scaling bottleneck in the library. We present solutions that mitigate this problem, resulting in 22% improvement in the performance for a 65,536-core run on an IBM Blue Gene/P system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

This work examines the performance of leading-edge systems designed for machine learning computing, including the NVIDIA DGX-2, Amazon Web Services (AWS) P3, IBM Power System Accelerated Compute Server AC922, and a consumer-grade Exxact TensorEX TS4 GPU server. Representative deep learning workloads from the fields of computer vision and natural language processing are the focus of the analysis. Performance analysis is performed along with a number of important dimensions. Performance of the communication interconnects and large and high-throughput deep learning models are considered. Different potential use models for the systems as standalone and in the cloud also are examined. The effect of various optimization of the deep learning models and system configurations is included in the analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Traveler: Navigating Task Parallel Traces for Performance Analysis

Understanding the behavior of software in execution is a key step in identifying and fixing performance issues. This is especially important in high performance computing contexts where even minor performance tweaks can translate into large savings in terms of computational resource use. To aid performance analysis, developers may collect an execution trace —a chronological log of program activity during execution. As traces represent the full history, developers can discover a wide array of possibly previously unknown performance issues, making them an important artifact for exploratory performance analysis. However, interactive trace visualization is difficult due to issues of data size and complexity of meaning. Traces represent nanosecond-level events across many parallel processes, meaning the collected data is often large and difficult to explore. The rise of asynchronous task parallel programming paradigms complicates the relation between events and their probable cause. Here, to address these challenges, we conduct a continuing design study in collaboration with high performance computing researchers. We develop diverse and hierarchical ways to navigate and represent execution trace data in support of their trace analysis tasks. Through an iterative design process, we developed Traveler , an integrated visualization platform for task parallel traces. Traveler provides multiple linked interfaces to help navigate trace data from multiple contexts. We evaluate the utility of Traveler through feedback from users and a case study, finding that integrating multiple modes of navigation in our design supported performance analysis tasks and led to the discovery of previously unknown behavior in a distributed array library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Internet Performance Analysis of South Asian Countries Using End-to-End Internet Performance Measurements

Internet performance is highly correlated with key economic development metrics of a region. According to World Bank, the economic growth of a country increases 1.3% with a 10% increase in the speed of the Internet. Therefore, it is necessary to monitor and understand the performance of the Internet links in the region. It helps to figure out the infrastructural inefficiencies, poor resource allocation, and routing issues in the region. Moreover, it provides healthy suggestions for future upgrades. Therefore, the objective of this paper is to understand the Internet performance and routing infrastructure of South Asian countries in comparison to the developed world and neighboring countries using end-to-end Internet performance measurements. The South Asian countries comprise nearly 32% of the Internet users in Asia and nearly 16% of the world. The Internet performance metrics in the region are collected through the PingER framework. The framework is developed by the SLAC National Accelerator Laboratory, USA and is running for the last 20 years. PingER has 16 monitoring nodes in the region, and in the last year PingER monitors about 40 sites in South Asia using the ubiquitous ping facility. The collected data is used to estimate the key Internet performance metrics of South Asian countries. The performance metrics are compared with the neighboring countries and the developed world. Particularly, the TCP throughput of the countries is also correlated with different development indices. Further, worldwide Internet connectivity and routing patterns of the countries are investigated to figure out the inconsistencies in the region. Furthermore, the performance analysis revealed that the South Asia region is 7-10 years behind the developed regions of North America (USA and Canada), Europe, and East Asia.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Building a Community Infrastructure for Scalable On-Line Performance Analysis Tools around Open|SpeedShop

In this project we created a community tool infrastructure for program development tools targeting Petascale class machines and beyond. This includes tools for performance analysis, debugging, and correctness tools, as well as tuning and optimization frameworks. The developed infrastructure provides a comprehensive and extensible set of individual tool building components. We started with the basic elements necessary across all tools in such an infrastructure followed by a set of generic core modules that allow a comprehensive performance analysis at scale. Further, we developed a methodology and workflow that allows others to add or replace modules, to integrate parts into their own tools, or to customize existing solutions. In order to form the core modules, we built on the existing Open|SpeedShop infrastructure and decomposed it into individual modules that match the necessary tool components. At the same time, we addressed the challenges found in performance tools for petascale systems in each module. When assembled, this instantiation of community tool infrastructure provides an enhanced version of Open|SpeedShop, which, while completely different in its architecture, provides scalable performance analysis for petascale applications through a familiar interface. This project also built upon and enhances capabilities and reusability of project partner components as specified in the original project proposal. The overall project team’s work over the project funding cycle was focused on several areas of research, which are described in the following sections. The reminder of this report also highlights related work as well as preliminary work that supported the project. In addition to the project partners funded by the Office of Science under this grant, the project team included several collaborators who contribute to the overall design of the envisioned tool infrastructure. In particular, the project team worked closely with the other two DOE NNSA laboratories Los Alamos and Sandia leveraging co-funding for Krell by ASC’s Common Computing Environment (CCE) program as laid out in the original proposal. The ASC CCE co-funding, coordinated through LLNL, was for 50% of the total project funding, with the ASC CCE portion of the funding going entirely to Krell, while the ASCR funding itself was split between Krell and the funded partners. This report covers the entire project from both funding sources. Additionally, the team leveraged the expertise of software engineering researchers from Carnegie Mellon University, who specialize in software framework design, in order to achieve a broadly acceptable component framework. The Component Based Tool Framework (CBTF) software has been released to the community. Information related to the project and the released software can be found on the CBTF wiki page at: http://sourceforge.net/p/cbtf/wiki/Home.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

A mono-dimensional nuclear fuel performance analysis code, PUMA, development from a coupled approach

Multidimensional-multi-physical phenomena in nuclear fuels are treated as a set of mono-dimensional-coupled problems which encompass heat, displacement, fuel constituent redistribution, and fission gas release. Rather than uncoupling these coupled equations as in conventional fuel performance analysis codes, efforts are put into to obtain fully coupled solutions by relying on the recent advances of numerical analysis. Through this approach, a new SFR metal fuel performance analysis code, called PUMA (Performance of Uranium Metal fuel rod Analysis code) is under development. Although coupling between temperature and fuel constituent was made easily, the coupling between the mechanical equilibrium equation and a set of stiff kinetics equations for fission gas release is accomplished by introducing one-level Newton scheme through backward differentiation formula. Displacement equations from 1D finite element formulation of the mechanical equilibrium equation are solved simultaneously with stress equation, creep equation, swelling equation, and FGR equations. Calculations was made successfully such that the swelling and the hydrostatic pressure are interrelated each other. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Connecting Performance Analysis and Visualization to Advance Extreme Scale Computing

In the first week of January 2014 Dagstuhl hosted a Perspectives Workshop on “Connecting Performance Analysis and Visualization to Advance Extreme Scale Computing”. The event brought together two previously separate communities – from Visualization and HPC Performance Analysis – to discuss a long term joined research agenda. The goal was to identify and address the challenges in using visual representations to understand and optimize the performance of extreme-scale applications running on today’s most powerful computing systems like climate modeling, combustion, material science or astro-physics simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Performance Analysis: Work Control Events Identified January - August 2010

This performance analysis evaluated 24 events that occurred at LLNL from January through August 2010. The analysis identified areas of potential work control process and/or implementation weaknesses and several common underlying causes. Human performance improvement and safety culture factors were part of the causal analysis of each event and were analyzed. The collective significance of all events in 2010, as measured by the occurrence reporting significance category and by the proportion of events that have been reported to the DOE ORPS under the ''management concerns'' reporting criteria, does not appear to have increased in 2010. The frequency of reporting in each of the significance categories has not changed in 2010 compared to the previous four years. There is no change indicating a trend in the significance category and there has been no increase in the proportion of occurrences reported in the higher significance category. Also, the frequency of events, 42 events reported through August 2010, is not greater than in previous years and is below the average of 63 occurrences per year at LLNL since 2006. Over the previous four years, an average of 43% of the LLNL's reported occurrences have been reported as either ''management concerns'' or ''near misses.'' In 2010, 29% of the occurrences have been reported as ''management concerns'' or ''near misses.'' This rate indicates that LLNL is now reporting fewer ''management concern'' and ''near miss'' occurrences compared to the previous four years. From 2008 to the present, LLNL senior management has undertaken a series of initiatives to strengthen the work planning and control system with the primary objective to improve worker safety. In 2008, the LLNL Deputy Director established the Work Control Integrated Project Team to develop the core requirements and graded elements of an institutional work planning and control system. By the end of that year this system was documented and implementation had begun. In 2009, training of the workforce began and as of the time of this report more than 50% of authorized Integration Work Sheets (IWS) use the activity-based planning process. In 2010, LSO independently reviewed the work planning and control process and confirmed to the Laboratory that the Integrated Safety Management (ISM) System was implemented. LLNL conducted a cross-directorate management self-assessment of work planning and control and is developing actions to respond to the issues identified. Ongoing efforts to strengthen the work planning and control process and to improve the quality of LLNL work packages are in progress: completion of remaining actions in response to the 2009 DOE Office of Health, Safety, and Security (HSS) evaluation of LLNL's ISM System; scheduling more than 14 work planning and control self-assessments in FY11; continuing to align subcontractor work control with the Institutional work planning and control system; and continuing to maintain the electronic IWS application. The 24 events included in this analysis were caused by errors in the first four of the five ISMS functions. The most frequent cause was errors in analyzing the hazards (Function 2). The second most frequent cause was errors occurring when defining the work (Function 1), followed by errors during the performance of work (Function 4). Interestingly, very few errors in developing controls (Function 3) resulted in events. This leads one to conclude that if improvements are made to defining the scope of work and analyzing the potential hazards, LLNL may reduce the frequency or severity of events. Analysis of the 24 events resulted in the identification of ten common causes. Some events had multiple causes, resulting in the mention of 39 causes being identified for the 24 events. The most frequent cause was workers, supervisors, or experts believing they understood the work and the hazards but their understanding was incomplete. The second most frequent cause was unclear, incomplete or confusing documents directing the work. Together, these two causes were mentioned 17 times and contributed to 13 of the events. All of the events with the cause of ''workers, supervisors, or experts believing they understood the work and the hazards but their understanding was incomplete'' had this error in the first two ISMS functions: define the work and analyze the hazard. This means that these causes result in the scope of work being ill-defined or the hazard(s) improperly analyzed. Incomplete implementation of these functional steps leads to the hazards not being controlled. The causes are then manifested in events when the work is conducted. The process to operate safely relies on accurately defining the scope of work. This review has identified a number of examples of latent organizational weakness in the execution of work control processes.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Empirical Performance Analysis of HPC Applications with Portable Hardware Counter Metrics [Thesis]

In this dissertation, we demonstrate that it is possible to develop methods of empirical hardware-counter-based performance analysis for scientific applications running on diverse types of CPUs. Although hardware counters have been used in performance analysis for at least 30 years, the methods used are still limited to particular CPU vendors or even particular generations of CPUs from the same vendor. Our motivating hypothesis is that hardware counter-based measurements could be developed to provide consistent performance information on diverse CPU types. This dissertation proves the hypothesis was correct by demonstrating one such set of metrics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

PATHA: Performance Analysis Tool for HPC Applications

Large science projects rely on complex workflows to analyze terabytes or petabytes of data. These jobs are often running over thousands of CPU cores and simultaneously performing data accesses, data movements, and computation. It is difficult to identify bottlenecks or to debug the performance issues in these large workflows. In order to address these challenges, we have developed Performance Analysis Tool for HPC Applications (PATHA) using the state-of-art open source big data processing tools. Our framework can ingest system logs to extract key performance measures, and apply the most sophisticated statistical tools and data mining methods on the performance data. Furthermore, it utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of PATHA, we conduct a case study on the workflows from an astronomy project known as the Palomar Transient Factory (PTF). This study processed 1.6 TB of system logs collected on the NERSC supercomputer Edison. Using PATHA, we were able to identify performance bottlenecks, which reside in three tasks of PTF workflow with the dependency on the density of celestial objects.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Slider Crank WEC Performance Analysis with Adaptive Autoregressive Filtering

This paper investigates a performance analysis of wave excitation force prediction to extract wave power for a slider crank power take-off system (PTOS) based on auto regressive (AR) filters. To efficiently convert wave energy into electricity, the prediction of wave excitation forces to keep the generator and the wave excitation force in sync is important for maximum energy extraction. The study shows a prediction methodology of half period and zero crossings in the practical scenario of irregular ocean waves. The prediction has been tested for different wave periods and with different filter orders. The prediction results have been used in the PTOS simulation to analyze the energy extraction. It has been shown that the prediction accuracy in the wave half period between the truth data and the predicted data drives the WEC energy extraction efficiency. The amplitude of the wave force is not used and hence the prediction deviation in the wave force amplitude does not affect the PTOS energy extraction. Further analysis shows that the optimum energy can be extracted at 15 th order filter with moderate prediction horizon length.

autoregressive filter↗
DOE OSTI2015

Procedure for Performing Security Camera Optical Performance Analysis Tests

Digital security camera technologies proliferate the security marketplace with manufacturers extoling the functions, features, specifications and virtues of their cameras. A process to comparatively analyze security camera technologies using international camera analysis standards and methods provides a defensible means to assess camera image quality on a technically comparable basis. The procedures in this document use ISO standard camera test charts and camera image analysis software to perform test chart image analysis for comparative camera assessment. Step-by-step procedures for acquiring test chart images in the laboratory and producing analysis data using analysis software GUI controls and interfaces for comparative analysis of security camera image quality are detailed in this document. The procedures have been validated through the test and analysis of fixed box and 180 degree field of view multi-imager cameras as well as, fixed and varifocal lenses and analog-to-digital video encoder image quality performance attributes.

42 ENGINEERING↗
DOE OSTI2015

Formulas for Fast Computation of Divergence Statistics Applied to Quantitative Performance Analysis

In an earlier work, we reported on the extension to the statistical analysis capability of the Visualization Tool Kit (VTK), which we developed for the calculation of divergence statistics, with the particular aim of providing quantitative means for High Performance Computing (HPC) performance analysis, of which we provided an example as well as user's manual. However, we did not provide the mathematical foundations for this work. In the current report, we fill this void with the complete derivation of the formulas which we used in the divergence statistics engine. This provides the foundations for future work which will aim at generalizing these formulas for more detailed HPC performance analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Performance Analysis of Large Scale HPC Workflows for Earth System Models

Climate assessment reports, such as the Intergovernmental Panel on Climate Change’s 5th assesment report [IPCC AR5; IPCC et al., 2014] or the United States National Climate Assessment [U.S. Global Change Research Program, 2014], increasingly rely on many-ensemble climate projections from fully-coupled Earth system models (ESM) to asses possible future climate states. ESM participate in a large set of Coupled Model Intercomparson Projects (CMIP) to better understand their models and the climate system, so that they may provide credible climate projections. However, fully participating in these intercomparison exercises represents an extremely large computational personnel burden for modeling centers [Eyring et al., 2016, Williams et al., 2016a]. However, there has been little attention within the climate modeling community paid to the in situ performance of ESMs up until now. Recently a computational performance model intercomparison project (CPMIP) for CMIP6 has begun [Balaji et al., 2017], but there has not yet been an effort to gather, process, and analyze the historical performance data that has been saved within archived log files from previous modeling efforts, nor the development of performance analysis system capable of providing detailed statistical analyses or visualizations of this performance data. Therefore, we’ve begun a joint effort to analyze the historical and current performance data on both GFDL and OLCF modeling systems. Here, we have developed a preliminary performance analysis system to captue, analyize, and visualize end-to-end in situ performance data and applied it to NOAA/GFDL’s post-processing system. Through the course of the project a number of performance improvements have been made, with the steps necessary to handle the expected volume of data (Big Data) likely to come out of the CMIP6 exercises (TB to PB) provided. We have also begun developing a statistical workload model that will allow modeling centers to determined their expected scientific throughput in the context of expected resource (computational and personnel) availability and accounting for typical workflow disruptions. This will allow them to develop a CMIP participation strategy and maximize their level of participation in the exercises. We expect this infrastructure, and the data it produces, to ultimately allow us to identify performance issues and workflow errors that exist across Earth system modeling centers, and possibly HPC modeling centers in general. Center-independent solutions to workflow or performance issues would help alleviate the computational burden for CMIP6+ modeling projects and should realize both labor and cost savings benefits throughout the CMIP community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

SYMBIOSYS: A Methodology for Performance Analysis of Composable HPC Data Services

Microservices are a powerful new way of building, customizing, and deploying distributed services owing to their flexibility and maintainability. Several large-scale distributed platforms have emerged to serve the growing needs of data-centric workloads and services in commercial computing. Concurrently, high-performance computing (HPC) systems and software are rapidly evolving to meet the demands of diversified applications and heterogeneity. The interplay of hardware factors, software configuration parameters, and the flexibility offered with a microservice architecture makes it nontrivial to estimate the optimal service instantiation for a given application workload. Further, this problem is exacerbated when considering that these services operate in a dynamic and heterogeneous HPC environment. An optimally integrated service can be vastly more performant than a haphazardly integrated one. Existing performance tools for HPC either fail to understand the request-response model of communication inherent to microservices or they operate within a narrow scope, limiting the insight that can be gleaned from employing them in isolation. We propose a methodology for integrated performance analysis of HPC microservices frameworks and applications called SYMBIOSYS. We describe its design and implementation within the context of the Mochi framework. This integration is achieved by combining distributed callpath profiling and tracing with a performance data exchange strategy that collects fine-grained, low-level metrics from the RPC communication library and network layers. The result is a portable, low-overhead performance analysis setup that provides a holistic profile of the dependencies among microservices and how they interact with the Mochi RPC software stack. Using HEPnOS, a production-quality Mochi data service, we demonstrate the low-overhead operation of SYMBIOSYS at scale and use it to identify the root causes of poorly performing service configurations.

microservices↗