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DOE OSTI2014

Domi

Domi (Greek for "structure") is a Trilinos package that provides support for multi-dimensional data arrays distributed over multiple processors of a high performance computing system. The software supports data arrays of arbitrary number of dimensions mapped to a multi-dimensional grid of processors. Domi provides local access to the data, as well as the ability to compute global quantities such as norms, maxima and minima. Data arrays can be constructed with boundary and communication padding, necessary for finite difference algorithms, and provides high-level methods for communicating data between processors to update these buffers. The distributed maps and vectors in Domi can be converted to the equivalent data structures in Epetra and Tpetra (both of which assume unstructured data), using view semantics when appropriate, for use with other Trilinos solvers.

Spotz, WilliamF.↗

DOE OSTI2014

Report for the ASC CSSE L2 Milestone (4873) - Demonstration of Local Failure Local Recovery Resilient Programming Model.

Recovery from process loss during the execution of a distributed memory parallel application is presently achieved by restarting the program, typically from a checkpoint file. Future computer system trends indicate that the size of data to checkpoint, the lack of improvement in parallel file system performance and the increase in process failure rates will lead to situations where checkpoint restart becomes infeasible. In this report we describe and prototype the use of a new application level resilient computing model that manages persistent storage of local state for each process such that, if a process fails, recovery can be performed locally without requiring access to a global checkpoint file. LFLR provides application developers with an ability to recover locally and continue application execution when a process is lost. This report discusses what features are required from the hardware, OS and runtime layers, and what approaches application developers might use in the design of future codes, including a demonstration of LFLR-enabled MiniFE code from the Matenvo mini-application suite.

Heroux, Michael Allen↗

DOE OSTI2014

Workshop on Software Productivity for Extreme-scale Science, January 13-14, 2014, Hilton Hotel, Rockville, MD

While emerging extreme-scale computing systems provide unprecedented resources for scientific discovery, DOE’s scientific communities now face a crisis in extreme-scale scientific software productivity due to a powerful confluence of factors: ongoing disruptive changes in computer architectures and new frontiers in modeling, simulation, and analysis of complex multiscale and multiphysics phenomena. Research in mission-critical science and energy problems has become increasingly dependent on high-performance computing, but productivity in extreme-scale scientific application development has not kept pace. The principal issues are (1) a significant lag between extreme-scale hardware and algorithmic innovations and their effective use in applications, leading to poorly scaling codes; (2) obstacles in the combined use of independently developed software components; (3) lack of agile yet rigorous software engineering practices for high-performance computing; and (4) failure to consider the entire lifecycle of large scientific software efforts, leading to fragile, complicated application codes that are increasingly difficult to enhance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Domi

Spotz, William F.↗

DOE OSTI2019

SST-GPU: An Execution -Driven CUDA Kernel Scheduler and Streaming-Multiprocessor Compute Model

Programmable accelerators have become commonplace in modern computing systems. Advances in programming models and the availability of massive amounts of data have created a space for massively parallel acceleration where the context for thousands of concurrent threads are resident on-chip. These threads are grouped and interleaved on a cycle-by-cycle basis among several massively parallel computing cores. The design of future supercomputers relies on an ability to model the performance of these massively parallel cores at scale. To address the need for a scalable, decentralized GPU model that can model large GPUs, chiplet-based GPUs and multi-node GPUs, this report details the first steps in integrating the open-source, execution driven GPGPU-Sim into the SST framework. The first stage of this project, creates two elements: a kernel scheduler SST element accepts work from SST CPU models and schedules it to an SM-collection element that performs cycle-by-cycle timing using SSTs Mem Hierarchy to model a flexible memory system.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Systems and methods for optimizing component placement in a battery design

Systems, computer readable media, and method concern determining operational parameters of an inventory of components to be included in a battery. The components can include storage components and electrical components. The method includes generating a system of equations that describe suitable combinations of components in locations in a battery design layout for the battery, the system of equations being generated based at least partially on rules and constraints, and includes analyzing the system of equations using integer optimization. The integer optimization can optimizes, based at least partially on the operational parameters, one or more objective functions in the system of equations to determine at least one solution for the battery design layout, and the at least one solution can include a set of the components for the locations in the battery design layout for the battery. The method includes providing, via an interface, the at least one solution.

Cox, Kelsea↗

DOE OSTI2017

Epidemic failure detection and consensus for extreme parallelism

Future extreme-scale high-performance computing systems will be required to work under frequent component failures. The MPI Forum s User Level Failure Mitigation proposal has introduced an operation, MPI Comm shrink, to synchronize the alive processes on the list of failed processes, so that applications can continue to execute even in the presence of failures by adopting algorithm-based fault tolerance techniques. This MPI Comm shrink operation requires a failure detection and consensus algorithm. This paper presents three novel failure detection and consensus algorithms using Gossiping. The proposed algorithms were implemented and tested using the Extreme-scale Simulator. The results show that in all algorithms the number of Gossip cycles to achieve global consensus scales logarithmically with system size. The second algorithm also shows better scalability in terms of memory and network bandwidth usage and a perfect synchronization in achieving global consensus. The third approach is a three-phase distributed failure detection and consensus algorithm and provides consistency guarantees even in very large and extreme-scale systems while at the same time being memory and bandwidth efficient.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Response of a Memristive Biomembrane and Demonstration of Potential Use in Online Learning

The pervasive von Neumann architecture uses complex processor cores and sequential computation. In contrast, the brain is massively parallel and highly efficient, owing to the ability of the neurons and synapses to store and process information simultaneously and to adapt according to incoming information. These features have motivated researchers to develop a host of brain-inspired computers, devices, and models, collectively referred to as neuromorphic computing systems. The quest for synaptic materials capable of closely mimicking biological synapses has led to an alamethicin-doped, synthetic biomembrane with volatile memristive properties which can emulate key synaptic functions to facilitate learning and computation. In contrast to its solid-state counterparts, this two-terminal, biomolecular memristor features similar structure, switching mechanisms, and ionic transport modality as biological synapses while consuming considerably lower power. To use the device as a circuit element, it is important to understand its response to different kinds of input signals. Here we develop a simplified closed form analytical solution based on the underlying state equations for pulse and sine wave inputs. A Verilog-A model based on Runge-Kutta method was developed to incorporate the device in a circuit simulator. Finally, the paper demonstrates possible applications for short- and long-term learning using its unique volatile memristive properties.

Hasan, Md Sakib↗

DOE OSTI2014

Performance Refactoring of Instrumentation, Measurement, and Analysis Technologies for Petascale Computing. The PRIMA Project

The growing number of cores provided by today’s high-end computing systems present substantial challenges to application developers in their pursuit of parallel efficiency. To find the most effective optimization strategy, application developers need insight into the runtime behavior of their code. The University of Oregon (UO) and the Juelich Supercomputing Centre of Forschungszentrum Juelich (FZJ) develop the performance analysis tools TAU and Scalasca, respectively, which allow high-performance computing (HPC) users to collect and analyze relevant performance data – even at very large scales. TAU and Scalasca are considered among the most advanced parallel performance systems available, and are used extensively across HPC centers in the U.S., Germany, and around the world. The TAU and Scalasca groups share a heritage of parallel performance tool research and partnership throughout the past fifteen years. Indeed, the close interactions of the two groups resulted in a cross-fertilization of tool ideas and technologies that pushed TAU and Scalasca to what they are today. It also produced two performance systems with an increasing degree of functional overlap. While each tool has its specific analysis focus, the tools were implementing measurement infrastructures that were substantially similar. Because each tool provides complementary performance analysis, sharing of measurement results is valuable to provide the user with more facets to understand performance behavior. However, each measurement system was producing performance data in different formats, requiring data interoperability tools to be created. A common measurement and instrumentation system was needed to more closely integrate TAU and Scalasca and to avoid the duplication of development and maintenance effort. The PRIMA (Performance Refactoring of Instrumentation, Measurement, and Analysis) project was proposed over three years ago as a joint international effort between UO and FZJ to accomplish these objectives: (1) refactor TAU and Scalasca performance system components for core code sharing and (2) integrate TAU and Scalasca functionality through data interfaces, formats, and utilities. As presented in this report, the project has completed these goals. In addition to shared technical advances, the groups have worked to engage with users through application performance engineering and tools training. In this regard, the project benefits from the close interactions the teams have with national laboratories in the United States and Germany. We have also sought to enhance our interactions through joint tutorials and outreach. UO has become a member of the Virtual Institute of High-Productivity Supercomputing (VI-HPS) established by the Helmholtz Association of German Research Centres as a center of excellence, focusing on HPC tools for diagnosing programming errors and optimizing performance. UO and FZJ have conducted several VI-HPS training activities together within the past three years.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Low Loss Graded Index Polymer Optical Fiber for Local Networking

Here, the objective of this Department of Energy SBIR program has been to develop technology for the advancement of advanced computing systems. NanoSonic worked with two subcontractors, the Polymicro Division of Molex, a U.S.-based manufacturer of specialized optical fiber and fiber components, and Virginia Tech, a research university involved through the Global Environment for Network Innovations (GENI) program in high-speed computer networking research. NanoSonic developed a patented molecular-level self-assembly process to manufacture polymer-based optical fibers in a way similar to the modified chemical vapor deposition (MCVD) approach typically used to make glass optical fibers. Although polymer fiber has a higher attenuation per unit length than glass fiber, short connectorized polymer fiber jumpers offer significant cost savings over their glass counterparts, particularly due to the potential use of low-cost plastic fiber connectors. As part of the SBIR commercialization process, NanoSonic exclusively licensed this technology to a large ($100B+ market cap) U.S.-based manufacturing conglomerate near the end of the first year of the Phase II program. With this base technology developed and licensed, NanoSonic then worked with Polymicro to address secondary program goals of using related but not conflicting production methods to enhance the performance of other specialty optical fiber products and components, and Virginia Tech continued its evaluation of developed polymer fibers in its network infrastructure system on the university campus. We also report our current understanding of the observation during the Phase I program of quantum conductance and partial quantum conductance in metal-insulator-metal (MIM) devices. Such conductance behavior may be modeled as singlemode behavior in one-dimensional electrically conducting waveguides, similar in principle to singlemode optical propagation in dielectric fiber waveguides. Although NanoSonic has not licensed any of the additional technology developed during the second year of the program, several proprietary discussions with major materials companies are underway as of the conclusion of Phase II.

36 MATERIALS SCIENCE↗

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 OSTI2022

Computational Study of Variable Fuel-Air Ratio and Hydrogen Doping in a Rotating Detonation Engine

Rotating detonation engines (RDEs) hold great promise as a technology for powering aviation engines. The pressure gain that results from continuously traversing detonation waves enables high theoretical performance compared to deflagration-based combustion. However, the harsh operating conditions inside an RDE restrict the availability of suitable experimental diagnostics to study these systems in detail and the range of spatial and temporal scales render numerical simulations of these systems difficult. In this study, we have combined adaptive mesh refinement and a robust shock-capturing approach to study RDE systems computationally using the compressible, reactive solver PeleC. Multi-species transport along with compressible Navier-Stokes equations are solved in the model along with finite-rate chemistry. Complex geometries are represented using an embedded boundary method with second-order spatial accuracy and the system is evolved in time using a second-order Runge-Kutta method. We present studies of a methane-air RDE system that is doped with varying levels of hydrogen. Our analysis focusses on how fuel mixture and doping levels effect the detonation flame structure and the generation of multiple wave modes.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Containerization in ATLAS Software Development and Data Production

The ATLAS experiment's software production and distribution on the grid benefits from a semi-automated infrastructure that provides up-to-date information about software usability and availability through the CVMFS dis-tribution service for all relevant systems. The software development process uses a Continuous Integration pipeline involving testing, validation, packag-ing and installation steps. For opportunistic sites that can not access CVMFS, containerized releases are needed. These standalone containers are currently created manually to support Monte-Carlo data production at such sites. In this paper we will describe an automated procedure for the containerization of AT-LAS software releases in the existing software development infrastructure, its motivation, integration and testing in the distributed computing system.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

DCA++

With the DCA++ project, we aim to gain insight into the fascinating physics of strongly correlated electron systems by employing modern quantum cluster methods. The DCA++ code provides a state of the art implementation of the dynamical cluster approximation (DCA) and its DCA+ extension. High scalability and portable performance allow to exploit today's leadership computing systems.

SciDAC↗

DOE OSTI2016

The Next Frontier in Computing

Exascale computing refers to computing systems capable of at least one exaflop or a billion calculations per second (1018). That is 50 times faster than the most powerful supercomputers being used today and represents a thousand-fold increase over the first petascale computer that came into operation in 2008. How we use these large-scale simulation resources is the key to solving some of today’s most pressing problems, including clean energy production, nuclear reactor lifetime extension and nuclear stockpile aging.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing

Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

Capacity allocation↗

DOE OSTI2015

Towards intelligent diagnostic system employing integration of mathematical and engineering model

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well as mathematical model of clustering technique have been widely used in developing the medical diagnostic systems. The selected features will be classified using mathematical models that embedded engineering theory such as artificial intelligence, support vector machine, neural network and fuzzy-neuro system. These classifiers will provide the diagnostic results without human intervention. Among many publishable researches, several prototypes have been developed namely NeuralPap, Neural Mammo, and Cervix Kit. The former system (NeuralPap) is an automatic intelligent diagnostic system for classifying and distinguishing between the normal and cervical cancerous cells. Meanwhile, the Cervix Kit is a portable Field-programmable gate array (FPGA)-based cervical diagnostic kit that could automatically diagnose the cancerous cell based on the images obtained during sampling test. Besides the cervical diagnostic system, the Neural Mammo system is developed to specifically aid the diagnosis of breast cancer using a fine needle aspiration image.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Evaluation of pre-training large language models on leadership-class supercomputers

Large language models (LLMs) have arisen rapidly to the center stage of artificial intelligence as the foundation models applicable to many downstream learning tasks. However, how to effectively build, train, and serve such models for many high-stake and first-principle-based scientific use cases are both of great interests and of great challenges. Moreover, pre-training LLMs with billions or even trillions of parameters can be prohibitively expensive not just for academic institutions, but also for well-funded industrial and government labs. Furthermore, the energy cost and the environmental impact of developing LLMs must be kept in mind. Here, in this work, we conduct a first-of-its-kind performance analysis to understand the time and energy cost of pre-training LLMs on the Department of Energy (DOE)’s leadership-class supercomputers. Employing state-of-the-art distributed training techniques, we evaluate the computational performance of various parallelization approaches at scale for a range of model sizes, and establish a projection model for the cost of full training. Our findings provide baseline results, best practices, and heuristics for pre-training such large models that should be valuable to HPC community at large. We also offer insights and optimization strategies for using the first exascale computing system, Frontier, to train models of the size of GPT-3 and beyond.

97 MATHEMATICS AND COMPUTING↗