Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Computer Systems”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11

Matrix Multiply Performance of GPUs on Exascale-class HPE/Cray Systems

The computation of dense matrix-matrix products (GEMMs) is central to many modeling and simulation workloads as well as AI/ML deep learning campaigns. In fact, millions of dollars are spent annually on computing GEMMs, and large model training demands are increasing exponentially. Specialized processors such as GPUs are designed to perform well for these operations. However, the performance of GEMMs on GPUs can exhibit complex behaviors depending on many factors, making it challenging to optimize the performance of GEMMs on these processors. In this study we undertake an examination of GEMM performance on several leading GPU models taken from product lines of GPUs to be deployed in forthcoming exascale computing systems. We show results to illustrate the many factors that can affect performance of GEMMs on GPUs. We then present data collected from a large number of test runs for an example GEMM operation to show the dependence behaviors of GEMM rate on matrix dimensions. Finally, we show results from machine learning-based performance models using novel feature engineering methods to fit the measured performance, providing a potential basis for GEMM performance tuning and autotuning methods for GPUs. Recommendations are also given for how to achieve high GEMM performance on modern GPUs.

Melesse Vergara, Veronica↗

Systems and methods for designing new materials for superlubricity

A method for designing new materials for superlubricity comprises developing, on a computational system, a computational supercell comprising x unit cells of a base material, each unit cell comprising y atoms of the base material. The computational system replaces randomly chosen z atoms of the base material with an impurity atom of an impurity material to form a candidate material. The computational system determines volumetric strain of the candidate material. In response to the volumetric strain exceeding a predetermined threshold, the computational system determines that the candidate material has superlubricity. The computational system displays the candidate material to a user if the candidate material has superlubricity.

Narayanan, Badri↗

High-Performance Computing for Earth System Modeling

High-performance computing (HPC) plays an important role during the development of Earth system models. This chapter reviews HPC efforts related to Earth system models, including community Earth system models and energy exascale Earth system models. Specifically, this chapter evaluates computational and software design issues, analyzes several current HPC-related model developments, and provides an outlook for some promising areas within Earth system modeling in the era of exascale computing.

Wang, Dali↗

On computing stress in polymer systems involving multi-body potentials from molecular dynamics simulation

Hardy stress definition has been restricted to pair potentials and embedded-atom method potentials due to the basic assumptions in the derivation of a symmetric microscopic stress tensor. Force decomposition required in the Hardy stress expression becomes obscure for multi-body potentials. In this work, we demonstrate the invariance of the Hardy stress expression for a polymer system modeled with multi-body interatomic potentials including up to four atoms interaction, by applying central force decomposition of the atomic force. The balance of momentum has been demonstrated to be valid theoretically and tested under various numerical simulation conditions. The validity of momentum conservation justifies the extension of Hardy stress expression to multi-body potential systems. Computed Hardy stress has been observed to converge to the virial stress of the system with increasing spatial averaging volume. This work provides a feasible and reliable linkage between the atomistic and continuum scales for multi-body potential systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)

Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The final versions of the reports are combined in this document, and are presented along with introductory material.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)

Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The final versions of the reports are combined in this document, and are presented along with introductory material.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Editorial: Quantum Information and Quantum Computing for Chemical Systems

Quantum computing has emerged as an exciting inter-disciplinary research topic that cuts across the traditional fields of physics, computer science, and engineering. It is a revolutionary model of computation that has offered new insights into methods for modeling and simulation of chemical systems. Applications of quantum computing to chemistry have demonstrated rapid progress on both theoretical and experimental fronts. Past theoretical efforts have shown how to adapt quantum computation to a variety of problems including electronic structure and molecular dynamics. In addition, the development of quantum algorithms for quantum chemistry has been stimulated by the greater availability of more capable quantum computing devices. Advances in the number and quality of qubits continues to enable remarkable proof-of-concept demonstrations working towards a milestone of quantum computational advantage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Mechanism of supporting sub-communicator collectives with o(64) counters as opposed to one counter for each sub-communicator

A system and method for enhancing barrier collective synchronization on a computer system comprises a computer system including a data storage device. The computer system includes a program stored in the data storage device and steps of the program being executed by a processor. The system includes providing a plurality of communicators for storing state information for a barrier algorithm. Each communicator designates a master core in a multi-processor environment of the computer system. The system allocates or designates one counter for each of a plurality of threads. The system configures a table with a number of entries equal to the maximum number of threads. The system sets a table entry with an ID associated with a communicator when a process thread initiates a collective. The system determines an allocated or designated counter by searching entries in the table.

97 MATHEMATICS AND COMPUTING↗

Comparative study of machine learning techniques for post-combustion carbon capture systems

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.

97 MATHEMATICS AND COMPUTING↗

Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems

Designing a neuromorphic computing system involves selection of several hyperparameters that not only affect the accuracy of the framework, but also the energy efficiency and speed of inference and training. These hyperparameters might be inherent to the training of the spiking neural network (SNN), the input/output encoding of the real-world data to spikes, or the underlying neuromorphic hardware. In this work, we present a Bayesian-based hyperparameter optimization approach for spiking neuromorphic systems, and we show how this optimization framework can lead to significant improvement in designing accurate neuromorphic computing systems. In particular, we show that this hyperparameter optimization approach can discover the same optimal hyperparameter set for input encoding as a grid search, but with far fewer evaluations and far less time. We also show the impact of hardware-specific hyperparameters on the performance of the system, and we demonstrate that by optimizing these hyperparameters, we can achieve significantly better application performance.

Parsa, Maryam↗

Data Movement Dominates: Final Report

Over the past three years in this project, what we have observed is that the primary reason for data movement in large-scale systems is that the per-node capacity is not large enough—i.e., one of the solutions to the data-movement problem (certainly not the only solution that is required, but a significant one nonetheless) is to increase per-node capacity so that inter-node traffic is reduced. This unfortunately is not as simple as it sounds. Today’s main memory systems for datacenters, enterprise computing systems, and supercomputers, fail to provide high per-socket capacity [Dirik & Jacob 2009; Cooper-Balis et al. 2012], except at extremely high price points (factors of 10–100x the cost/bit of consumer main-memory systems) [Stokes 2008]. The reason is that our choice of technology for today’s main memory systems—i.e., DRAM, which we have used as a main-memory technology since the 1970s [Jacob et al. 2007]—can no longer keep up with our needs for density and price per bit. Main memory systems have always been built from the cheapest, densest, lowest-power memory technology available, and DRAM is no longer the cheapest, the densest, nor the lowest-power storage technology out there. It is now time for DRAM to go the way that SRAM went: move out of the way for a cheaper, slower, denser storage technology, and become a cache instead. This inflection point has happened before, in the context of SRAM yielding to DRAM. There was once a time that SRAM was the storage technology of choice for all main memories [Tomasulo 1967; Thornton 1970; Kidder 1981]. However, once DRAM hit volume production in the 1970s and 80s, it supplanted SRAM as a main memory technology because it was cheaper, and it was denser. It also happened to be lower power, but that was not the primary consideration of the day. At the time, it was recognized that DRAM was much slower than SRAM, but it was only at the supercomputer level (For instance the Cray X-MP in the 1980s and its follow-on, the Cray Y-MP, in the 1990s) that could one afford to build ever- larger main memories out of SRAM—the reasoning for moving to DRAM was that an appropriately designed memory hierarchy, built of DRAM as main memory and SRAM as a cache, would approach the performance of SRAM, at the price-per-bit of DRAM [Mashey 1999]. Today it is quite clear that, were one to build an entire multi-gigabyte main memory out of SRAM instead of DRAM, one could improve the performance of almost any computer system by up to an order of magnitude—but this option is not even considered, because to build that system would be prohibitively expensive. It is now time to revisit the same design choice in the context of modern technologies and modern systems. For reasons both technical and economic, we can no longer afford to build ever-larger main memory systems out of DRAM. Flash memory, on the other hand, is significantly cheaper and denser than DRAM and therefore should take its place. While it is true that flash is significantly slower than DRAM, one can afford to build much larger main memories out of flash than out of DRAM, and we show that an appropriately designed memory hierarchy, built of flash as main memory and DRAM as a cache, will approach the performance of DRAM, at the price-per-bit of flash. In our studies as part of this project, we have investigated Non-Volatile Main Memory (NVMM), a new main-memory architecture for large-scale computing systems, one that is specifically designed to address the weaknesses described previously. In particular, it provides the following features: non-volatility: The bulk of the storage is comprised of NAND flash, and in this organization DRAM is used only as a cache, not as main memory. Furthermore, the flash is journaled, which means that operations such as checkpoint/restore are already built into the system. 1+ terabytes of storage per socket: SSDs and DRAM DIMMs have roughly the same form factor (several square inches of PCB surface area), and terabyte SSDs are now commonplace. performance approaching that of DRAM: DRAM is used as a cache to the flash system. price-per-bit approaching that of NAND: Flash is currently well under $0.50 per gigabyte; DDR3 SDRAM is currently just over $10 per gigabyte [Newegg 2014]. Even today, one can build an easily affordable main memory system with a terabyte or more of NAND storage per CPU socket (which would be extremely expensive were one to use DRAM), and our cycle- accurate, full-system experiments show that this can be done at a performance point that lies within a factor of two of DRAM.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Noise tolerant spatiotemporal chaos computing

We introduce and design a noise tolerant chaos computing system based on a coupled map lattice (CML) and the noise reduction capabilities inherent in coupled dynamical systems. The resulting spatiotemporal chaos computing system is more robust to noise than a single map chaos computing system. In this CML based approach to computing, under the coupled dynamics, the local noise from different nodes of the lattice diffuses across the lattice, and it attenuates each other's effects, resulting in a system with less noise content and a more robust chaos computing architecture.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Network protection system using linkographs

A method and apparatus for managing an attack on a computer system. A computer identifies actions taken by an adversary in the computer system and links connecting the actions over time using an ontology defining linking rules for linking the actions over time. The computer creates a graph of the actions with the links connecting the actions over time. The graph shows a number of patterns of behavior for the adversary. The computer then identifies a protective action to take with respect to the computer system using the graph of the actions taken by the adversary.

Zage, David John↗

Path scanning for the detection of anomalous subgraphs and use of DNS requests and host agents for anomaly/change detection and network situational awareness

A system, apparatus, computer-readable medium, and computer-implemented method are provided for detecting anomalous behavior in a network. Historical parameters of the network are determined in order to determine normal activity levels. A plurality of paths in the network are enumerated as part of a graph representing the network, where each computing system in the network may be a node in the graph and the sequence of connections between two computing systems may be a directed edge in the graph. A statistical model is applied to the plurality of paths in the graph on a sliding window basis to detect anomalous behavior. Data collected by a Unified Host Collection Agent ("UHCA") may also be used to detect anomalous behavior.

97 MATHEMATICS AND COMPUTING↗

Path scanning for the detection of anomalous subgraphs and use of DNS requests and host agents for anomaly/change detection and network situational awareness

A system, apparatus, computer-readable medium, and computer-implemented method are provided for detecting anomalous behavior in a network. Historical parameters of the network are determined in order to determine normal activity levels. A plurality of paths in the network are enumerated as part of a graph representing the network, where each computing system in the network may be a node in the graph and the sequence of connections between two computing systems may be a directed edge in the graph. A statistical model is applied to the plurality of paths in the graph on a sliding window basis to detect anomalous behavior. Data collected by a Unified Host Collection Agent (“UHCA”) may also be used to detect anomalous behavior.

Neil, Joshua Charles↗

The Future of Computing: Integrating Scientific Computation on Neuromorphic Systems

Neuromorphic computing is known for its integration of algorithms and hardware elements that are inspired by the brain. Conventionally, this nontraditional method of computing is used for many neural or learning inspired applications. Unfortunately, this has resulted in the field of neuromorphic computing being relatively narrow in scope. In this paper we discuss two research areas actively trying to widen the impact of neuromorphic systems. The first is Fugu, a high-level programming interface designed to bridge the gap between general computer scientists and those who specialize in neuromorphic areas. The second aims to map classical scientific computing problems onto these frameworks through the example of random walks. This elucidates a class of scientific applications that are conducive to neuromorphic algorithms.

97 MATHEMATICS AND COMPUTING↗

The neurobench framework for benchmarking neuromorphic computing algorithms and systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website (neurobench.ai).

Yik, Jason [Harvard Univ., Cambridge, MA (United S↗

I/O routing in a multidimensional torus network

A method, system and computer program product are disclosed for routing data packet in a computing system comprising a multidimensional torus compute node network including a multitude of compute nodes, and an I/O node network including a plurality of I/O nodes. In one embodiment, the method comprises assigning to each of the data packets a destination address identifying one of the compute nodes; providing each of the data packets with a toio value; routing the data packets through the compute node network to the destination addresses of the data packets; and when each of the data packets reaches the destination address assigned to said each data packet, routing said each data packet to one of the I/O nodes if the toio value of said each data packet is a specified value. In one embodiment, each of the data packets is also provided with an ioreturn value used to route the data packets through the compute node network.

97 MATHEMATICS AND COMPUTING↗