Optical Networks for High-Performance Computing: Promises and Perils.
Abstract not provided.
Rodrigues, Arun F.↗
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Low-cost optical fiber interconnects for networks and high-performance computing
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97 MATHEMATICS AND COMPUTING↗
Field-Deployable Quantum Memory for Quantum Networking
High-performance quantum memories are an essential component for regulating temporal events in quantum networks. As a component in quantum-repeaters, they have the potential to support the distribution of entanglement beyond the physical limitations of fiber loss. This will enable key applications such as quantum key distribution, network-enhanced quantum sensing, and distributed quantum computing. Here, we present a quantum memory engineered to meet real-world deployment and scaling challenges. The memory technology utilizes a warm rubidium vapor as the storage medium, and operates at room temperature, without the need for vacuum- and/or cryogenic- support. Here, we demonstrate performance specifications of high-fidelity retrieval (95%) and low operation error (10 –2 ) at a storage time of 160 µs for single-photon level quantum memory operations. We further show a substantially improved storage time (with classical-level light) of up to 1ms by suppressing atomic diffusions. The device is housed in an enclosure with a standard 2U rackmount form factor, and can robustly operate on a day scale in a noisy environment. This result marks an important step toward implementing quantum networks in the field.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
UCX: An Open Source Framework for HPC Network APIs and Beyond, In: 2015 IEEE 23rd Annual Symposium on High-Performance Interconnects
This paper presents Unified Communication X (UCX), a set of network APIs and their implementations for high throughput computing. UCX comes from the combined effort of national laboratories, industry, and academia to design and implement a high-performing and highly-scalable network stack for next generation applications and systems. UCX design provides the ability to tailor its APIs and network functionality to suit a wide variety of application domains and hardware. We envision these APIs to satisfy the networking needs of many programming models such as Message Passing Interface (MPI), OpenSHMEM, Partitioned Global Address Space (PGAS) languages, task-based paradigms and I/O bound applications. To evaluate the design we implement the APIs and protocols, and measure the performance of overhead-critical network primitives fundamental for implementing many parallel programming models and system libraries. Our results show that the latency, bandwidth, and message rate achieved by the portable UCX prototype is very close to that of the underlying driver. With UCX, we achieved a message exchange latency of 0.89 us, a bandwidth of 6138.5 MB/s, and a message rate of 14 million messages per second. As far as we know, this is the highest bandwidth and message rate achieved by any network stack (publicly known) on this hardware.
Computer Science↗
ExaTN: Scalable GPU-Accelerated High-Performance Processing of General Tensor Networks at Exascale
We present ExaTN (Exascale Tensor Networks), a scalable GPU-accelerated C++ library which can express and process tensor networks on shared- as well as distributed-memory high-performance computing platforms, including those equipped with GPU accelerators. Specifically, ExaTN provides the ability to build, transform, and numerically evaluate tensor networks with arbitrary graph structures and complexity. It also provides algorithmic primitives for the optimization of tensor factors inside a given tensor network in order to find an extremum of a chosen tensor network functional, which is one of the key numerical procedures in quantum many-body theory and quantum-inspired machine learning. Numerical primitives exposed by ExaTN provide the foundation for composing rather complex tensor network algorithms. We enumerate multiple application domains which can benefit from the capabilities of our library, including condensed matter physics, quantum chemistry, quantum circuit simulations, as well as quantum and classical machine learning, for some of which we provide preliminary demonstrations and performance benchmarks just to emphasize a broad utility of our library.
97 MATHEMATICS AND COMPUTING↗
Network protocols for real-time applications
The Fiber Distributed Data Interface (FDDI) and the SAE AE-9B High Speed Ring Bus (HSRB) are emerging standards for high-performance token ring local area networks. FDDI was designed to be a general-purpose high-performance network. HSRB was designed specifically for military real-time applications. A workshop was conducted at NASA Ames Research Center in January, 1987 to compare and contrast these protocols with respect to their ability to support real-time applications. This report summarizes workshop presentations and includes an independent comparison of the two protocols. A conclusion reached at the workshop was that current protocols for the upper layers of the Open Systems Interconnection (OSI) network model are inadequate for real-time applications.
Johnson, Marjory J.↗
ASCR@40: Highlights and Impacts of ASCR’s Programs
A report compiled by the ASCAC Subcommittee on the 40-year history of ASCR for the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. The Office of Advanced Scientific Computing Research (ASCR) sits within the Office of Science in the Department of Energy (DOE). Per their web pages, “the mission of the ASCR program is to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to the DOE.” This succinct statement encompasses a wide range of responsibilities for computing and networking facilities; for procuring, deploying, and operating high performance computing, networking, and storage resources; for basic research in mathematics and computer science; for developing and sustaining a large body of software; and for partnering with organizations across the Office of Science and beyond. While its mission statement may seem very contemporary, the roots of ASCR are quite deep—long predating the creation of DOE. Applied mathematics and advanced computing were both elements of the Theoretical Division of the Manhattan Project. In the early 1950s, the Manhattan Project scientist and mathematician John von Neumann, then a commissioner for the AEC (Atomic Energy Commission), advocated for the creation of a Mathematics program to support the continued development and applications of digital computing. Los Alamos National Laboratory (LANL) scientist John Pasta created such a program to fund researchers at universities and AEC laboratories. Under several organizational name changes, this program has persisted ever since, and would eventually grow to become ASCR.
97 MATHEMATICS AND COMPUTING↗
SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential
SuperLab 2.0 (a five-lab demonstration) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, photovoltaics, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactors, control centers, and gas turbines, across five U.S. Department of Energy (DOE) national laboratories: National Laboratory of the Rockies (NLR), Idaho National Laboratory, National Energy Technology Laboratory, Lawrence Berkeley National Laboratory, and Sandia National Laboratories. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance DOE network, and are controlled via a centralized energy controller hosted at NLR's Advanced Research on Integrated Energy Systems facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Plastic deformation mechanisms in polyimide resins and their semi-interpenetrating networks
High-performance thermoset resins and composites are critical to the future growth of space, aircraft, and defense industries in the USA. However, the processing-structure-property relationships in these materials remain poorly understood. In the present ASEE/NASA Summer Research Program, the plastic deformation modes and toughening mechanisms in single-phase and multiphase thermoset resins were investigated. Both thermoplastic and thermoset polyimide resins and their interpenetrating networks (IPNs and semi-IPNs) were included. The fundamental tendency to undergo strain localization (crazing and shear banding) as opposed to a more diffuse (or homogeneous) deformation in these polymers were evaluated. Other possible toughening mechanisms in multiphase thermoset resins were also examined. The topological features of network chain configuration/conformation and the multiplicity of phase morphology in INPs and semi-IPNs provide unprecedented opportunities for studying the toughening mechanisms in multiphase thermoset polymers and their fiber composites.
Jang, Bor Z.↗
HPC and Cloud Convergence Beyond Technical Boundaries: Strategies for Economic Sustainability, Standardization, and Data Accessibility
At the IEEE/ACM International Conference for High-Performance Computing, Networking, Storage, and Analysis (SC23), held in Denver, experts discussed the convergence of high-performance computing and cloud computing. Experts explored how this integration could address current scientific computing limitations, enhance computational capabilities, and foster global collaboration while focusing on economic, security, technical, and community challenges and opportunities.
97 MATHEMATICS AND COMPUTING↗
Improving processing and toughness of a high performance composite matrix through an interpenetrating polymer network. VI
The use of a semiinterpenetrating polymer network (SIPN) of the high-performance polyimide NR-150B2 to reduce brittleness and improve processability in the highly crosslinked acetylene-terminated polyimides Thermid LR-600, AL-600, MC-600, and FA-700 is described. The theoretical basis of the SIPN process is reviewed; the preparation and characterization of the neat SIPN resins and unidirectional graphite-fiber composites are explained; and the results are presented in extensive tables, graphs, and micrographs and discussed in detail. Significant increases in fracture energy were observed with SIPN, from 93 J/sq m for unmodified LR-600 to 283-603 J/sq m for the SIPN materials; the room-temperature flexural strength of the unidirectional composites also increased, from 1344 MPa for an unmodified MC-600 composite to 2020-1751 MPa for the SIPN composites. The potential applicability of SIPN-based composites to aerospace structures and electronic components is indicated.
Pater, Ruth H.↗
Auto-HPCnet: An Automatic Framework to Build Neural Network-based Surrogate for High-Performance Computing Applications
High-performance computing communities are increasingly adopt- ing Neural Networks (NN) as surrogate models in their applications to generate scientific insights. Replacing an execution phase in the application with NN models can bring significant performance im- provement. However, there is a lack of tools that can help domain scientists automatically apply NN-based surrogate models to HPC applications. We introduce a framework, named Auto-HPCnet, to democratize the usage of NN-based surrogates. Auto-HPCnet is the first end-to-end framework that makes past proposals for the NN-based surrogate model practical and disciplined. Auto-HPCnet introduces a workflow to address unique challenges when apply- ing the approximation, such as feature acquisition and meeting the application-specific constraint on the quality of final computation outcome. We show that Auto-HPCnet can leverage NN for a set of HPC applications and achieve 5.50× speedup on average (up to 16.8× speedup and with data preparation cost included) while meeting the application-specific constraint on the final computation quality.
Dong, Wenqian↗
Positron annihilation spectroscopic study of high performance semi-interpenetrating network polyimids
Semi-interpenetrating (S-IPN) network polyimids were made from different proportions of LaRC RP46 (a thermosetpolyimid) and LaRC BDTA-ODA (a thermoplastic polyimid). The ultimate goal of this networking is to improve the mechanical properties of the thermoset polyimid. Positron lifetime study was made to calculate lifetime based on second component of the life time spectra and the free volume & microvoid size. All these properties tend to decrease steadily with increasing thermoset content except at the 50 percent thermoset level where these properties show sudden drop. This result contradicts with the initial expectation that the blend properties should change gradually if it were a solid solution of thermoset (TSP) and thermoplastic (TPP) components. Thermal analyses (TMA, DSC, DMA & TGA) were run to complement the positron life time studies. The TMA and DSC studies confirm the contradiction mentioned above. Further experimentation with S-IPN polymers made at TSP/TTP content around 50/50 level are being conducted to explain this anomaly. Scanning electron microscope study of the S-IPN polyimid samples is under way in order to detect morphological differences which might help explain the phenomenon mentioned above.
Ray, Asit K.↗
High Performance Parallel Architectures
Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operational. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, aimed at concentrating the vital information and discarding redundant data. One such transformation, which is widely used in remote sensing, is the Principal Components Analysis (PCA). This report summarizes our progress on the development of a parallel PCA and its implementation on two Beowulf cluster configuration; one with fast Ethernet switch and the other with a Myrinet interconnection. Details of the implementation and performance results, for typical sets of multispectral and hyperspectral NASA remote sensing data, are presented and analyzed based on the algorithm requirements and the underlying machine configuration. It will be shown that the PCA application is quite challenging and hard to scale on Ethernet-based clusters. However, the measurements also show that a high- performance interconnection network, such as Myrinet, better matches the high communication demand of PCA and can lead to a more efficient PCA execution.
El-Ghazawi, Tarek↗
Silicon Photonics Parallel Processor for High Performance Computing and Sensor Networks: Phase I Final Report
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17 WIND ENERGY↗
Freeze-drying for morphological control of high performance semi-interpenetrating polymer networks. III
The feasibility of using a freeze-drying (solvent removal by sublimation) approach for controlling the morphology of a high-performance semi-IPN is assessed. A high-performance thermoplastic polyimide and commercially available 4,4'-bismaleimide diphenylenemethane were dissolved in a solvent, 1,3,5-trioxane. The solvent was removed from the constituents by freeze-drying. For purposes of comparison, the constituents were dissolved in a high-boiling-point solvent, N,N-dimethylformamide. The solvent was removed from the solution by evaporation. The physical and mechanical properties and phase morphology of the neat resins and composites prepared by freeze-drying and traditional solution methods are presented and compared. It is concluded that the TG is higher and that the magnitude of minor constituent separation is less in the freeze-dry processed materials than for the processed solution.
Hsiung, H. J.↗
Performance Evaluation of Vertical Federated Machine Learning Against Adversarial Threats on Wide-Area Control System: Preprint
Federated machine learning (FL) is gaining significant popularity to develop cybersecurity solutions in power grids because of its advanced capability to support decentralized data handing at local devices, its privacy preservation, and its low-bandwidth requirement. However, the evolving adversarial machine learning (AML) threats raise significant concerns for the cybersecurity of FL architectures. The FL-based split neural network (SplitNN) achieves high performance through the decentralized training of local neural network models while preserving data privacy across multiple entities. In this paper, we propose a methodology for evaluating the performance of a vertical FLbased anomaly detector against different types of AML attacks, including denial-of-service attacks, adversarial data injection attacks, and replay attacks on the trained local models deployed in the grid network. For a case study, we consider the modified IEEE 13-bus system, and we develop SplitNN-based binary and multiclass classification models to detect, locate, and identify different types of data integrity attacks on the volt-watt control with two pooling layers: maximum pooling and AvgPool. Our experimental results, computed through performance metrics, reveal that the severity of these AML attacks varies with the integrated pooling mechanism, the type of classification model, and the nature of the cyberattack. Further, the AML attacks negatively impacted the prediction time per sample for the pretrained SplitNN during the online testing.
adversarial threats↗