Reliability-Aware Offloading and Allocation in Multilevel Edge Computing System
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Cutting-edge science is increasingly data-driven due to the emergence of scientific machine learning models that can guide scientists toward fruitful areas of exploration. Experimental science facilities such as light and neutron sources, particle colliders, and radio astronomy telescopes are also producing raw measurement data at rates that exceed available data storage and computing capacity at those facilities. As a result, scientific workflows are being developed that concurrently couple experiments at science facilities with high-performance computing (HPC) facilities to enable analysis of experimental data while the experiment is ongoing, and where analysis results are potentially fed back to the experiment for use in experimental control and/or steering in a time-sensitive manner. Our goal is to design, prototype, and deploy a new capability for the Oak Ridge Leadership Computing Facility (OLCF) that enables such workflows through support for bidirectional, memory-based streaming of data from external experiments into and out of OLCF HPC systems. This high-level design document describes the related work and motivating use cases that inform our understanding of the technical requirements for this capability, and describes a proposed architectural solution that meets these requirements and our plans for demonstrating the capability.
Physics-based three-dimensional (3D) numerical simulations of earthquake ground motions provide an alternative to reliance on limited and highly scattered empirical data and models for seismic hazard assessment. We performed fully 3D earthquake ground motion simulations on the Sierra and Lassen high-performance computing (HPC) platforms at Livermore Computing. These new HPC platforms feature graphics processing unit (GPU) acceleration for improved performance. Ground motions were computed with SW4, a summation-by-parts finite difference code for anelastic wave propagation. SW4 was recently enhanced to run efficiently on GPU-accelerated platforms. We found that SW4 runs almost fifty (50) times faster in terms of node-hours on new GPU-accelerated platforms compared to conventional central processing unit (CPU) platforms with agreement of computed ground motion time-series to the full single precision which they are written. We focused on moment magnitude, M W , 7.0 earthquakes on the Hayward Fault. We were able to run three simulations resolving ground motions from static displacements (0 Hz) to frequencies of 10 Hz (wavelengths as short as 50 meters) making these Sierra calculations the highest resolution regional-scale earthquake simulations ever made for California. Results show good agreement between simulated ground motion intensities (peak ground velocity and acceleration, spectral response) and empirical models, including frequencies above 10 Hz. On Lassen we were able to run a suite of 52 M W 7.0 Hayward Fault earthquake simulations resolved 0-5 Hz for both plane-layered, one-dimensional (1D) and 3D Earth models. These simulations were used to develop a methodology for characterizing and removing systematic path and site effects which bias ground motion intensities. These simulations using the newly enhanced SW4 code allow us to run higher resolution seismic simulations with shorter run times, providing a new capability for seismic hazard and risk studies. Results were presented at scientific conferences and workshops and two publications are being prepared based on work performed in this project.
The possibility of using the LUCKY-A three-dimensional multiprocessor transport code as a neutronics module for the COREMELT integrated non-stationary code for performing safety analysis, which is used for fast sodium reactor safety validation, is discussed. In this application, more stringent requirements are imposed on the computing speed of the LUCKY-A code. To test LUCKY-A possibilities, a typical computational problem is examined in a formulation characteristic for the proposed use of the code. The possibilities of the code for parallelized calculations are shown, and a comparison is made with calculations performed with the MMKKENO and TRIGEX codes. The efficacy of a parallel computational process is investigated as a function of the number of computing kernels used.
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It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.
The various technologies presented herein relate to determining a network attack is taking place, and further to adjust one or more network parameters such that the network becomes dynamically configured. A plurality of machine learning algorithms are configured to recognize an active attack pattern. Notification of the attack can be generated, and knowledge gained from the detected attack pattern can be utilized to improve the knowledge of the algorithms to detect a subsequent attack vector(s). Further, network settings and application communications can be dynamically randomized, wherein artificial diversity converts control systems into moving targets that help mitigate the early reconnaissance stages of an attack. An attack(s) based upon a known static address(es) of a critical infrastructure network device(s) can be mitigated by the dynamic randomization. Network parameters that can be randomized include IP addresses, application port numbers, paths data packets navigate through the network, application randomization, etc.
It appears that there are enormous differences of opinion as to the probability of a [system failure]. The higher figures come from the working engineers, and the very low figures from management. When playing Russian roulette the fact that the first shot got off safely is little comfort for the next. [T]here have been recent suggestions by management to curtail elaborate and expensive tests as being unnecessary. However, this must be resisted. The proper way to save money is to curtail the number of requested changes, not the quality of testing for each.
Traditional Monte Carlo particle transport codes are expected to run inefficiently on next-generation architectures as they are memory-intensive and highly divergent. Since electrons and photons also behave differently, the future for coupled electron-photon radiation transport looks even worse. This project describes preliminary efforts to improve the performance of Monte Carlo particle transport codes when using accelerators like the graphics processing unit (GPU). Two key issues are addressed: how to handle memory-intensive tallies, and how to reduce divergence. Tallying on the GPU can be done efficiently by post-processing particle data, or by using a feature called warp shuffle for summing scores in parallel during the simulation. Reducing divergence is possible by using an event-based algorithm for particle tracking instead of the traditional history-based one. Although performance tests presented in this work show that the history-based algorithm generally outperformed the event-based one for simple problems, this outcome will likely change as the complexity of the code increases.
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