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Neuroscience initiatives aim to develop new technologies and tools to measure and manipulate neuronal circuits. In this paper, to deal with the massive amounts of data generated by these tools, the authors envision the co-location of open data repositories in standardized formats together with high-performance computing hardware utilizing open source optimized analysis codes.
Purpose: Alpha-emitting radionuclides exhibit a potential advantage for cancer treatments because they release large amounts of ionizing energy over a few cell diameters (50–80 μm), causing localized, irreparable double-strand DNA breaks that lead to cell death. Radioimmunotherapy (RIT) approaches using monoclonal antibodies labeled with α emitters may thus inactivate targeted cells with minimal radiation damage to surrounding tissues. Tools are needed to visualize and quantify the radioactivity distribution and absorbed doses to targeted and nontargeted cells for accurate dosimetry of all treatment regimens utilizing α particles, including RIT and others (e.g., Ra-223), especially for organs and tumors with heterogeneous radionuclide distributions. The aim of this study was to evaluate and characterize a novel single-particle digital autoradiography imager, the ionizing-radiation quantum imaging detector (iQID) camera, for use in α-RIT experiments. Methods: The iQID camera is a scintillator-based radiation detection system that images and identifies charged-particle and gamma-ray/x-ray emissions spatially and temporally on an event-by-event basis. It employs CCD-CMOS cameras and high-performance computing hardware for real-time imaging and activity quantification of tissue sections, approaching cellular resolutions. In this work, the authors evaluated its characteristics for α-particle imaging, including measurements of intrinsic detector spatial resolutions and background count rates at various detector configurations and quantification of activity distributions. The technique was assessed for quantitative imaging of astatine-211 ({sup 211}At) activity distributions in cryosections of murine and canine tissue samples. Results: The highest spatial resolution was measured at ∼20 μm full width at half maximum and the α-particle background was measured at a rate as low as (2.6 ± 0.5) × 10{sup −4} cpm/cm{sup 2} (40 mm diameter detector area). Simultaneous imaging of multiple tissue sections was performed using a large-area iQID configuration (ø 11.5 cm). Estimation of the {sup 211}At activity distribution was demonstrated at mBq/μg-levels. Conclusions: Single-particle digital autoradiography of α emitters has advantages over traditional film-based autoradiographic techniques that use phosphor screens, in terms of spatial resolution, sensitivity, and activity quantification capability. The system features and characterization results presented in this study show that the iQID is a promising technology for microdosimetry, because it provides necessary information for interpreting alpha-RIT outcomes and for predicting the therapeutic efficacy of cell-targeted approaches using α emitters.
has been widely accepted that software virtualization has a big negative impact on high-performance computing (HPC) application performance. This work explores the potential use of Infiniband hardware virtualization in an OpenNebula cloud towards the efficient support of MPI-based workloads. We have implemented, deployed, and tested an Infiniband network on the FermiCloud private Infrastructure-as-a-Service (IaaS) cloud. To avoid software virtualization towards minimizing the virtualization overhead, we employed a technique called Single Root Input/Output Virtualization (SRIOV). Our solution spanned modifications to the Linux’s Hypervisor as well as the OpenNebula manager. We evaluated the performance of the hardware virtualization on up to 56 virtual machines connected by up to 8 DDR Infiniband network links, with micro-benchmarks (latency and bandwidth) as well as w a MPI-intensive application (the HPL Linpack benchmark).
Two-dimensional transition metal dichalcogenides (2D-TMDs)-based memtransistors have emerged as promising candidates for neuromorphic hardware due to their exceptional ability to emulate synaptic behavior. However, many existing 2D-TMDs memtransistors rely on polycrystalline channels with grain boundaries or defects introduced through postgrowth treatments, raising concerns about material integrity and the preservation of intrinsic properties. Here, in this work, we demonstrate a monocrystalline monolayer MoS 2 memtransistor fabricated on a silicon nitride (SiN X ) substrate, achieving a large resistive switching ratio of 10 4 , a dynamic range exceeding 90, along with highly linear and symmetric weight updates, minimal cycle-to-cycle variability, and low device-to-device variability. These attributes are critical for enabling high-performance neuromorphic hardware. Based on experimental data, we further show that these artificial synapses enable a recognition accuracy of more than 97% on the MNIST handwritten digits data set. Our findings present a straightforward approach to realizing 2D-TMDs memtransistors through dielectric engineering, offering a promising platform for next-generation neuromorphic computing systems.
The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.
Abstract We show that the quantum approximate optimization algorithm (QAOA) for higher-order, random coefficient, heavy-hex compatible spin glass Ising models has strong parameter concentration across problem sizes from 16 up to 127 qubits for p = 1 up to p = 5, which allows for computationally efficient parameter transfer of QAOA angles. Matrix product state (MPS) simulation is used to compute noise-free QAOA performance. Hardware-compatible short-depth QAOA circuits are executed on ensembles of 100 higher-order Ising models on noisy IBM quantum superconducting processors with 16, 27, and 127 qubits using QAOA angles learned from a single 16-qubit instance using the JuliQAOA tool. We show that the best quantum processors find lower energy solutions up to p = 2 or p = 3, and find mean energies that are about a factor of two off from the noise-free distribution. We show that p = 1 QAOA energy landscapes remain very similar as the problem size increases using NISQ hardware gridsearches with up to a 414 qubit processor.
Significance To perform hardware-based neuromorphic computing, novel materials exhibiting a wide variety of electronic properties are currently being explored. VO 2 is well known to exhibit an insulator-to-metal transition as well as volatile resistive switching. Many questions regarding the basic mechanism of the nonvolatile switching in this material are unanswered. In this work, the formation and relaxation of conductive filaments through nonvolatile resistive switching in VO 2 devices have been realized. The V 5 O 9 Magnéli phase conductive filament has been identified. Our results demonstrate that both resistive switching behaviors can be achieved in a single material, crucial for future technology like resistive switching memories or neuromorphic logic.
The capabilities of current computer simulations provide a unique opportunity to model small-angle scattering (SAS) data at the atomistic level, and to include other structural constraints ranging from molecular and atomistic energetics to crystallography, electron microscopy and NMR. This extends the capabilities of solution scattering and provides deeper insights into the physics and chemistry of the systems studied. Realizing this potential, however, requires integrating the experimental data with a new generation of modelling software. To achieve this, the CCP-SAS collaboration (http://www.ccpsas.org/) is developing open-source, high-throughput and user-friendly software for the atomistic and coarse-grained molecular modelling of scattering data. Robust state-of-the-art molecular simulation engines and molecular dynamics and Monte Carlo force fields provide constraints to the solution structure inferred from the small-angle scattering data, which incorporates the known physical chemistry of the system. The implementation of this software suite involves a tiered approach in which GenApp provides the deployment infrastructure for running applications on both standard and high-performance computing hardware, and SASSIE provides a workflow framework into which modules can be plugged to prepare structures, carry out simulations, calculate theoretical scattering data and compare results with experimental data. GenApp produces the accessible web-based front end termed SASSIE-web , and GenApp and SASSIE also make community SAS codes available. Applications are illustrated by case studies: (i) inter-domain flexibility in two- to six-domain proteins as exemplified by HIV-1 Gag, MASP and ubiquitin; (ii) the hinge conformation in human IgG2 and IgA1 antibodies; (iii) the complex formed between a hexameric protein Hfq and mRNA; and (iv) synthetic `bottlebrush' polymers.
Finding where people live and the vulnerabilities of man-made facilities during natural disasters, is not only critical for rescue efforts, but also essential for damage assessment in the aftermath. Leveraging on the availability of high resolution satellite imagery, advances in machine learning and high performance computing hardware, it is now possible to generate geographical maps for man-made facilities at scale. Mapping from satellite imagery can be a daunting task due to the enormous amount of data to be processed over large areas. In this short paper we take advantage of annotated satellite imagery and automate the semantic labeling of mobile home parks using an efficient framework rooted in patch-based and pixel-level classification. This multilevel labeling effort is a precursor to our future goal for deploying very large scale deep convolutional neural networks toward both broad and finer characterization of man-made structures from one-meter resolution NAIP images.
Distributed energy resource (DER) systems are rapidly being adopted and integrated within the electric power grid. Developments in smart grid devices and communication protocols are advancing the power system domain but are also introducing new cyber attack vectors. In particular, the ability to maintain the confidentiality, integrity, and availability of data is of increasing concern. Cryptography is a powerful tool that can be leveraged to protect DER systems and their critical information. This paper discusses prominent methods of cryptographic authentication and encryption that can be used to secure DER communications. Specific considerations and recommendations for applying cryptography to DER systems are provided, including system design constraints and system impact. These will be demonstrated with two case studies that assess cryptography hardware requirements and communications latency in DERs.
y Reverse-mode algorithmic differentiation (AD) is an established method for obtaining adjoint derivatives of computer simulation applications. In computational fluid dynamics (CFD), adjoint derivatives of a cost function output such as drag or lift with respect to design parameters such as surface coordinates or geometry control points are a key ingredient for shape optimization, uncertainty quantification and flow control. The computational cost of CFD applications and their derivatives makes it essential to use high-performance computing hardware efficiently, including multi- and many-core architectures. Nevertheless, OpenMP is not supported in most AD tools, and previously shown methods achieve poor scalability of the derivative code. We present the AD of an OpenMP-parallelized finite volume compressible flow solver for unstructured meshes. Our approach enables us to reuse the parallelization of the original code in the computation of adjoint derivatives. The method works by identifying code segments that can be differentiated in reverse-mode without changing their memory access pattern. The OpenMP parallelization is integrated into the derivative code during the build process in a way that is robust to modifications of the original code and independent of the OpenMP support of the differentiation tool. We show the scalability of our adjoint CFD solver on test cases ranging from thousands to millions of finite volume mesh cells on CPUs with up to 16 threads as well as on an Intel XeonPhi card with 236 threads. We demonstrate that our approach is more practical to implement for production-sized CFD codes and produces more efficient adjoint derivative code than previously shown AD methods.
Fluorinated gases are critical to a number of high-technology industries including semiconductor manufacturing and related processes such as flat panel display, LED, and photovoltaic manufacturing. These fluorinated gases have very high global warming potentials (GWPs) and, unfortunately are chemically very stable, in some cases lasting for thousands of years in the atmosphere. For environmental reasons, it is imperative that the release of these gases be minimized, but existing methods to destroy them requires a significant amount of energy. The goal of this project was to develop a more energy efficient method of destroying these high GWP gases using computational fluid dynamics, but the computational requirements are significant. In this project, we bring together resources available at the national labs, including high-performance computing hardware and modern computational methods, in order to explore an industrially important problem with key environmental and economic impact. In this project, we examined the state-of-the-art models for the physical processes involved, and made subtle changes to arrive at alternative formulations considerably better suited for computation and study. We examined several representative scenarios and developed a set of analyses techniques and tools that could be used to explore the systems in much greater detail. This exploratory project has demonstrated to ALZETA that HPC can indeed provide a unique view into their complex engineering problem - one that is simply inaccessible by any other means. The tools allow us to explore the details of the chemical process, and allow one to construct "what-if" scenarios to address shortcomings of the physical device in its current operating modes. However, as with many sources of completely new information, many new questions are illuminated with this capability. It will take considerable time, effort and experience before we learn to probe the solutions, pose operating scenarios and confirm modifications to the device that lead to real engineering advances.
MLIR (Multi-Level Intermediate Representation), is an extensible compiler framework that supports high-level data structures and operation constructs. These higher-level code representations are particularly applicable to the artificial intelligence and machine learning (AI/ML) domain, allowing developers to more easily support upcoming heterogeneous AI/ML accelerators and develop flexible domain specific compilers/frameworks with higher-level intermediate representations (IRs) and advanced compiler optimizations. The result of using MLIR within the LLVM compiler framework is expected to yield significant improvement in the quality of generated machine code, which in turn will result in improved performance and hardware efficiency
Imaging opportunities (viewable conjunctions) of Resident Space Objects (RSOs) by satellites are not continuously discovered. We propose to continuously produce and report viewable conjunctions among objects in orbit. Viewable conjunctions are events in space and time when a satellite may favorably view a Resident Space Object (RSO). Favorability is defined by a set of constraints, e.g., solar illumination, distance between observer and target, orbital location for viewable event. Computing viewable conjunctions requires calculation of orbital propagation while considering constraints based on the state vectors of position, velocity, with covariance for both satellite and RSO. We propose two parallel lanes of effort: acceleration and research. The objective of acceleration is to avoid missed opportunities and reduce latency for satellite maneuver requests through continuous prediction and reporting of viewable conjunctions. The effort will begin by deploying currently available software on dedicated systems and continue with optimizing the code for high performance computing hardware. The research lane aims to expand RSO inspection and modeling capabilities. Among our current research ideas are spectral characterization of RSO materials and planning multiple observations to recover RSO 3D form. Computing resources at Oak Ridge National Laboratory (ORNL) are available for the acceleration work. Laika, Maxar conjunction prediction dashboard software, and Bluesim, Maxar orbital propagation software, are expected to be the first software in the acceleration lane. Laike and Bluesim are to be provided by the sponsor, and output will be made accessible through its dashboard. Deliverables will follow a gated schedule to the sponsor. ORNL will provide progressively more robust viewable conjunction assessments from both modelled and actual ephemerides.
Neutrino experiments like DUNE are limited by how well we understand the way neutrinos actually interact with atoms. The best way to fix that is to measure interactions on hydrogen, since a hydrogen nucleus is just a single free proton with largely none of the nuclear effects that heavier targets like argon have. The catch is that no one has built a hydrogen bubble chamber for a neutrino beam since the 1980s, mostly because the old chambers had too low of an active time. MAMBA is a project focused on bringing that technology back but built to cycle continuously at 1 Hz. I spent my summer internship on two of the subsystems that make that kind of speed possible. First, I helped install and test a new solid copper thermal link that replaced the old flexible braided cooling path, and it brought the coldhead down to 21.3 K, right at the target temperature. Second, I built and tested a Raspberry Pi based PLC control system, and across 20,000 trials it answered in a median of 0.64 ms, with only 0.66\% of trials running past 1 ms. Both results point the same direction: that inexpensive, off the shelf hardware can perform fast and reliable enough to eventually run a bubble chamber the way MAMBA needs it to run.
With the planned addition of tracking information to the Compact Muon Solenoid (CMS) Level-1 trigger for the High-Luminosity Large Hadron Collider (HL-LHC), the trigger algorithms can be completely reconceptualized. We explore the feasibility of using particle flow-like reconstruction and pileup per particle identification (PUPPI) pileup mitigation at the hardware trigger level. This represents a new type of multi-subdetector pattern recognition challenge for the HL-LHC. We present proof-of-principle studies on both physics and hardware-resource performance of a prototype algorithm for use by CMS in the HL-LHC era.
In various embodiments the present disclosure provides a system and a method for estimating energy usage in a vehicle. The system includes hardware that perform certain operations that include receiving data associated with a route to be taken by the vehicle. The operations include determining, based on the data associated with the route and a state of the vehicle, an estimate corresponding to the energy that would be used by the vehicle when traversing a segment of the route.
Abstract not provided.