End-to-end Performance Modeling of Distributed GPU Applications
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We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endcap. The ML algorithm is trained to predict clusters of hits originating from the same incident particle by labeling the hits with the same cluster index. We impose simple criteria to assess whether the hits associated as a cluster by the prediction are matched to those hits resulting from any particular individual incident particles. The algorithm is studied by simulating two tau leptons in each of the two HGCAL endcaps, where each tau may decay according to its measured standard model branching probabilities. The simulation includes the material interaction of the tau decay products which may create additional particles incident upon the calorimeter. Using this varied multiparticle environment we can investigate the application of this reconstruction technique and begin to characterize energy containment and performance.
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Cyber-physical systems require reliable, safe, and secure control of critical infrastructure, combining computational and networking capabilities, which heighten the risk of cyber attacks. These attacks can disrupt the physical process, causing unforeseen consequences. One solution is the use of fully homomorphic encryption (FHE) to protect the control loop, allowing for secure computations and communications without compromising signal and control system privacy. The challenge with FHE, however, is its requirement for inputs to be integers. This presentation introduces a modified Learning With Errors (LWE) FHE approach that encodes control system dynamics and signals into integers. Our proposed scheme leverages a generalized LWE encoding function and modifies the Gentry-Sahai-Waters gadget decomposition tool to encrypt the control system. Using the modified LWE scheme, we formalize a fully encrypted control system, supported by simulated results.
Deep neural networks are known to be resilient to random bit-wise faults in their parameters. However, this resilience has primarily been established through evaluations of classification models. The extent to which this claim holds for large-language models remains underexplored. In this work, we conduct an extensive measurement study on the impact of random bitwise faults in commercial-scale language models. We perform an in-depth analysis of the resulting generation outputs. We first expose that these language models are not truly resilient to random bit-flips. While aggregate metrics such as accuracy may suggest resilience, an in-depth inspection of the generated outputs shows significant degradation in text quality. Our analysis also shows that tasks requiring more complex reasoning suffer more from performance and quality degradation. Moreover, we extend our analysis to models with augmented reasoning capabilities, such as Chain-of-Thought or Mixture of Experts architectures, and characterize their failure scenarios under random bit-flips.
A fuel rod for a nuclear reactor, including a cladding tube having a first end with an annular end face, a second end with an annular end face, and a cylindrical body portion extending therebetween, and a first tube end plug including a front portion, an annular lip with an annular end face, and a substantially straight cylindrical body portion extending therebetween, wherein the surface area of the annular end face of the first end of the cladding tube and the annular end face of the annular lip of the first tube end plug are substantially equal, and the annular end face of the first end of the cladding tube and the annular end face of the annular lip of the first tube end plug are connected by a resistance pressure weld.
In the United States, construction and demolition debris predominately end up in landfills with minimal end‐of‐life Re‐X (recover, recycle, reuse, etc.) scenarios, resulting in large environmental impacts and lost opportunities for material recovery. Except for concrete and metals, which seem to have a few well‐defined end‐of‐life pathways, there seems to be a lack of well‐documented end‐of‐life scenarios for other construction materials, let alone their emissions data. Hence, there is a need for documented end‐of‐life Re‐X scenarios and end‐of‐life data of more building materials to motivate widespread use of Re‐X strategies in building design. This paper outlines the efforts of the National Renewable Energy Laboratory, Carbon Leadership Forum, Building Transparency, and Skidmore, Owings & Merrill to (a) create an open‐access BRE‐X (Building Re‐X) end‐of‐life emissions database consisting of greenhouse gas emissions data associated with various end‐of‐life scenarios for a select list of high‐impact building construction materials, and (b) integrate the BRE‐X end‐of‐life emissions database with CAD/BIM/LCA tools for evaluating various end‐of‐life scenarios. The paper also presents a few existing life cycle inventory databases that contain sparse amounts of end‐of‐life data for a few construction materials and their limitations in terms of scaling and data consolidation. Finally, a sample of how the collected data can be ingested into whole‐building LCA tools using open data formats and a public access link to the BRE‐X end‐of‐life emissions database is also included.
As a part of the design of the MITR Design Demonstration Element (DDE), a new design for the end fittings must be considered to meet specific experiment needs, e.g., compatibility with the geometrical restrictions of the DDE testing location (BR2 test reactor located in Mol, Belgium), and to allow for proper inspection of these test elements during the experiments. The design of the MITR DDE end fittings is substantially different from the design of the end fittings of the MITR LEU fuel elements, with the DDE end fittings considerably smaller in overall size and weight. Consequently, the stiffness of the end fittings as well as of the entire fuel element is affected. The end fittings in the MITR LEU fuel element are designed primarily for proper handling and flow distribution. The stiffness of the end fittings also constitutes a fraction of the total stiffness of the entire fuel element. It is debatable to what extent the end fittings provide the structural rigidity necessary for the fuel element to properly respond to potential loadings. It is presumed that the bulk of the overall stiffness of the element is derived from swaging multiple fuel plates into the side plates. However, the contribution of stiffness from various components of the MITR LEU fuel element and MITR DDE has not been determined so far. The primary objective of the work presented in this report was to assess the extent to which the stiffness of the end fitting in the MITR DDE contributes to the stiffness of the entire fuel element, and how it compares to the equivalent stiffness of the end fitting in the MITR LEU fuel element. Several simplified load cases were analyzed for that purpose using the geometries of the MITR LEU fuel element, MITR DDE, as well as that of the MITR DDE without the top end fitting.
Cellulose reducing ends are believed to play a vital role in the cellulose recalcitrance to enzymatic conversion. However, their role in insoluble cellulose accessibility and hydrolysis is not clear. Thus, in this study, reducing ends of insoluble cellulose derived from various sources were modified by applying reducing and/or oxidizing agents. Here, the effects of cellulose reducing ends modification on cellulose reducing ends, cellulose structure, and cellulose accessibility to cellulase were evaluated along with the impact on cellulose hydrolysis with complete as well purified cellulase components. Sodium borohydride (NaBH 4 ) reduction and sodium chlorite-acetic acid (SC/AA) oxidation were able to modify more than 90% and 60% of the reducing ends, respectively, while the bicinchoninic acid (BCA) reagent applied for various cycles oxidized cellulose reducing ends to various extents. X-ray diffractograms of the treated solids showed that these treatments did not change the cellulose crystalline structure and the change in crystallinity index was insignificant. Surprisingly, it was found that the cellulose reducing ends modification, either through selective NaBH 4 reduction or BCA oxidation, had a negligible impact on cellulose accessibility as well on cellulose hydrolysis rates or final conversions with complete cellulase at loadings as low as 0.5 mg protein/g cellulose. In fact, in contrast to what is traditionally believed, modifications of cellulose reducing ends by these two methods had no apparent impact on cellulose conversion with purified cellulase components and their synergy. However, SC/AA oxidation resulted in significant drop in cellulose conversion (10%–50%) with complete as well purified cellulase components. Nonetheless, further research revealed that the cause for drop in cellulose conversion for the SC/AA oxidation case was due to primary hydroxyl groups (PHGs) oxidation and not the oxidation of reducing ends. Furthermore, it was found that the PHGs modification affects cellulose accessibility and slows the cellulase uptake as well resulting in significant drop in cellulose conversions.
Hydrograph separation is one of many approaches used to analyse shifts in source water contributions to stream flow resulting from climate change in remote watersheds. Understanding these shifts is vital, as shifts in source water contributions to a stream can shape water management decisions. Because remote watersheds are often inaccessible and have poorly characterized contributing water sources, or end-members, it is critical to understand the implications of using different hydrograph separation techniques in these data-limited environments. To explore the uncertainty associated with different techniques, results from two hydrograph separation techniques, mass balance and principle component analysis, were compared using 3 years of aqueous geochemical data from the East River watershed located in the Elk Mountains of Central Colorado. Solute concentrations of the end-members were characterized by both a limited set of direct chemical measurements of different sources and detailed seasonal instream chemistry to examine the influences of uncertain end-member compositions in a data-limited environment. Annual volumetric end-member contributions to stream flow had relatively good agreement across separation techniques. Large variations in time were observed in the hydrograph separations, depending on the end-member type, and estimated flow contributions varied between the selected solutes. End-member concentrations characterized by stream chemistry showed several limitations including a reduced number of distinguishable end-members and differences in timing of flow contributions. Here the results highlight the benefits of using multiple hydrograph separation techniques by providing a ‘weight-of-evidence’ approach to environments with limited end-member concentration data.