Global-local graph neural operator for real-time analysis of large-scale CO2 storage
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Software applications need to change and adapt as modern architectures evolve. Nowadays advancement in chip design translates to increased parallelism. Exploiting such parallelism is a major challenge in modern software engineering. Multicore processors are about to introduce a significant change in the way we design and use fundamental data structures. In this work we describe the design and programming principles of a software library of highly concurrent scalable and nonblocking data containers. In this project we have created algorithms and data structures for handling fundamental computations in massively multithreaded contexts, and we have incorporated these into a usable library with familiar look and feel. In this work we demonstrate the first design and implementation of a wait-free hash table. Our multiprocessor data structure design allows a large number of threads to concurrently insert, remove, and retrieve information. Non-blocking designs alleviate the problems traditionally associated with the use of mutual exclusion, such as bottlenecks and thread-safety. Lock-freedom provides the ability to share data without some of the drawbacks associated with locks, however, these designs remain susceptible to starvation. Furthermore, wait-freedom provides all of the benefits of lock-free synchronization with the added assurance that every thread makes progress in a finite number of steps. This implies deadlock-freedom, livelock-freedom, starvation-freedom, freedom from priority inversion, and thread-safety. The challenges of providing the desirable progress and correctness guarantees of wait-free objects makes their design and implementation difficult. There are few wait-free data structures described in the literature. Using only standard atomic operations provided by the hardware, our design is portable; therefore, it is applicable to a variety of data-intensive applications including the domains of embedded systems and supercomputers.Our experimental evaluation shows that our hash table design outperforms the most advanced locking solution, provided by Intel's TBB library, by 22%. When compared to more traditional locking designs we show a performance improvement by a factor of 7.92. When compared to alternative non-blocking designs, our hash table demonstrates solid performance gains in a large majority of cases, typically by a factor of 3.44.
The goals of this project are to develop new, scalable, high-fidelity algorithms for atomic-level simulations and program transformations that automatically restructure existing applications, enabling them to scale forward to Petascale systems and beyond. The techniques enable legacy MPI application code to exploit greater parallelism though increased latency hiding and improved workload assignment. The techniques were successfully demonstrated on high-end scalable systems located at DOE laboratories. Besides the automatic MPI program transformations efforts, the project also developed several new scalable algorithms for ab-initio molecular dynamics, including new massively parallel algorithms for hybrid DFT and new parallel in time algorithms for molecular dynamics and ab-initio molecular dynamics. These algorithms were shown to scale to very large number of cores, and they were designed to work in the latency hiding framework developed in this project. The effectiveness of the developments was enhanced by the direct application to real grand challenge simulation problems covering a wide range of technologically important applications, time scales and accuracies. These included the simulation of the electronic structure of mineral/fluid interfaces, the very accurate simulation of chemical reactions in microsolvated environments, and the simulation of chemical behavior in very large enzyme reactions.
The following work has been performed by PI Hans De Sterck, graduate student Manda Winlaw, postdoc Rasha Kashef, and graduate student Haifeng Xu, for the required tasks 1-5 (as listed in the Statement of Work).
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Wind energy production is characterized by high uncertainty due to the random nature of wind speed, which is weather dependent. Thus, in power systems with a significant penetration of wind generators, the problem of studying the impact of wind farms on the performance of these systems has to be considered in detail and requires the availability of accurate wind power forecasting methods. In addition, the time horizons to be taken into account as references for such studies are becoming more and more variable (from a few hours to a few days) due to the needs of liberalized electricity markets.
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This presentation explores parallel computing dynamics and its application in materials science.
The amount of text data has been growing exponentially in recent years, giving rise to automatic information extraction methods that store text annotations in a database. The current state-of-theart structured prediction methods, however, are likely to contain errors and it’s important to be able to manage the overall uncertainty of the database. On the other hand, the advent of crowdsourcing has enabled humans to aid machine algorithms at scale. As part of this project we introduced pi-CASTLE , a system that optimizes and integrates human and machine computing as applied to a complex structured prediction problem involving conditional random fields (CRFs). We proposed strategies grounded in information theory to select a token subset, formulate questions for the crowd to label, and integrate these labelings back into the database using a method of constrained inference. On both a text segmentation task over academic citations and a named entity recognition task over tweets we showed an order of magnitude improvement in accuracy gain over baseline methods.
This document will describe the principles of the Signal Variation Flow Technique, and the uncertainty models generated using it. We focus on the capture of the variation between the ideal signal and the measured signal. The ideal signal is defined to represent the signal output of a system whose full state behavior is known, with no variation in environment or during operation, and whose photons trajectories are not modified by the object. A CT uncertainty analysis is the result of two main steps. First, the radiography regime extends from the source to the collected image, which is 2D in the case of standard CT. This maps all upstream uncertainties into the variation observed on the radiograph and captures all variation in the physical domain. Second, the reconstruction regime extends from the captured images to the reconstructed 3D image. This regime is purely in the mathematical domain and corrects reconstruction algorithm artifacts/anomalies. The present work focuses on building the model through the radiography regime. The reconstruction regime is expected to be largely a study of algorithmic sensitivity, requiring the definition of a range of standardized tests through which the algorithms would be run. Radiographic variation would then be mapped through reconstruction sensitivities to predict the signal variation in the final image. An incomplete list for the reconstructed image variation output basis includes voxel density, edge blur and length variation, in analogous fashion to the basis functions presented in this work. The signal variation occurs in several forms at the radiograph. These forms are gathered into a complete basis set of functions describing all variation on the radiograph. The scale of each basis function is calculated independently via a specific SVFG. This includes 0D pixel noise (0DI), 0D energy noise (0DE), 1D blur (1DB), 1D length (1DL) or 2D position (2DP). These models are orthogonal in that they each explore a space in the signal variation domain that cannot be reached by the other basis functions. The variation basis functions, and associated SVFG models are split by output dimensionality, a term loosely used for categorization, and explained further below.
Abstract not provided.
This project demonstrates that Chapel programs can interface with MPI-based libraries written in C++ without storing multiple copies of shared data. Chapel is a language for productive parallel computing using global address spaces (PGAS). We identified two approaches to interface Chapel code with the MPI-based Grafiki and Trilinos libraries. The first uses a single Chapel executable to call a C function that interacts with the C++ libraries. The second uses the mmap function to allow separate executables to read and write to the same block of memory on a node. We also encapsulated the second approach in Docker/Singularity containers to maximize ease of use. Comparisons of the two approaches using shared and distributed memory installations of Chapel show that both approaches provide similar scalability and performance.
Abstract not provided.