A Massively Parallel Spatially Resolved Stochastic Cluster Dynamics Method for Simulations of Irradiated Materials

Histograms are a concise and flexible way to construct summary structures for large data sets. They have attracted a lot of attention in database research due to their utility in many areas, including query optimization, and approximate query answering. They are also a basic tool for data visualization and analysis.In this paper, we present a formal study of dynamic multidimensional histogram structures over continuous data streams. At the heart of our proposal is the use of a dynamic summary data structure (vastly different from a histogram) maintaining a succinct approximation of the data distribution of the underlying continuous stream. On demand, an accurate histogram is derived from this dynamic data structure. We propose algorithms for extracting such an accurate histogram and we analyze their behavior and tradeoffs. The proposed algorithms are able to provide approximate guarantees about the quality of the estimation of the histograms they extract.We complement our analytical results with a thorough experimental evaluation using real data sets.

Forwarding packets based on networking names is essential for network protocols on different layers, where the `names' could be addresses, packet/flow IDs, and content IDs. For long there have been efforts using dynamic and compact data structures for fast and memory-efficient forwarding. In this work, we identify that the recently developed programmable network paradigm has the potential to further reduce the time/memory complexity of forwarding structures by separating the data plane and control plane. This work presents the new designs of network forwarding structures under the programmable network paradigm, applying three typical dynamic and compact data structures: Bloom filters, Cuckoo hashing, and Othello hashing. We conduct careful analyses and experiments in real networks of these forwarding methods for multiple performance metrics, including lookup throughput, memory footprint, construction time, dynamic updates, and lookup errors. The results give rich insights on designing forwarding algorithms with dynamic and compact data structures. In particular, the new designs based on Cuckoo hashing and Othello hashing show significant advantages over the extensively studied Bloom filter based methods, in all situations discussed in this paper.

3D concrete printing (3DCP) is an innovative construction method that extrudes cementitious materials layer-by-layer to fabricate building components based on a digital model. 3DCP has gained increasing adoption globally for projects like buildings, bridges, retaining walls, and stormwater management systems. However, 3DCP places stringent demands on the rheological properties of the printable cementitious materials. Rheology is critical for the flow and buildability of fresh concrete during extrusion. The mixture must have a low yield stress for pumping, moderate viscosity to hold its shape after deposition, minimal bleeding and segregation of aggregates, as well as responsive rheology that can transition from fluid to solid state as layers are printed sequentially. In this dissertation, we aim to solve the challenges mentioned above including: (1) accurate and efficient quantification of rheological and thixotropic properties of cementitious materials, (2) evaluation of flow behavior of fresh concrete during extrusion. Using traditional experimental methods to evaluate rheological properties of cement paste is labor-intensive and time-consuming. Numerical simulations like Finite Element Method (FEM) or Computational Fluid Dynamics (CFD) can help increase the speed of finding the most suitable material for 3DCP. However, these simulation methods are costly in computing power, making them less desirable for simulating the dynamic and complex process of 3DCP. Moreover, mesh-based approaches are not suitable for complex geometries as the mesh generation would be challenging with curved boundaries, small features, holes or thin regions. Physics-Informed Neural Network (PINN) is introduced in the work as an innovative solution to simulate and model the whole process of 3DCP. It can leverage the flexibility and computational efficiency of data-based neural networks, which can accelerate the learning and convergence speed. At the same time, Partial Differential Equations (PDEs) would be embedded in the network structure to increase the accuracy of its prediction. The integrated governing physical laws can help the neural network understanding the underlying physics of the 3DCP process, potentially increasing the accuracy and reliability of simulations. A significant part of the research is dedicated to understanding the rheological and thixotropic behavior of cementitious materials, which is critical for the material design in 3DCP. We developed a Rheology-informed Neural Network, RheologyNet, where we embedded the rheological constitutive laws into the loss function. These physical laws can help the network focus on simulating the rheological properties of cementitious materials. Moreover, a thixotropic evaluation system has been established based on the proposed model to evaluate the thixotropic property of cement paste. This study is presented in Chapter 2. Then, we proposed a Navier-Stokes Informed Neural Network (NSINN) to study the flow behavior of cement paste in the 3DCP barrel and nozzle since it involves complicated Multiscale or Multiphysics behaviors, such as anisotropy and non-uniform shear rate distribution. The Navier Stokes Equation and the rheological constitutive equations are coupled and embedded into the NSINN architecture to simulate the flow behavior in the barrel during 3D-printing extrusion process. This approach demonstrates the potential of PINNs to provide high-accuracy simulations of velocity and pressure fields in a computationally efficient manner compared to traditional mesh-based models. Also, we used the NSINN to learn the relationship between the nozzle size and the printing quality. The details of the study are presented in Chapter 3. At last, a multi-subnetwork PINN architecture (MultiSubPINN) is proposed to address the limitations of traditional PINNs. This is because traditional PINNs used Fully-connected Neural Network (FNN) as main bone. It has problems like different outputs must share parameters within the network structure. However, this parameter sharing nature might lead to potential errors and decrease the model performance. The main architecture of this network is consisted of multiple separated sub-networks and the loss function of each sub-network is calculated separately to update the parameters in each sub-network. By doing this, we can ensure that the optimization for one output doesn't negatively impact others, therefore providing a more flexible, scalable, and potentially more accurate alternative for the evaluation. Besides, using randomly distributed training points in the computational domain for PINNs is a common way. However, many physical problems exhibit non-uniform behavior, with certain regions of the domain having more complex dynamics or sharper gradients than others. Randomly distributed training points might not adequately capture these regions, leading to a model that misrepresents the underlying physics in critical areas. We proposed a Physical-based sampling strategy to optimize the training of the PINNs. This study is presented in Chapter 4. In summary, this dissertation has established a new platform that is enabled by interpretable NN to accurately quantify cement paste’s rheological properties and simulate the flow behavior of 3D concrete printing process. The findings suggest that PINNs can not only improve the efficiency and accuracy of simulations but also provide a more profound understanding of the material behaviors and process dynamics critical to 3DCP. The novel PINN structure developed from this dissertation is a powerful general platform that could be applicable to a variety of material domains for complex rheological behavior predictions.

Stochastic cluster dynamics method for simulations of multispecies irradiation damage accumulation

Let P be a set of n points in the Euclidean plane and let C be a convex figure. In 1985, Chazelle and Edelsbrunner presented an algorithm, which preprocesses P such that for any query point q, the points of P in the translate C+q can be retrieved efficiently. Assuming that constant time suffices for deciding the inclusion of a point in C, they provided a space and query time optimal solution. Their algorithm uses O(n) space. A query with output size k can be solved in O( log n+k) time. The preprocessing step of their algorithm, however, has time complexity O(n2). We show that the usage of a new construction method for layers reduces the preprocessing time to O(n log n). We thus provide the first space, query time and preprocessing time optimal solution for this class of point retrieval problems. Besides, we present two new dynamic data structures for these problems. The first dynamic data structure allows on-line insertions and deletions of points in O(( log n)2) time. In this dynamic data structure, a query with output size k can be solved in O( log n+k( log n)2) time. The second dynamic data structure, which allows only semi-online updates, has O(( log n)2) amortized update time and O( log n+k) query time.

Positron annihilation in titanium alloys modified by proton irradiation

Subject index (NOTE: 294/1–2 and 294/3)

Automatic parallelization is a promising strategy to improve application performance in the multicore era. However, common programming practices such as the reuse of data structures introduce artificial constraints that obstruct automatic parallelization. Privatization relieves these constraints by replicating data structures, thus enabling scalable parallelization. Prior privatization schemes are limited to arrays and scalar variables because they are sensitive to the layout of dynamic data structures. This work presents Privateer, the first fully automatic privatization system to handle dynamic and recursive data structures, even in languages with unrestricted pointers. To reduce sensitivity to memory layout, Privateer speculatively separates memory objects. Privateer's lightweight runtime system validates speculative separation and speculative privatization to ensure correct parallel execution. Privateer enables automatic parallelization of general-purpose C/C++ applications, yielding a geomean whole-program speedup of 11.4x over best sequential execution on 24 cores, while non-speculative parallelization yields only 0.93x.

Dynamic adaptive data structures for monitoring data streams

We present the newest version of the GROningen MOlecular Simulation program package, GROMOS96. GROMOS96 has been developed for the dynamic modelling of (bio)molecules using the methods of molecular dynamics, stochastic dynamics, and energy minimization as well as the path-integral formalism. An overview of its functionality is given, highlighting methodology not present in the last major release, GROMOS87. The organization of the code is outlined, and reliability, testing, and efficiency issues involved in the design of this large (73 000 lines of FORTRAN77 code) and complex package are discussed. Finally, we present two applications illustrating new functionality: local elevation simulation and molecular dynamics in four spatial dimensions.

With the growth of interconnected power systems in size and complexity, the dynamic stability simulation in the time domain is becoming increasingly time consuming. In order to speed up the dynamic analysis to meet the needs for real-time simulation of large-scale power systems, a coarse grain parallel method is proposed for solving the time-consuming sparse network solution arising from the network analysis. This method has been successfully incorporated to a conventional dynamic stability simulator. Test results obtained from a practical power system illustrated that this parallel method is capable of speeding up the overall dynamic analysis significantly.

The main result of this paper is a new perfect matching algorithm for biconnected cubic graphs. The algorithm runs in time O(n log2 n). It is also possible, by applying randomized data structures, to get O(n logn loglog3 n) average time. Our solution improves the one given by T. Biedl et al. [3]. The algorithm of Biedl et al. runs in time O(n log4 n). We use a similar approach. However, thanks to exploring some properties of biconnected cubic graphs we are able to replace complex fully-dynamic biconnectivity data structure with much simpler, dynamic graph connectivity and dynamic tree data structures. Moreover, we present a significant modification of the new algorithm which makes application of a decremental dynamic graph connectivity data structure possible, instead of one supporting the fully dynamic graph connectivity. It gives hope for further improvements.

New results obtained in experiments on laser irradiation (I = 10 14 W/cm 2 , λ = 1.055μm) of low-density (1-30 mg/cm 3 ) fibrous and foam-like materials are presented and discussed. The effect of low-density material microstructure of irradiated samples on physical processes in high-temperature plasma was investigated. Using a variety of complementary X-ray and optical methods the pronounced effect of material microstructure on the parameters and dynamics of produced plasma was found.

We investigate the query evaluation problem for fixed queries over fully dynamic databases where tuples can be inserted or deleted. The task is to design a dynamic data structure that can immediately report the new result of a fixed query after every database update. We consider unions of conjunctive queries (UCQs) and focus on the query evaluation tasks testing (decide whether an input tuple belongs to the query result), enumeration (enumerate, without repetition, all tuples in the query result), and counting (output the number of tuples in the query result). We identify three increasingly restrictive classes of UCQs which we call t-hierarchical, q-hierarchical, and exhaustively q-hierarchical UCQs. Our main results provide the following dichotomies: If the query's homomorphic core is t-hierarchical (q-hierarchical, exhaustively q-hierarchical), then the testing (enumeration, counting) problem can be solved with constant update time and constant testing time (delay, counting time). Otherwise, it cannot be solved with sublinear update time and sublinear testing time (delay, counting time), unless the OV-conjecture and/or the OMv-conjecture fails. We also study the complexity of query evaluation in the dynamic setting in the presence of integrity constraints, and we obtain similar dichotomy results for the special case of small domain constraints (i.e., constraints which state that all values in a particular column of a relation belong to a fixed domain of constant size).

Boolean Satisfiability (SAT) is a classical NP-complete problem with both theoretical and practical interests. This paper presents our work in developing an application-specific processor for SAT based on a commercial configurable processor core. We customize the processor configuration and design new instruction extensions based on the data structure and atomic operations used in SAT. The customized processor has achieved around 24 /spl times/ speedup at a very low hardware cost. The small size of the processor makes it possible to integrate multiple processors and other customized logic into a single chip for an application-specific multiprocessor solution for SAT. Our work shows the strength of application-specific processing in accelerating applications with complex control and dynamic data structures - an area that has traditionally not been targeted by application-specific processing. It also demonstrates that configurable processor cores can be used to cut the development time and cost for designing and building such application-specific processors.