Starting in the 1990s, immigrants began to settle throughout the United States, often moving to new destinations far from the traditional receiving communities. This paper is a case study of Clark County, Ohio, which has seen its immigrant population grow rapidly in the past two decades, with many of these newcomers coming from the small town of El Saúz de Abajo in the state of Michoacán. Many in this group express a strong sense of stability in their lives in this new destination and I ask what people mean when they express this sentiment. This paper argues that the sense of stability people express is complex and that it can only be understood relative to their past experiences.

In my dissertation, I take up the question of immigrant assimilation in a unique way, using youth soccer as a lens to investigate the topic. I examine the role that the sport has played in the assimilation of immigrants, focusing particularly on its potential to develop social capital between immigrants and non-immigrants. Over 18 months of fieldwork with three youth soccer clubs in San Diego, dozens of interviews with experts, and archival research into the history of youth soccer in the United States, I find that the sport does little to bring together immigrants and non-immigrants. Although Latinos, the largest immigrant group in Southern California, play soccer in significant numbers, they play it mostly separately from the affluent suburbanites who are the other main group involved with the sport. The history of soccer, long seen as an un-American sport until it became the sport of youth in the growing suburbs in the post- World War II period, helps to explain this finding. As soccer has been taken up by increasingly affluent suburbanites looking for a sport to serve as a means of distinction, it has enabled them to perform their social status. The world of suburban soccer today remains one of affluence, and many involved with this world have much to gain from it retaining this status. Although young male Latino players, the children of largely working-class Latino immigrants who have arrived in such large numbers since 1965, are often among the most talented youth players, they are often underrepresented on top-level youth soccer teams. Sports have often been presented as a panacea to social problems, including immigrant assimilation. Political scientist Robert Putnam has suggested that sports can foster connections between diverse people who might not otherwise interact. But Putnam's idea that sports can foster the development of social capital between diverse people assumes everyone comes to the field on equal footing. As my dissertation shows, the playing field is far from equal, and in the end youth soccer does more to reinforce the continued segregation of immigrants than it does to foster their assimilation

We present a robust and scalable preconditioner for the solution of large-scale linear systems that arise from the discretization of elliptic PDEs amenable to rank compression. The preconditioner is based on hierarchical low-rank approximations and the cyclic reduction method. The setup and application phases of the preconditioner achieve log-linear complexity in memory footprint and number of operations, and numerical experiments exhibit good weak and strong scalability at large processor counts in a distributed memory environment. Numerical experiments with linear systems that feature symmetry and nonsymmetry, definiteness and indefiniteness, constant and variable coefficients demonstrate the preconditioner applicability and robustness. Furthermore, it is possible to control the number of iterations via the accuracy threshold of the hierarchical matrix approximations and their arithmetic operations, and the tuning of the admissibility condition parameter. Together, these parameters allow for optimization of the memory requirements and performance of the preconditioner.

We exploit the high memory bandwidth of AI-customized Cerebras CS-2 systems for seismic processing. By leveraging low-rank matrix approximation, we fit memory-hungry seismic applications onto memory-austere SRAM wafer-scale hardware, thus addressing a challenge arising in many wave-equation-based algorithms that rely on Multi-Dimensional Convolution (MDC) operators. Exploiting sparsity inherent in seismic data in the frequency domain, we implement embarrassingly parallel tile low-rank matrix-vector multiplications (TLR-MVM), which account for most of the elapsed time in MDC operations, to successfully solve the Multi-Dimensional Deconvolution (MDD) inverse problem. By reducing memory footprint along with arithmetic complexity, we fit a standard seismic benchmark dataset into the small local memories of Cerebras processing elements. Deploying TLR-MVM execution onto 48 CS-2 systems in support of MDD gives a sustained memory bandwidth of 92.58PB/s on 35, 784, 000 processing elements, a significant milestone that highlights the capabilities of AI-customized architectures to enable a new generation of seismic algorithms that will empower multiple technologies of our low-carbon future.

The challenges of visualization at the extreme scale involve issues of scale, complexity, temporal exploration and uncertainty. The Visualization and Analytics Center for Enabling Technologies (VACET) focuses on leveraging scientific visualization and analytics software technology as an enabling technology to increased scientific discovery and insight. In this paper, we introduce new uses of visualization frameworks through the introduction of Equivalence Class Functions (ECFs). These functions give a new class of derived quantities designed to greatly expand the ability of the end user to explore and visualize data. ECFs are defined over equivalence classes (i.e., groupings) of elements from an original mesh, and produce summary values for the classes as output. ECFs can be used in the visualization process to directly analyze data, or can be used to synthesize new derived quantities on the original mesh. The design of ECFs enable a parallel implementation that allows the use of these techniques on massive data sets that require parallel processing.

The Visualization and Analytics Center for Enabling Technologies (VACET) focuses on leveraging scientific visualization and analytics software technology as an enabling technology for increasing scientific productivity and insight. Advanced in computational technology have resulted in an ``information big bang,'' which in turn has created a significant data understanding challenge. This challenge is widely acknowledged to be one of the primary bottlenecks in contemporary science. The vision of VACET is to adapt, extend, create when necessary, and deploy visual data analysis solutions that are responsive to the needs of DoE's computational and experimental scientists. Our center is engineered to be directly responsive to those needs and to deliver solutions for use in DoE's large open computing facilities. The research and development directly target data understanding problems provided by our scientific application stakeholders. FACET draws from a diverse set of visualization technology ranging from production quality applications and application frameworks to state-of-the-art algorithms for visualization, analysis , analytics, data manipulation, and data management.