Systems Architecture Commons^™

Towards Expressive And Versatile Visualization-As-A-Service (Vaas), Tanner C. Hobson

Doctoral Dissertations

The rapid growth of data in scientific visualization has posed significant challenges to the scalability and availability of interactive visualization tools. These challenges can be largely attributed to the limitations of traditional monolithic applications in handling large datasets and accommodating multiple users or devices. To address these issues, the Visualization-as-a-Service (VaaS) architecture has emerged as a promising solution. VaaS leverages cloud-based visualization capabilities to provide on-demand and cost-effective interactive visualization. Existing VaaS has been simplistic by design with focuses on task-parallelism with single-user-per-device tasks for predetermined visualizations. This dissertation aims to extend the capabilities of VaaS by exploring data-parallel visualization …

Optimizing Collective Communication For Scalable Scientific Computing And Deep Learning, Jiali Li

Doctoral Dissertations

In the realm of distributed computing, collective operations involve coordinated communication and synchronization among multiple processing units, enabling efficient data exchange and collaboration. Scientific applications, such as simulations, computational fluid dynamics, and scalable deep learning, require complex computations that can be parallelized across multiple nodes in a distributed system. These applications often involve data-dependent communication patterns, where collective operations are critical for achieving high performance in data exchange. Optimizing collective operations for scientific applications and deep learning involves improving the algorithms, communication patterns, and data distribution strategies to minimize communication overhead and maximize computational efficiency.

Within the context of this …

Interdomain Route Leak Mitigation: A Pragmatic Approach, Benjamin Tyler Mcdaniel

Doctoral Dissertations

The Internet has grown to support many vital functions, but it is not administered by any central authority. Rather, the many smaller networks that make up the Internet - called Autonomous Systems (ASes) - independently manage their own distinct host address space and routing policy. Routers at the borders between ASes exchange information about how to reach remote IP prefixes with neighboring networks over the control plane with the Border Gateway Protocol (BGP). This inter-AS communication connects hosts across AS boundaries to build the illusion of one large, unified global network - the Internet. Unfortunately, BGP is a dated protocol …

Programming Models' Support For Heterogeneous Architecture, Wei Wu

Doctoral Dissertations

Accelerator-enhanced computing platforms have drawn a lot of attention due to their massive peak computational capacity. Heterogeneous systems equipped with accelerators such as GPUs have become the most prominent components of High Performance Computing (HPC) systems. Even at the node level the significant heterogeneity of CPU and GPU, i.e. hardware and memory space differences, leads to challenges for fully exploiting such complex architectures. Extending outside the node scope, only escalate such challenges.

Conventional programming models such as data- ow and message passing have been widely adopted in HPC communities. When moving towards heterogeneous systems, the lack of GPU integration causes …

Achieving High Reliability And Efficiency In Maintaining Large-Scale Storage Systems Through Optimal Resource Provisioning And Data Placement, Lipeng Wan

Doctoral Dissertations

With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues.

First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience …

Interactive Feature Selection And Visualization For Large Observational Data, Jingyuan Wang

Doctoral Dissertations

Data can create enormous values in both scientific and industrial fields, especially for access to new knowledge and inspiration of innovation. As the massive increases in computing power, data storage capacity, as well as capability of data generation and collection, the scientific research communities are confronting with a transformation of exploiting the advanced uses of the large-scale, complex, and high-resolution data sets in situation awareness and decision-making projects. To comprehensively analyze the big data problems requires the analyses aiming at various aspects which involves of effective selections of static and time-varying feature patterns that fulfills the interests of domain users. …

Dynamic Task Execution On Shared And Distributed Memory Architectures, Asim Yarkhan

Doctoral Dissertations

Multicore architectures with high core counts have come to dominate the world of high performance computing, from shared memory machines to the largest distributed memory clusters. The multicore route to increased performance has a simpler design and better power efficiency than the traditional approach of increasing processor frequencies. But, standard programming techniques are not well adapted to this change in computer architecture design.

In this work, we study the use of dynamic runtime environments executing data driven applications as a solution to programming multicore architectures. The goals of our runtime environments are productivity, scalability and performance. We demonstrate productivity by …

A Scalable Architecture For Simplifying Full-Range Scientific Data Analysis, Wesley James Kendall

Doctoral Dissertations

According to a recent exascale roadmap report, analysis will be the limiting factor in gaining insight from exascale data. Analysis problems that must operate on the full range of a dataset are among the most difficult. Some of the primary challenges in this regard come from disk access, data managment, and programmability of analysis tasks on exascale architectures. In this dissertation, I have provided an architectural approach that simplifies and scales data analysis on supercomputing architectures while masking parallel intricacies to the user. My architecture has three primary general contributions: 1) a novel design pattern and implmentation for reading multi-file …