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Articles 1 - 8 of 8
Full-Text Articles in Computer Sciences
Reducing Memory Access Latencies Using Data Compression In Sparse, Iterative Linear Solvers, Neil Lindquist
Reducing Memory Access Latencies Using Data Compression In Sparse, Iterative Linear Solvers, Neil Lindquist
All College Thesis Program, 2016-2019
Solving large, sparse systems of linear equations plays a significant role in certain scientific computations, such as approximating the solutions of partial differential equations. However, solvers for these types of problems usually spend most of their time fetching data from main memory. In an effort to improve the performance of these solvers, this work explores using data compression to reduce the amount of data that needs to be fetched from main memory. Some compression methods were found that improve the performance of the solver and problem found in the HPCG benchmark, with an increase in floating point operations per second …
Ai-Human Collaboration Via Eeg, Adam Noack
Ai-Human Collaboration Via Eeg, Adam Noack
All College Thesis Program, 2016-2019
As AI becomes ever more competent and integrated into our lives, the issue of AI-human goal misalignment looms larger. This is partially because there is often a rift between what humans explicitly command and what they actually mean. Most contemporary AI systems cannot bridge this gap. In this study we attempted to reconcile the goals of human and machine by using EEG signals from a human to help a simulated agent complete a task.
The Algorithmic Composition Of Classical Music Through Data Mining, Tom Donald Richmond, Imad Rahal
The Algorithmic Composition Of Classical Music Through Data Mining, Tom Donald Richmond, Imad Rahal
All College Thesis Program, 2016-2019
The desire to teach a computer how to algorithmically compose music has been a topic in the world of computer science since the 1950’s, with roots of computer-less algorithmic composition dating back to Mozart himself. One limitation of algorithmically composing music has been the difficulty of eliminating the human intervention required to achieve a musically homogeneous composition. We attempt to remedy this issue by teaching a computer how the rules of composition differ between the six distinct eras of classical music by having it examine a dataset of musical scores, rather than explicitly telling the computer the formal rules of …
Making Scientific Applications Portable: Software Containers And Package Managers, Curtis Noecker
Making Scientific Applications Portable: Software Containers And Package Managers, Curtis Noecker
All College Thesis Program, 2016-2019
Scientific workflows for high-performance computing (HPC) are becoming increasingly complex. Developing a way to simplify these workflows could save many hours for both HPC users and developers, potentially eliminating any time spent managing software dependencies and experiment set-up. To accomplish this, we propose using two programs together: Docker and Spack. Docker is a container platform and Spack is a package manager designed specifically for HPC. In this paper, we show how Docker and Spack can be used to containerize the extreme-scale Scientific Software Development Kit (xSDK). Doing this makes the xSDK far more accessible to non-computer scientists and lowers time …
High Performance Techniques Applied In Partial Differential Equations Library, Shilei Lin
High Performance Techniques Applied In Partial Differential Equations Library, Shilei Lin
All College Thesis Program, 2016-2019
This thesis explores various Trilinos packages to determine a method for updating the deal.ii library, which specializes in solving partial differential equations by finite element methods. It begins with introducing related concepts and general goals, followed by exploring computational and mathematical methods which are analytical solutions of one-dimensional Boussinesq equations and developing newer prototypes for solvers in deal.ii based on Trilinos packages. After demonstrating the methods, it indicates the reducing solving time in newer prototypes. Based on results from the prototype, similar methods are applied to update the deal.ii library. In the end, a testing program is exploited to demonstrate …
Hpc Made Easy: Using Docker To Distribute And Test Trilinos, Sean J. Deal
Hpc Made Easy: Using Docker To Distribute And Test Trilinos, Sean J. Deal
All College Thesis Program, 2016-2019
Virtualization is an enticing option for computer science research given its ability to provide repeatable, standardized environments, but traditional virtual machines have too much overhead cost to be practical. Docker, a Linux-based tool for operating-system level virtualization, has been quickly gaining popularity throughout the computer science field by touting a virtualization solution that is easily distributable and more lightweight than virtual machines. This thesis aims to explore if Docker is a viable option for conducting virtualized research by evaluating the results of parallel performance tests using the Trilinos project.
Performance Portable High Performance Conjugate Gradients Benchmark, Zachary Bookey
Performance Portable High Performance Conjugate Gradients Benchmark, Zachary Bookey
All College Thesis Program, 2016-2019
The High Performance Conjugate Gradient Benchmark (HPCG) is an international project to create a more appropriate benchmark test for the world's most powerful computers. The current LINPACK benchmark, which is the standard for measuring the performance of the top 500 fastest computers in the world, is moving computers in a direction that is no longer beneficial to many important parallel applications. HPCG is designed to exercise computations and data access patterns more commonly found in applications. The reference version of HPCG exploits only some parallelism available on existing supercomputers and the main focus of this work was to create a …
Retrival Of Atmospheric Aerosol Size Distributions Using Stochastic Particle Swarm Optimization, Benjamin D. Nault-Maurer
Retrival Of Atmospheric Aerosol Size Distributions Using Stochastic Particle Swarm Optimization, Benjamin D. Nault-Maurer
All College Thesis Program, 2016-2019
A stochastic particle swarm optimization (SPSO) technique’s robustness is studied in regards to atmospheric aerosol size distribution estimations for a bimodal distribution that focuses on Aitken and accumulation mode aerosols. The SPSO method is used to calculate a set of 11 aerosol optical depth (AOD) values based on a size distribution and match them to an inputted set of AOD values. This method is tested using computer generated AOD values with fixed distribution parameters, generated AOD values with varying distribution parameters, two sets of AOD measurements in clear conditions, and one set of AOD values in hazy conditions. The SPSO …