Summary of Research Accomplishments

Abstract

The traditional approach for assessing the performance of scientific applications on HPC platforms consists in executing these applications on these platforms. But conducting these real-world experiments comes with several difficulties. Besides being often time-, labor-, and resource-intensive, experiments are limited to application and platform configurations at hand, thus precluding the exploration of "what if?" scenarios. A way to resolve these difficulties is to resort to simulation. The main concern, then, is that of simulation accuracy. For a simulation to be accurate, the parameters that define the behavior of the simulation models can be calibrated with respect to ground-truth executions. Simulation calibration, in the current state of the art, relies, at best, on labor-intensive manual procedures. We propose an automated simulation calibration approach, and apply this approach to the specific context of the simulation of MPI applications on leadership class HPC platforms. This poster will motivate the development of this approach and detail our methodology and results.

Best Poster Award

Abstract

Introductory Computer Science classes are important for laying the foundation for advanced programming courses. However, students without prior programming experience may find these courses challenging, leading to difficulties in understanding concepts and engaging in academic dishonesty such as plagiarism. While there exists plagiarism detection techniques and tools, not all of them are suitable for academic settings, especially in introductory programming courses. This paper introduces PasteTrace, a novel open-source plagiarism detection tool designed specifically for introductory programming courses. Unlike traditional methods, PasteTrace operates within an Integrated Development Environment that tracks the student's coding activities in real-time for evidence of plagiarism. Our evaluation of PasteTrace in two introductory programming courses demonstrates the tool's ability to provide insights into student behavior and detect various forms of plagiarism, outperforming an existing well-established tool.

Abstract

Many parallel and distributed computing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or ``calibrated," based on ground-truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributed computing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable trade-offs between simulation accuracy and simulation speed.

Abstract

Workflow Management Systems used to automate the execution of scientific workflow applications on parallel and distributed computing platforms must make scheduling decisions at runtime. A large number of workflow scheduling algorithms have been proposed in the literature, but often these algorithms are evaluated based on simplifying assumptions that may not hold in practice. Furthermore, published algorithm evaluation and/or comparison results are necessarily only for a subset of all possible scenarios, and thus may not include scenarios relevant to particular use-cases. Consequently, it is difficult for Workflow Management Systems (WMSs) developers to decide which scheduling algorithm should be implemented. To obviate this difficulty, one possible approach is to implement a portfolio of scheduling algorithms and select the most effective algorithm at runtime. One method for performing this selection is to run an online simulation for each algorithm in the portfolio. The algorithm that leads to the best performance, in simulation, is selected for future use. The above simulation-driven portfolio scheduling (SDPS) approach has been proposed in a few parallel and distributed computing contexts. The main objective of this work is to evaluate the feasibility and potential merit of SDPS if implemented in WMSs. We perform this evaluation using simulated WMS executions, where the simulations are instantiated from real-world platform and workflow configurations. Our main finding is that SDPS is on par with or outperforms an approach in which a single algorithm is used, where this algorithm is the one that performs best on average across all our experimental scenarios. Furthermore, we find that SDPS remains an attractive proposition even in the presence of high levels of simulation error and for simulators with relatively low levels of sophistication. In many of our experimental scenarios we find that mitigating simulation error at runtime can further improve performance. Finally, we show that simulation overhead can be made sufficiently low for SDPS to be feasible in practice.

Abstract

Bathymetry, or the measurement of depth in any body of water, has been an area of research since man began to venture out onto the open waters. Historically, researching the near-shore surf zone has been a time consuming and expensive process. The tools and methods used to gather data points in the surf zone are either time inefficient, expensive, or both. This is an issue considering how dynamic the surf zone environment can be. It is possible that by the time the surf zone bathymetry measurements have been completed, they are already out of date. This project utilizes unmanned aerial systems (UAS) to gather high-quality video of the nearshore surf zone waves crest. This footage is then processed using particle image velocimetry (PIV), a method for determining the velocity of particles in sequential images. This velocity is then processed using linear-wave theory shallow water approximations for calculating wave celerity from depth, but ran in reverse, to obtain the bathymetry itself. Ground-truth field measurements are used to verify the resulting velocity and depth.

Created a program called CASTER - Computer Assisted Segment Tool Environment Revisit. CASTER is designed to handle with "stacks" of SEM scans of mice retina. Each stack has several hundred images totaling ~100GB. CASTER allows the stack to be viewed in "3D" by scrolling through the stack. It also supports segmenting (coloring) with a paint like interface on layers and then exporting this segmentation as a 3d object. The tool was used by biologists for tracing and measuring nerve cells through the stack.

Mainly the tool is designed for the rapid prototyping of automatic and semiautomatic segmentation algorithms. In addition to the standard painting tools (various bushes and a flood fill tool) CASTER features a edge finding brush as well as a dynamically sized brush called RAE CAST that work by projecting lines in all directions until each hits a wall, then coloring in each line.