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This paper presents an overview of an NSF Research Experience for Undergraduate (REU) Site on Trust and Reproducibility of Intelligent Computation, delivered by faculty and graduate students in the Kahlert School of Computing at University of Utah. The chosen themes bring together several concerns for the future in produc- ing computational results that can be trusted: secure, reproducible, based on sound algorithmic foundations, and developed in the context of ethical considerations. The research areas represented by student projects include machine learning, high-performance computing, algorithms and applications, computer security, data science, and human-centered computing. In the first four weeks of the program, the entire student cohort spent their mornings in lessons from experts in these crosscutting topics, and used one-of-a-kind research platforms operated by the University of Utah, namely NSF-funded CloudLab and POWDER facilities; reading assignments, quizzes, and hands-on exercises reinforced the lessons. 

Abstract Symmetric instability is a mechanism that can transfer geostrophic kinetic energy to overturning and dissipation. To date, symmetric instability has only been recognized to occur at the ocean surface or near topographic boundary layers. Analyses of direct microstructure measurements reveal enhanced dissipation caused by symmetric instability in the northwestern equatorial Pacific thermocline, which provides the first observational evidence of subsurface symmetric instability away from boundaries. Enhanced subsurface cross-equatorial exchange provides the negative potential vorticity needed to drive the symmetric instability, which is well reproduced by numerical modeling. These results suggest a new route to energy dissipation for large scale currents, and hence a new ocean turbulent mixing process in the ocean interior. Given the importance of vertical mixing in the evolution of equatorial thermocline, models may need to account for this mechanism to produce more reliable climate projections. 

Automated techniques for analyzing floating-point code for roundoff error as well as control-flow instability are of growing importance. It is important to compute rigorous estimates of roundoff error, as well as determine the extent of control-flow instability due to roundoff error flowing into conditional statements. Currently available analysis techniques are either non-rigorous or do not produce tight roundoff error bounds in many practical situations. Our approach embodied in a new tool called \seesaw employs {\em symbolic reverse-mode automatic differentiation}, smoothly handling conditionals, and offering tight error bounds. Key steps in \seesaw include weakening conditionals to accommodate roundoff error, computing a symbolic error function that depends on program paths taken, and optimizing this function whose domain may be non-rectangular by paving it with a rectangle-based cover. Our benchmarks cover many practical examples for which such rigorous analysis has hitherto not been applied, or has yielded inferior results. 

Automated techniques for analyzing floating-point code for roundoff error as well as control-flow instability are of growing importance. It is important to compute rigorous estimates of roundoff error, as well as determine the extent of control-flow instability due to roundoff error flowing into conditional statements. Currently available analysis techniques are either non-rigorous or do not produce tight roundoff error bounds in many practical situations. Our approach embodied in a new tool called \seesaw employs {\em symbolic reverse-mode automatic differentiation}, smoothly handling conditionals, and offering tight error bounds. Key steps in \seesaw include weakening conditionals to accommodate roundoff error, computing a symbolic error function that depends on program paths taken, and optimizing this function whose domain may be non-rectangular by paving it with a rectangle-based cover. Our benchmarks cover many practical examples for which such rigorous analysis has hitherto not been applied, or has yielded inferior results. 

New abstractions and frameworks are born when one creates hard-coded solutions to important tasks, regardless of whether they scale or result in software that can be meaningfully released. This paper describes our experience creating such a light-weight framework out of a previous tool effort FLiT for detecting compiler-induced numerical variability. The resulting framework FLOAT has already helped us better understand and fix performance bugs in FLiT. Our design of FLOAT and the ways in which we anticipate it enabling the adoption and re-purposing of FLiT, though likely not exhaustive, are described. We also express our views on the appropriate scope of such an approach, especially given that variations of compilation, linking, and execution abound, and specializing in that domain may be advantageous in the long-term as opposed to investing in an overly generalized paradigm.