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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. 

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.