by Santosh Nagarakatte and Jay Lim on Apr 28, 2022 | Tags: awards, correct math libraries, optimization, runtimes
To create a single polynomial approximation that produces correct results for multiple representations and rounding modes, we propose to generate a polynomial that generates the correctly rounded result of f(x) using the non-standard round-to-odd rounding mode with 2 additional precision bits compared to the largest floating point representation that we wish to support. We provide a proof that this method produces correctly rounded results for multiple representations and for all the standard rounding modes. More detailed explanation of our approach can be found in our POPL 2022 distinguished paper.
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by Jay Lim and Santosh Nagarakatte on Aug 26, 2021 | Tags: awards, correct math libraries, optimization, program synthesis, runtimes
Everyone uses math libraries. Surprisingly, mainstream math libraries do not produce correct results for several thousands of inputs. Developers are seldom aware of them, which affects reproducibility and portability. We describe our new method for synthesizing elementary functions that produces correct results for all inputs but is also significantly faster than the state of the art libraries, which have been optimized for decades.
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by Alejandro Aguirre on Jul 15, 2021 | Tags: awards, formal verification, probabilistic programming
Sensitivity properties study how the output of a program changes when we make small changes to its input. How can we reason about sensitivity of programs that have probabilistic behavior?
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by Craig Disselkoen and Marco Vassena on Apr 21, 2021 | Tags: awards, computer security, program analysis, Spectre
Defending cryptographic code from Spectre attacks is difficult. Blade is a fully automatic approach to eliminate speculative leaks provably and efficiently.
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by Steven Holtzen on Dec 28, 2020 | Tags: awards, exact inference, probabilistic programming
The cost of inference is the primary barrier for wider application of probabilistic programming languages. How can we scale inference to truly huge programs?
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