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 Simon Marlow, Simon Peyton Jones, and Satnam Singh on Dec 16, 2019 | Tags: concurrency, functional programming, Haskell, MIP award, parallelism, runtimes
Runtime Support for Multicore Haskell (ICFP’09) was awarded the SIGPLAN ten-year most-influential paper award in 2019. In this blog post we reflect on the journey that led to the paper, and what has happened since.
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