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# PL Perspectives

Perspectives on computing and technology from and for those with an interest in programming languages.

Real programming languages are living things, changing and evolving. As with any production code, most of their designer’s time is spent on bug fixing and small improvements, rather than on the radical new features. One of the unique things about Kotlin is that it has been evolving in the use-case and community-driven fashion for years, starting way before it went to the stable 1.0 release in 2016, even for some time before it went public in 2011. Language design in the real world is a maintenance of a complex system and it runs into novel research questions with respect to type systems, feature interactions, usability, real-life code patterns in big code, etc.

## One Polynomial Approximation to Produce Correctly Rounded Results for Multiple Representations and Rounding Modes

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.

## Improving gender balance in academia, a computer scientist’s suggestion of where to start (part 2 of 2)

We all need to step up and start working to improve gender equality in computer science and engineering. This is the second of two blog posts about this fascinating and frustrating problem, in which I provide three suggestions for how to get started.

## Improving gender balance in academia: a computer scientist’s suggestion of where to start (part 1 of 2)

We all need to step up and start working to improve gender equality in computer science and engineering. In two blog posts, I give my view of this fascinating and frustrating problem. I provide three suggestions for how to get started.

## Provably Space-Efficient Parallel Functional Programming

You have heard your grandmother tell you many times: parallel programming is hard. In 2022, does it still have to be? Back in grandma’s heyday, they knew a cool and breezy way to do parallelism: pure functional programming. They knew that pure functions are parallel by default, being free of pesky concurrency bugs and all. But, parallel functional programming remained slow and steady, resisting practical efficiency for decades. This post shows the way towards solving the performance problems of functional programming.

## Circuit Scaling for Analog Computing

Modern analog computers a host of analog behaviors that affect the fidelity of the mapped computation. How can we mitigate these analog behaviors in compilation?

## Bad Reasons to Reject Good Papers, and vice versa

Peer review is an essential aspect of academic research, giving a feedback loop that stimulates and rewards high-quality work – but as we all know, it doesn’t always function well. To help maintain a consensus of what constitutes good reviewing, this note spells out some bad and good reasons to reject and accept papers.