PLSE Blog

PyTorch is a popular open-source tensor library for machine learning (ML) and scientific computing in Python. It’s especially popular among the research community because of its active open-source community and its flexibility for experimenting with new ML architectures. For all of its benefits, it has a clear downfall compared to other ML frameworks like TensorFlow. It’s slow! Recent work from the PyTorch team at Meta attempts to bridge the flexibility-performance gap with torch.compile, a feature that speeds up PyTorch code with compilation. In this blog post, I’ll discuss the motivation for torch.compile and its implementation as a Python-level just-in-time (JIT) compiler called TorchDynamo.

This is the first part of a two-part blog post. In this post, I’ll set up the problem we’re trying to solve, and a future post will go into more detail about how our solution works.

As programming languages researchers, we’re entitled to a certain level of mathematical rigor behind the languages we write and analyze. Programming languages have semantics, which are definitions of what statements in the language mean. We can use those semantics to do all sorts of useful things, like error checking, compiling for efficiency, code transformation, and so on.

This post is also available as a GitHub repo.

As a follow up to Alexandra’s recent post about speculative execution and how your mental model of a computer is probably wrong, I thought I’d follow up with another example of how our programming abstraction has drifted from the underlying hardware implementation—this time with respect to when shared memory operations are visible to another program. These specifications, called memory consistency models (also just memory models or MCMs), are unfortunately tricky to understand and are famously ambiguously specified.

As part of my responsibilities as an industrial PhD student with PLSE and Intel, I develop and maintain, alongside my colleague and PLSE alumni Bill Zorn, a reference implementation of floating-point arithmetic. Hardware designers use this reference model to formally specify the behavior of floating point in hardware designs and verify the correctness of their implementations of these designs. Our library must not only have reasonable performance while simulating these designs and be consumable by formal verification tools for equivalence checking or property verification; but it must also correctly answer the often difficult question about floating point: “but what do it do?” With the prevalence of machine learning and its demand for ever smaller number formats for increased performance, we explored the interesting and often ridiculous world of small floating-point formats.

Speculative execution, aka speculation, is a form of computation that increasingly underlies every program our computers run. If your mental model of a CPU is a device that runs every instruction given to it, in the order given, with all prerequisite inputs gathered beforehand—wipe that model from your head, because we live in the world of speculation and Spectre.

When choosing a programming language to use for a project—or when implementing one—there are many considerations to keep in mind. One of those is whether or not the programming language uses any sort of garbage collection. Today, I’m going to talk about one method of garbage collection that we can implement ourselves fairly easily: two-space copying garbage collection.

In this post, I will relate some of the key findings and lessons learned in our 2024 ISSTA publication Equivalent Mutants In The Wild: Identifying and Efficiently Suppressing Equivalent Mutants for Java Programs (that’s a mouthful so I’ll just call it EMIW). In EMIW, my co-authors and I performed an extensive manual analysis of mutants generated for Java programs to gain insight into the types of equivalent mutants generated for real-world code. We found that a number of equivalent mutants follow simple patterns. We used these patterns to write simple, efficient static analyses, which we call suppression rules, to suppress equivalent mutants before the are ever generated. We added the suppression rules to the Major mutation framework and will refer to Major with suppression rules as EMS.

Hi friends! I’m back to discuss more accessibility improvements we’ve made to the PLSE website: how we found more issues, how we fixed them, and how we’ll (hopefully) not make these same mistakes in the future! In particular, this post touches on running automated browser tests across our entire website to flag specific types of accessibility issues. Along the way, we’ll learn about some specific web accessibility standards and how to implement them not-poorly!