(See this blog post for a short introduction to synthesis, or this paper for a long one.) In this piece I want to discuss an aspect of program synthesis that sounds like it should be easy, but isn’t: synthesizing constant values. For example, consider trying to synthesize an optimized x86-64 implementation of this code: The […]
Category Archives: Computer Science
Learning When Values are Changed by Implicit Integer Casts
C and C++ perform implicit casts when, for example, you pass an integer-typed variable to a function that expects a different type. When the target type is wider, there’s no problem, but when the target type is narrower or when it is the same size and the other signedness, integer values may silently change when […]
What’s the difference between an integer and a pointer?
(This piece is an alternate introduction and advertisement for a soon-to-be-published research paper.) In an assembly language we typically don’t have to worry very much about the distinction between pointers and integers. Some instructions happen to generate addresses whereas others behave arithmetically, but underneath there’s a single data type: bitvectors. At the opposite end of […]
Future Directions for Optimizing Compilers
I wanted to write a manifesto-ish sort of piece about what compilers are supposed to look like in the future. Nuno was the obvious coauthor since I’ve talked to him about this topic so much that I’m overall not really sure which parts started out as his ideas and which were mine. The article didn’t […]
How LLVM Optimizes a Function
An optimizing, ahead-of-time compiler is usually structured as: A frontend that converts source code into an intermediate representation (IR). A target-independent optimization pipeline: a sequence of passes that successively rewrite the IR to eliminate inefficiencies and forms that cannot be readily translated into machine code. Sometimes called the “middle end.” A target-dependent backend that generates […]
How Clang Compiles a Function
I’ve been planning on writing a post about how LLVM optimizes a function, but it seems necessary to first write about how Clang translates C or C++ into LLVM. This is going to be fairly high-level: rather than looking at Clang’s internals, I’m going to focus on how Clang’s output relates to its input we’re […]
Software Engineering Takeaways
I had a great time this spring teaching a software engineering course for a new professional masters degree program created by my department. Since I didn’t use slides or hand out lecture notes, some students were asking if maybe I could write up a summary of what I wanted them to learn in the course. […]
Paths to External Engagement in Computer Science Research
The other day I wrote a post imploring academic computer scientists to at least occasionally break out of their research bubbles and engage with real engineering problems where their research matters. That post was, admittedly, a bit facile about how one might make this engagement happen. This piece suggests some ways. I don’t claim any […]
Closing the Loop: The Importance of External Engagement in Computer Science Research
Computer scientists tend to work by separating the essence of a problem from its environment, solving it in an abstract form, and then figuring out how to make the abstract solution work in the real world. For example, there is an enormous body of work on solving searching and sorting problems and in general it […]
The Basic Toolbox
This post is aimed at computer science students. In the software engineering course I’m teaching this spring, I often find myself saying things like “you need to know a scripting language” or “everyone should be able to run a code coverage tool.” Finally, the other day, a student stopped me and asked for the whole […]