Code is increasingly becoming a core data modality of modern machine learning research
impacting not only the way we write code with conversational agents like OpenAI's ChatGPT …

A predictive compiler uses properties of a program to decide how to optimize it. The compiler
is trained on a collection of programs to derive a model which determines its actions in face …

The optimization space of a compiler is the set of every possible sequence of optimizations
that said compiler can use. The exploration of the optimization space of any mainstream …

The growing popularity of machine learning frameworks and algorithms has greatly
contributed to the design and exploration of good code optimization sequences. Yet, in spite …

Type inference is a feature that is common to a variety of programming languages. While, in
the past, it has been prominently present in functional ones (eg, ML and Haskell), today …

Effective code optimization in compilers plays a central role in computer and software
engineering. While compilers can be made to automatically search the optimization space …

Typical compilers provide users with default optimization levels: sequences of code-
transformation passes that can be tuned to either generate faster or smaller executables …

Tuning application performance on modern computing infrastructures involves choices in a
vast design space as modern computing architectures can have several complex structures …

Mainstream compilers infer code properties from data structures, such as trees and graphs.
The latter is useful to represent the control flow and the data dependencies in a code. In …

Dynamic compilers perform compilation and generation of target code during runtime,
implying that the compilation time is added into the program runtime. Thus, to build a high …