5th Year Master of Science in Computer Science Thesis Presentation
— 11:30pm
Location:
In Person
-
Gates Hillman 7101
Speaker:
ETHAN CHU
,
Master's Student, Computer Science Department, Carnegie Mellon University
https://www.linkedin.com/in/ethan-chu-19538744
The Theory and Implementation of Resource Aware ML 2
Resource Aware ML (RaML) is a tool that can compute the resource use of programs in the ML family (SML, OCaml, etc) through a type-based technique known as automatic amortized resource analysis (AARA). Existing implementations of RaML have numerous shortcomings, such as lacking support for regular recursive types and being difficult to maintain.
This thesis presents the theory and implementation of a new and improved version of RaML, Resource Aware ML 2.Using previous work by Grosen et al, RaML 2 is able to support regular recursive types, non-monotone resource analysis, and more. Furthermore, it augments Grosen's work, extending these new features to univariate as opposed to multivariate AARA.
Additionally, this thesis presents supporting features for a full RaML implementation. This includes a new Intermediate Representation (IR) that the resource analysis operates on; this IR is implemented efficiently with an infrastructure known as Abstract Binding Trees. Other contributions include an SML frontend implementation that can translate arbitrary SML code to the RaML IR, as well as a linear programming solver backend.
Thesis Committee:
Jan Hoffmann (Chair)
Frank Pfenning