Tapasweni Pathak

Principal Product Manager at Microsoft Azure Core Engineering

email: tapaswenipathak@gmail.com

Education: B.Tech in Computer Science, Indira Gandhi Delhi Technical University for Women, 2014

Incomplete project: Improving performance of C++ modules in Clang
The C++ modules technology aims to provide a scalable compilation model for the C++ language. The C++ Modules technology in Clang provides an io-efficient, on-disk representation capable to reduce build times and peak memory usage. The internal compiler state such as the abstract syntax tree (AST) is stored on disk and lazily loaded on demand. C++ Modules improve the memory footprint for interpreted C++ through the Cling C++ interpreter developed by CERN and the compiler research group at Princeton. The current implementation is pretty good at making most operations on demand. However in a few cases, we eagerly load pieces of the AST, for example at module import time and upon selecting a suitable template specialization. When selecting the template specialization we load all template specializations from the module files just to find out they are not suitable. There is a patch that partially solves this issue by introducing a template argument hash and use it to look up the candidates without deserializing them. However, the data structure it uses to store the hashes leads to quadratic search which is inefficient when the number of modules becomes sufficiently large.

Project Proposal: URL

Mentors: Vassil Vassilev


Improving performance of C++ modules in Clang-Project - Roadmap, Slides, Team Meeting, 25 May 2022