Objective
The energy consumption of data centers and ICT devices grows at an alarming rate and will be responsible for up to 20% of the global energy consumption by 2030. To sustain the ongoing digital transformation, we must find ways to run software dramatically more efficiently. A promising direction is incremental computing. Incremental computations react to input changes rather than recomputing their result from scratch, which is known to deliver asymptotic speedups in theory and order-of-magnitude speedups in practice.
However, current approaches to incrementality have limited applicability: They either require expert knowledge, or only support specialized domains (e.g. database queries), or only yield modest speedups. The goal of this project is to develop a methodology for automatically incrementalizing computations and significantly improving their time and energy efficiency.
The AutoInc project achieves this ambitious goal by establishing a novel foundation for incremental computing in three complementary parts:
- A library of incremental operators for atomic and other essential data types,
- A low-level intermediate representation (IR) for describing compositional incremental computations,
- An optimizing compiler that translates existing code to the low-level incremental IR.
Research Results
Project Members
Project publications
This project is funded from the European Research Council (ERC) under the European Union's Horizon 2023 (Grant Agreement ID 101125325).
Last overhaul of this page: May 2024