# Graph Partitioning with Acyclicity Constraints

arXiv: https://arxiv.org/abs/1704.00705 · HTML: https://mlsanity.com/paper/1704.00705/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2017.

## Built on by (most cited first, 2 of 2)

- [Model Parallelism on Distributed Infrastructure: A Literature Review from Theory to LLM Case-Studies](https://mlsanity.com/paper/2403.03699/) (2024)
- [Practical Performance Guarantees for Pipelined DNN Inference](https://mlsanity.com/paper/2311.03703/) (2023)

## Abstract (arXiv)

Graphs are widely used to model execution dependencies in applications. In particular, the NP-complete problem of partitioning a graph under constraints receives enormous attention by researchers because of its applicability in multiprocessor scheduling. We identified the additional constraint of acyclic dependencies between blocks when mapping computer vision and imaging applications to a heterogeneous embedded multiprocessor. Existing algorithms and heuristics do not address this requirement and deliver results that are not applicable for our use-case. In this work, we show that this more constrained version of the graph partitioning problem is NP-complete and present heuristics that achieve a close approximation of the optimal solution found by an exhaustive search for small problem instances and much better scalability for larger instances. In addition, we can show a positive impact on the schedule of a real imaging application that improves communication volume and execution time.