# Ridgeline: A 2D Roofline Model for Distributed Systems

arXiv: https://arxiv.org/abs/2209.01368 · HTML: https://mlsanity.com/paper/2209.01368/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2022.

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

- [Scaling Intelligence: Designing Data Centers for Next-Gen Language Models](https://mlsanity.com/paper/2506.15006/) (2025)
- [COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training](https://mlsanity.com/paper/2211.16648/) (2022)

## Abstract (arXiv)

In this short paper, we introduce the Ridgeline model, an extension of the Roofline model [4] for distributed systems. The Roofline model targets shared memory systems, bounding the performance of a kernel based on its operational intensity, and the peak compute throughput and memory bandwidth of the execution system. In a distributed setting, with multiple communicating compute entities, the network must be taken into account to model the system behavior accurately. The Ridgeline aggregates information on compute, memory, and network limits in one 2D plot to show, in an intuitive way, which of the resources is the expected bottleneck. We show the applicability of the Ridgeline in a case study based on a data-parallel Multi-Layer Perceptron (MLP) instance.