# Accelerating Irregular Computations with Hardware Transactional Memory and Active Messages

arXiv: https://arxiv.org/abs/2010.09135 · HTML: https://mlsanity.com/paper/2010.09135/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Motif Prediction with Graph Neural Networks](https://mlsanity.com/paper/2106.00761/) (2021)
- [Neural Graph Databases](https://mlsanity.com/paper/2209.09732/) (2022)
- [GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra](https://mlsanity.com/paper/2103.03653/) (2021)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [GraphSeek: Next-Generation Graph Analytics with LLMs](https://mlsanity.com/paper/2602.11052/) (2026)
- [Higher-Order Graph Databases](https://mlsanity.com/paper/2506.19661/) (2025)

## Abstract (arXiv)

We propose Atomic Active Messages (AAM), a mechanism that accelerates irregular graph computations on both shared- and distributed-memory machines. The key idea behind AAM is that hardware transactional memory (HTM) can be used for simple and efficient processing of irregular structures in highly parallel environments. We illustrate techniques such as coarsening and coalescing that enable hardware transactions to considerably accelerate graph processing.We conduct a detailed performance analysis of AAM on Intel Haswell and IBM Blue Gene/Q and we illustrate various performance tradeoffs between different HTM parameters that impact the efficiency of graph processing. AAM can be used to implement abstractions offered by existing programming models and to improve the performance of irregular graph processing codes such as Graph500 or Galois.