# ACCL+: an FPGA-Based Collective Engine for Distributed Applications

arXiv: https://arxiv.org/abs/2312.11742 · HTML: https://mlsanity.com/paper/2312.11742/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 106 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [The Architectural Implications of Facebook's DNN-based Personalized Recommendation](https://mlsanity.com/paper/1906.03109/) (2019)
- [DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference](https://mlsanity.com/paper/2001.02772/) (2020)
- [TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning](https://mlsanity.com/paper/1908.03072/) (2019)
- [Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations](https://mlsanity.com/paper/2005.05968/) (2020)
- [Cross-Stack Workload Characterization of Deep Recommendation Systems](https://mlsanity.com/paper/2010.05037/) (2020)
- [Farview: Disaggregated Memory with Operator Off-loading for Database Engines](https://mlsanity.com/paper/2106.07102/) (2021)
- [Clio: A Hardware-Software Co-Designed Disaggregated Memory System](https://mlsanity.com/paper/2108.03492/) (2021)
- [A Survey of System Architectures and Techniques for FPGA Virtualization](https://mlsanity.com/paper/2011.09073/) (2020)
- [Streaming Message Interface: High-Performance Distributed Memory Programming on Reconfigurable Hardware](https://mlsanity.com/paper/1909.03231/) (2019)

## Built on by (most cited first, 3 of 3)

- [hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware](https://mlsanity.com/paper/2512.01463/) (2025)
- [Efficient Tabular Data Preprocessing of ML Pipelines](https://mlsanity.com/paper/2409.14912/) (2024)
- [Accelerating Recommender Model ETL with a Streaming FPGA-GPU Dataflow](https://mlsanity.com/paper/2501.12032/) (2025)

## Abstract (arXiv)

FPGAs are increasingly prevalent in cloud deployments, serving as Smart NICs or network-attached accelerators. Despite their potential, developing distributed FPGA-accelerated applications remains cumbersome due to the lack of appropriate infrastructure and communication abstractions. To facilitate the development of distributed applications with FPGAs, in this paper we propose ACCL+, an open-source versatile FPGA-based collective communication library. Portable across different platforms and supporting UDP, TCP, as well as RDMA, ACCL+ empowers FPGA applications to initiate direct FPGA-to-FPGA collective communication. Additionally, it can serve as a collective offload engine for CPU applications, freeing the CPU from networking tasks. It is user-extensible, allowing new collectives to be implemented and deployed without having to re-synthesize the FPGA circuit. We evaluated ACCL+ on an FPGA cluster with 100 Gb/s networking, comparing its performance against software MPI over RDMA. The results demonstrate ACCL+'s significant advantages for FPGA-based distributed applications and highly competitive performance for CPU applications. We showcase ACCL+'s dual role with two use cases: seamlessly integrating as a collective offload engine to distribute CPU-based vector-matrix multiplication, and serving as a crucial and efficient component in designing fully FPGA-based distributed deep-learning recommendation inference.