[2608.00358] HCCL: Collective Communication for Meta Training and Inference Accelerators

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Computer Science > Networking and Internet Architecture

arXiv:2608.00358 (cs)

[Submitted on 1 Aug 2026]

Title:HCCL: Collective Communication for Meta Training and Inference Accelerators

Authors:Wesley Bland, Tiago Antunes, Lars Paul Huse, Chidambaram Muthu, Adel Abouchaev, Rabib Alam, Abdullah Alperen, Alexey Andronov, Jose Anto Akkara, Vineet Badhwar, Pavan Balaji, Daniel Berkovitch, Bartosz Bogdanski, Shmeelok Chakraborty, Sungjun Cho, John Choi, James Custer, Rodrigo De Castro, Nguyen Dinh Pham, Matthew Edwards, Kristian Evensen, Evan Ezell, Alex Finestead, Seth Goldstein, Prankur Gupta, Ranwei Hu, Adam Incera, Anand Jayaraman, Prashanth Kannan, Soumil Kanwal, Martin Karp, Sameer Kumar, Naina Kuruballi Mahesh, Wei Lin Guay, Cristian Lumezanu, Cory Modlin, Dag Georg Moxnes, Hoang Nam Nguyen, Ashay Narsale, Jaden Padua, Kirtesh Patil, Minh Pham, Amin Qassoud, Ashwin Ramachandran, David Ramon Prados, Pallavi Shurpali, Gregory R. Steinbrecher, John Sundharam, Vangelis Tasoulas, Fuhou Tian, Srinivas Vaidyanathan, Vimal Vasudevan, Nicolaas Viljoen, Daniel Winkelman, Yijing Zeng, Zhaoqi Zhu, Stig Arne Olsen, Gilad Goldfarb, Rajiv Krishnamurthy, Rajeev Nair, Jonas Olsson, Joseph Provine, Sreeram Ravinoothala, Shivayogi Ugaji, Hongyi Zeng, Nairan Zhang<br>View a PDF of the paper titled HCCL: Collective Communication for Meta Training and Inference Accelerators, by Wesley Bland and 65 other authors

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Abstract:We present HCCL, a collective communication library co-designed with Meta's MTIA 300 accelerator, the first Meta chip to integrate backend networking directly on chip package. MTIA 300 includes dedicated message engines (MEs) with near-memory compute (NMC) that fully offload collective execution from the compute grid, enabling large overlap between computation and communication. HCCL uses a compiled communication model in which the host generates a complete description of each collective including dependencies. We describe the control and data path architecture, topology-aware algorithm selection across MTIA 300's asymmetric scale-up and scale-out network, and optimizations for both training and inference workloads. For training, HCCL achieves up to 940 GB/s on intra-rack collectives while introducing less than 0.5% degradation to concurrent compute throughput. For inference, we leverage one-sided communication primitives that bypass the scheduling path to minimize collective latency and describe collective designs that improve compute-communication pipelining for latency-sensitive workloads.

Comments:<br>12 pages, 17 figures, to be published in the proceedings of "SC '26: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis"

Subjects:

Networking and Internet Architecture (cs.NI); Distributed, Parallel, and Cluster Computing (cs.DC)

Cite as:<br>arXiv:2608.00358 [cs.NI]

(or<br>arXiv:2608.00358v1 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2608.00358

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Wesley Bland [view email]<br>[v1]<br>Sat, 1 Aug 2026 00:14:41 UTC (864 KB)

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