Showing 1–5 of 5 results for author: Kannan, P

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 , et al. (41 additional authors not shown)

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 compil… ▽ More 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. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Comments: 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"

The More the Merrier: Running Multiple Neuromorphic Components On-Chip for Robotic Control

Authors: Evan Eames, Priyadarshini Kannan, Ronan Sangouard, Philipp Plank, Elvin Hajizada, Gintautas Palinauskas, Lana Amaya, Michael Neumeier, Sai Thejeshwar Sharma, Marcella Toth, Prottush Sarkar, Axel von Arnim

Abstract: It has long been realized that neuromorphic hardware offers benefits for the domain of robotics such as low energy, low latency, as well as unique methods of learning. In aiming for more complex tasks, especially those incorporating multimodal data, one hurdle continuing to prevent their realization is an inability to orchestrate multiple networks on neuromorphic hardware without resorting to off-… ▽ More It has long been realized that neuromorphic hardware offers benefits for the domain of robotics such as low energy, low latency, as well as unique methods of learning. In aiming for more complex tasks, especially those incorporating multimodal data, one hurdle continuing to prevent their realization is an inability to orchestrate multiple networks on neuromorphic hardware without resorting to off-chip process management logic. To address this, we show a first example of a pipeline for vision-based robot control in which numerous complex networks can be run entirely on hardware via the use of a spiking neural state machine for process orchestration. The pipeline is validated on the Intel Loihi 2 research chip. We show that all components can run concurrently on-chip in the milli Watt regime at latencies competitive with the state-of-the-art. An equivalent network on simulated hardware is shown to accomplish robotic arm plug insertion in simulation, and the core elements of the pipeline are additionally tested on a real robotic arm. △ Less

Submitted 14 February, 2026; originally announced February 2026.

Comments: IOP Journal of Neuromorphic Computing and Engineering, preliminary acceptance

Collective Communication for 100k+ GPUs

Authors: Min Si, Pavan Balaji, Yongzhou Chen, Ching-Hsiang Chu, Adi Gangidi, Saif Hasan, Subodh Iyengar, Dan Johnson, Bingzhe Liu, Regina Ren, Deep Shah, Ashmitha Jeevaraj Shetty, Greg Steinbrecher, Yulun Wang, Bruce Wu, Xinfeng Xie, Jingyi Yang, Mingran Yang, Kenny Yu, Minlan Yu, Cen Zhao, Wes Bland, Denis Boyda, Suman Gumudavelli, Prashanth Kannan , et al. (14 additional authors not shown)

Abstract: The increasing scale of large language models (LLMs) necessitates highly efficient collective communication frameworks, particularly as training workloads extend to hundreds of thousands of GPUs. Traditional communication methods face significant throughput and latency limitations at this scale, hindering both the development and deployment of state-of-the-art models. This paper presents the NCCLX… ▽ More The increasing scale of large language models (LLMs) necessitates highly efficient collective communication frameworks, particularly as training workloads extend to hundreds of thousands of GPUs. Traditional communication methods face significant throughput and latency limitations at this scale, hindering both the development and deployment of state-of-the-art models. This paper presents the NCCLX collective communication framework, developed at Meta, engineered to optimize performance across the full LLM lifecycle, from the synchronous demands of large-scale training to the low-latency requirements of inference. The framework is designed to support complex workloads on clusters exceeding 100,000 GPUs, ensuring reliable, high-throughput, and low-latency data exchange. Empirical evaluation on the Llama4 model demonstrates substantial improvements in communication efficiency. This research contributes a robust solution for enabling the next generation of LLMs to operate at unprecedented scales. △ Less

Submitted 9 January, 2026; v1 submitted 22 October, 2025; originally announced October 2025.

ACM Class: C.2.4; I.2

An Extensible Software Transport Layer for GPU Networking

Authors: Yang Zhou, Zhongjie Chen, Ziming Mao, ChonLam Lao, Shuo Yang, Pravein Govindan Kannan, Jiaqi Gao, Yilong Zhao, Yongji Wu, Kaichao You, Fengyuan Ren, Zhiying Xu, Costin Raiciu, Ion Stoica

Abstract: Fast-evolving machine learning (ML) workloads have increasing requirements for networking. However, host network transport on RDMA NICs is hard to evolve, causing problems for ML workloads. For example, single-path RDMA traffic is prone to flow collisions that severely degrade collective communication performance. We present UCCL, an extensible software transport layer to evolve GPU networking. UC… ▽ More Fast-evolving machine learning (ML) workloads have increasing requirements for networking. However, host network transport on RDMA NICs is hard to evolve, causing problems for ML workloads. For example, single-path RDMA traffic is prone to flow collisions that severely degrade collective communication performance. We present UCCL, an extensible software transport layer to evolve GPU networking. UCCL decouples the data path and control path of existing RDMA NICs and efficiently runs the control-path transport on host CPUs. This software extensibility brings in transport innovations that cannot be achieved in hardware for ML workloads, e.g., a multipath transport to resolve flow collisions. ML collectives atop UCCL achieve up to 4.5x higher performance compared to existing RDMA NICs. △ Less

Submitted 4 August, 2025; v1 submitted 24 April, 2025; originally announced April 2025.

Benchmarking tunnel and encryption methodologies in cloud environments

Authors: Pravein Govindan Kannan, Brent Salisbury, Palanivel Kodeswaran, Sayandeep Sen

Abstract: The recent past has seen the adoption of multi-cloud deployments by enterprises due to availability, features, and regulatory requirements. A typical deployment involves parts of an application/workloads running inside a private cloud with the other parts spread across multiple on-prem/public clouds. Typical cluster-to-cluster networking in such deployments involve the establishment of site-to-sit… ▽ More The recent past has seen the adoption of multi-cloud deployments by enterprises due to availability, features, and regulatory requirements. A typical deployment involves parts of an application/workloads running inside a private cloud with the other parts spread across multiple on-prem/public clouds. Typical cluster-to-cluster networking in such deployments involve the establishment of site-to-site encrypted tunnels to connect the workloads. In this report, we benchmark the performance of various tunneling and encryption technologies to provide directions on their use in multi-cloud deployments. Based on the various experiments conducted on three different testbeds, we present quantifiable data which can be leveraged by operators and cloud providers tasked with design and development decisions of multi-cloud network connectivity and orchestration. △ Less

Submitted 4 March, 2022; originally announced March 2022.