[Coin] Call For Paper: SIGCOMM'24 workshop on Networks for AI Computing

[Haoyu Song <[e-mail]>]

Please submit your research papers and attend the workshop. The workshop is right before the SIGCOMM main conference.
We would have two keynote speeches from both industry and  academia to cover the research and application in this hot area.
See you in Sydney this summer!

The official website (TBA) https://conferences.sigcomm.org/sigcomm/2024/workshop/naic/

SIGCOMM NAIC’24 Workshop

Generative AI is transforming many aspects of modern society with content
ranging from text and image to videos. The Large Language Models (LLMs)
and other Artificial Intelligence (AI)/Machine Learning (ML) that enable these
generative AI capabilities are placing an unprecedented amount of pressure on
modern data centers with anecdotal evidence suggesting that the largest model
can take months to train. To support these models, modern distributed training
cluster contains tens of thousands of GPUs/TPUs, with many expecting the
scale to further increase significantly.

More fundamentally, training these large models introduce network communication
patterns that require sophisticated and novel topology, routing, and
synchronization. As the adoption and use of such models grows, the data generated
and the data required to train and make inferences will place on emphasis
on the design of novel network primitives. The scale, workload, and performance
requirements require us to reconsider every layer of the network stacks and scrutinize
the solution from a holistic perspective. The recent industry initiative,
Ultra Ethernet Consortium (UEC), is actively working on Ethernet-based network
optimizations for AI and HPC workloads. The Open Compute Project (OCP)
is geared more toward infrastructure support for AI computing. The standard
organizations (e.g., IETF) are also seeking opportunities in networking for AI computing.
We believe the networking research community should take a bolder position and
bring cutting-edge innovations in this front as well.

The workshop aims to bring together researchers and experts from academia
and industry to share the latest research, trends, and challenges in cloud and
data center networks for AI computing. We expect it to enrich our understanding
of the AI workloads, communication patterns, and their impacts on networks,
and help the community to identify future research directions. We encourage
lively debate on issues like convergence vs disaggregation, front-end vs back-end,
smart edges vs programmable core, and the need for new interconnection,
new topology, new transport, and new routing algorithms and protocols.

Topics of interest include, but are not limited to:

• Technologies for RDMA and Ethernet efficiency, performance, security, and extensibility
• Load balancing for distributed learning
• Lossless and loss-tolerate network design
• Host and network integration and coordination
• New transport protocol and congestion control for AI training
• Storage network optimization
• New network architecture and topologies for AI and HPC
• Non-minimal adaptive routing
• SmartNIC/DPU offloading
• AI backend networking system design and optimization
• Programmable networks for AI workload
• In network computing techniques and protocols for distributed training and MPI support
• Application aware networking for AI training and inference
• Collective communication optimization
• Networking for cross-DC learning
• Cross-layer optimization
• Convergence of computing, storage, and networking
• IPv6/SRv6-based hyperscale AI Datacenter
• Automate and intelligent AI DCN OAM
• LLM for DCN OAM
• Fault prediction, detection, and root cause analysis
• New measurement and telemetry metrics and methods
• Green data center for energy efficiency
• Traffic characterization for AI workload
• Network simulation and benchmarking for AI workload Submit Instructions

We invite researchers and practitioners to submit original research papers, including
position papers on disruptive ideas, and early-stage work with a potential
for full papers in the future.

Submissions must be original, unpublished work, and not under consideration at
another conference or journal. Submitted papers must be at most six (6) pages long,
excluding references and appendices, in two-column 10pt ACM format. Papers must
include author names and affiliations for single-blind peer reviewing by the PC.
Authors of accepted submissions are expected to present and discuss their work at the workshop.

Important Dates
• Submission Deadline: June 2, 23:59, AoE, 2024
• Acceptance Notification: June 10, 2024
• Camera-ready Deadline: June 14, 2024
• Workshop day: August 4, 2024

Organizers

TPC Co-chairs
• Arpit Gupta (UCSB)
• Xin Jin (Peking University)
• Vijay Sivaraman (University Of New South Wales)

TPC
• TBA

Steering Committee
• Theophilus A. Benson (CMU)
• Torsten Hoefler (ETH Zurich)
• TV Lakshman (Nokia Bell Labs)
• Haoyu Song (Futurewei)
• Ying Zhang (Meta)
• Zhi-li Zhang (U of Minnesota)