7 citations · 8 across the 9 of their papers we have counts for
11 papers
Every Microsecond Matters: Achieving Near Speed-of-Light Latency in GPU Collectives
Siyuan Shen, Anton Korzh, John Bachan +10
GPU collective communication is typically optimized for bandwidth, yet many emerging workloads are increasingly limited by latency. Long-context decode-heavy large language model (…
Demystifying NVSHMEM: A System-Level Analysis on Symmetric Memory and Device-Initiated Operations in GPU Communication
Yijun Ma, Siyuan Shen, Tiancheng Chen +6
NVSHMEM is NVIDIA's OpenSHMEM-based PGAS communication library for GPU clusters, enabling GPU-initiated, one-sided communication through symmetric memory. Despite its growing adopt…
Uno: A One-Stop Solution for Inter- and Intra-Datacenter Congestion Control and Reliable Connectivity
Tommaso Bonato, Sepehr Abdous, Abdul Kabbani +11
Cloud computing and AI workloads are driving unprecedented demand for efficient communication within and across datacenters. However, the coexistence of intra- and inter-datacenter…
EBCC v0.2: Error bounded compression for weather and climate applications
Langwen Huang, Luigi Fusco, Florian Scheidl +5
As the resolution of weather and climate simulations increases, the amount of data produced is growing rapidly from hundreds of terabytes to tens of petabytes. These data volumes l…
Zeppelin: Balancing Variable-length Workloads in Data Parallel Large Model Training
Chang Chen, Tiancheng Chen, Jiangfei Duan +7
Training large language models (LLMs) with increasingly long and varying sequence lengths introduces severe load imbalance challenges in large-scale data-parallel training. Recent…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
Ultra Ethernet's Design Principles and Architectural Innovations
Torsten Hoefler, Karen Schramm, Eric Spada +13
The recently released Ultra Ethernet (UE) 1.0 specification defines a transformative High-Performance Ethernet standard for future Artificial Intelligence (AI) and High-Performance…
RailX: A Flexible, Scalable, and Low-Cost Network Architecture for Hyper-Scale LLM Training Systems
Yinxiao Feng, Tiancheng Chen, Yuchen Wei +5
Increasingly large AI workloads are calling for hyper-scale infrastructure; however, traditional interconnection network architecture is neither scalable nor cost-effective enough.…
Evolving HPC services to enable ML workloads on HPE Cray EX
Stefano Schuppli, Fawzi Mohamed, Henrique Mendonça +10
The Alps Research Infrastructure leverages GH200 technology at scale, featuring 10,752 GPUs. Accessing Alps provides a significant computational advantage for researchers in Artifi…
Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms
Zhiyi Hu, Siyuan Shen, Tommaso Bonato +6
The NVIDIA Collective Communication Library (NCCL) is a critical software layer enabling high-performance collectives on large-scale GPU clusters. Despite being open source with a…
CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
Tiancheng Chen, Ales Kubicek, Langwen Huang +1
Training large language models (LLMs) now requires resources that exceed a single datacenter, making cross-datacenter strategies increasingly crucial. We present CrossPipe, a frame…