8 citations · 16 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
ATLAHS: An Application-centric Network Simulator Toolchain for AI, HPC, and Distributed Storage
Siyuan Shen, Tommaso Bonato, Zhiyi Hu +3
Network simulators play a crucial role in evaluating the performance of large-scale systems. However, existing simulators rely heavily on synthetic microbenchmarks or narrowly focu…
Sailor: Automating Distributed Training over Dynamic, Heterogeneous, and Geo-distributed Clusters
Foteini Strati, Zhendong Zhang, George Manos +7
The high GPU demand of ML training makes it hard to allocate large homogeneous clusters of high-end GPUs in a single availability zone. Leveraging heterogeneous GPUs available with…