5 papers
CCL-D: A High-Precision Diagnostic System for Slow and Hang Anomalies in Large-Scale Model Training
Yida Gu, Fakang Wang, Jianhao Fu +17
As training scales grow, collective communication libraries (CCL) increasingly face anomalies arising from complex interactions among hardware, software, and environmental factors.…
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
Jinda Jia, Cong Xie, Hanlin Lu +8
Recent years have witnessed a clear trend towards language models with an ever-increasing number of parameters, as well as the growing training overhead and memory usage. Distribut…
FastCLIP: A Suite of Optimization Techniques to Accelerate CLIP Training with Limited Resources
Xiyuan Wei, Fanjiang Ye, Ori Yonay +4
Existing studies of training state-of-the-art Contrastive Language-Image Pretraining (CLIP) models on large-scale data involve hundreds of or even thousands of GPUs due to the requ…
Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression
Hao Feng, Boyuan Zhang, Fanjiang Ye +9
DLRM is a state-of-the-art recommendation system model that has gained widespread adoption across various industry applications. The large size of DLRM models, however, necessitate…
A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization
Daoce Wang, Pascal Grosset, Jesus Pulido +8
Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is…