Search Results for author:
Found 4 papers, 1 papers with code
Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts
The inter-device communication of a MoE layer can occupy 47% time of the entire model execution with popular models and frameworks.
The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving
We survey the large language model (LLM) serving area to understand the intricate dynamics between cost-efficiency and accuracy, which is magnified by the growing need for longer contextual understanding when deploying models at a massive scale.
A Codesign of Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters
The exponentially enlarged scheduling space and ever-changing optimal parallelism plan from adaptive parallelism together result in the contradiction between low-overhead and accurate performance data acquisition for efficient cluster scheduling.
AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs
Graph neural networks (GNNs) are powerful tools for exploring and learning from graph structures and features.
