research
∙
05/23/2023
Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
Benchmarking and co-design are essential for driving optimizations and i...
share
research
∙
03/24/2023
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
As deep learning models and input data are scaling at an unprecedented r...
research
∙
11/30/2022
COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training
Modern Deep Learning (DL) models have grown to sizes requiring massive c...
research
∙
07/22/2022
Impact of RoCE Congestion Control Policies on Distributed Training of DNNs
RDMA over Converged Ethernet (RoCE) has gained significant attraction fo...
research
∙
10/09/2021
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models
The continuous growth in both size and training data for modern Deep Neu...
research
∙
09/24/2021
Exploring Multi-dimensional Hierarchical Network Topologies for Efficient Distributed Training of Trillion Parameter DL Models
Deep Neural Networks have gained significant attraction due to their wid...
research
∙
08/19/2020
Restructuring, Pruning, and Adjustment of Deep Models for Parallel Distributed Inference
Using multiple nodes and parallel computing algorithms has become a prin...
research
∙
06/30/2020
Efficient Communication Acceleration for Next-Gen Scale-up Deep Learning Training Platforms
Deep Learning (DL) training platforms are built by interconnecting multi...
research
∙
06/30/2020