 Our proposed designs in HyPar-Flow offer up to 3.1x speedup over sequential training for ResNet-110 and up to 1.6x speedup over Horovod-based data-parallel training for ResNet-1001; a model that has 1, 001 layers and 30 million parameters. We provide an in-depth performance characterization of the HyPar-Flow framework on multiple HPC systems with diverse CPU architectures including Intel Xeon(s)and AMD EPYC. HyPar-Flow provides 110x speed up on 128 nodes of the Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .
<coherence>  Our proposed designs in HyPar-Flow offer up to 3.1x speedup over sequential training for ResNet-110 and up to 1.6x speedup over Horovod-based data-parallel training for ResNet-1001; a model that has 1, 001 layers and 30 million parameters. We provide an in-depth performance characterization of the HyPar-Flow framework on multiple HPC systems with diverse CPU architectures including Intel Xeon(s)and AMD EPYC. HyPar-Flow provides 110x speed up on 128 nodes of the Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .
 Our proposed designs in HyPar-Flow offer up to 3.1x speedup over sequential training for ResNet-110 and up to 1.6x speedup over Horovod-based data-parallel training, 2) 110x speed up on 128 nodes of the Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .