Performance at Scale: Graphcore's Latest MLPerf Training Results

Graphcore's latest submission to MLPerf demonstrates two things very clearly – our IPU systems are getting larger and more efficient, and our software maturity means they are also getting faster and easier to use. Software optimisation continues to deliver significant performance gains, with our IPU-POD16 now outperforming Nvidia's DGX A100 for computer vision model, ResNet-50. Training ResNet-50 takes 28.3 minutes on the IPU-POD16, compared to 29.1 minutes for DGX A100 – a performance improvement of 24% since our first submission through software alone. It is a significant milestone, given that ResNet-50 has traditionally been a showpiece model for GPUs. Our software-driven performance gain for ResNet-50 on the IPU-POD64 was even greater at 41%.