Reading Between the MLPerf Lines

Every important benchmark needs to start somewhere.

The first round of MLperf results are in and while they might not deliver on what we would have expected in terms of processor diversity and a complete view into scalability and performance, they do shed light on some developments that go beyond sheer hardware when it comes to deep learning training.


Applying Machine Learning At The Front End Of HPC

IBM and the other vendors who are bidding on the CORAL2 systems for the US Department of Energy can’t talk about those bids, which are in flight, and Big Blue and its partners in building the “Summit” supercomputer at Oak Ridge National Laboratory and “Sierra” at Lawrence Livermore National Laboratory – that would be Nvidia for GPUs and Mellanox Technologies for InfiniBand interconnect – are all about publicly focusing on the present, since these two machines are at the top of the flops charts now.


Turning The CPU-GPU Hybrid System On Its Head

Sales of various kinds of high performance computing – not just technical simulation and modeling applications, but also cryptocurrency mining, massively multiplayer gaming, video rendering, visualization, machine learning, and data analytics – run on little boom-bust cycles that make it difficult for all suppliers to this market to make projections when they look ahead.