[physicsguy]

> As a HPC developer, it breaks my heart how worse academic software performance is compared to vendor libraries (from Intel or Nvidia). We need to start aiming much higher.

They're optimising for different things really.

Intel/Nvidia have the resources to (a) optimise across a wide range of hardware in their libraries (b) often use less well documented things (c) don't have to make their source code publicly accessible.

Take MKL for example - it's a great library, but implementing dynamic dispatch for all the different processor types is why it gets such good performance across x86-64 machines, it's not running the same code on each processor. No academic team can really compete with that.

[shihab]

I'm not asking an academic program first published 8 year ago (e3nn) to beat actively developed CuEquivariance library. An academic proposing new algorithms doesn't need to worry too much about performance. But any new work which focuses on performance, that includes this blog and a huge number of academic papers published every year, should absolutely use latest vendor libraries as baseline.