This article is a non sequitur. Spends the first half taking about the rising usage of low precision work in ml and then talks about how CERN is still using high precision.
Seems like the missing bit is talking about how CERN could benefit from low precision? But it's certainly not in the article.
It mentions GPUs. GPUs generally only offer low-precision floating point arithmetic. GPUs can do many more 32-bit floating point operations per-second than CPUs.
There must be a huge demand for running numerical workloads on faster kit (GPUs), but the attendant loss in precision can make it very difficult to get good results in high-energy particle physics. The idea here might be to develop numerical methods that make up for low-precision arithmetic yet preserve the speed advantage of using GPUs. If so, TFA is very poor indeed.
The Titan and Tesla NVIDIA cards are still built with 64 bit/double precision in mind with "only" a 50% performance reduction compared to FP32 in most of those devices. It is the GeForce gaming cards and the ones being targeted now for ML that are gimping FP64, where it is 1/4th or even 1/16th the speed of FP32.
The vast majority of "real" HPC still requires FP64 (only large industry categorized under HPC that I know is perfectly fine with FP32 is geospatial oil & gas stuff)... a valid LINPACK has a hard FP64 requirement.
Seems like the missing bit is talking about how CERN could benefit from low precision? But it's certainly not in the article.