erek
[H]F Junkie
- Joined
- Dec 19, 2005
- Messages
- 9,574
Things are starting to slow down at the top
"The AMD CEO makes the case that it may be time to make heavier use of AI and machine learning in HPC. And she's not alone in thinking this. Nvidia and Intel have both been pushing the advantages of lower precision compute, particularly for machine learning where trading a few decimal places of accuracy can mean the difference between days and hours for training.
Nvidia has arguably been the most egregious, claiming systems capable of multiple "AI exaflops." What they conveniently leave out, or bury in the fine print, is the fact they're talking about FP16, FP8, or Int8 performance, not the FP64 calculations typically used in most HPC workloads.
"Just taking a look at the relative performance over the last 10 years, as much as we've improved in traditional metrics around SpecInt Rate or flops, the AI flops have improved much faster," the AMD chief said. "They've improved much faster because we've had all these mixed precision capabilities."
One of the first applications of AI/ML for HPC could be for what Su refers to as AI surrogate physics models. The general principle is that practitioners employ traditional HPC in a much more targeted way and use machine learning to help narrow the field and reduce the computational power required overall.
Several DoE labs are already exploring the use of AI/ML to improve everything from climate models and drug discovery to simulated nuclear weapons testing and maintenance.
"It's early. There is a lot of work to be done on the algorithms here, and there's a lot of work to be done in how to partition the problems," Su said."
Source: https://www.theregister.com/2023/02/23/amd_zettaflop_systems_nuclear/
"The AMD CEO makes the case that it may be time to make heavier use of AI and machine learning in HPC. And she's not alone in thinking this. Nvidia and Intel have both been pushing the advantages of lower precision compute, particularly for machine learning where trading a few decimal places of accuracy can mean the difference between days and hours for training.
Nvidia has arguably been the most egregious, claiming systems capable of multiple "AI exaflops." What they conveniently leave out, or bury in the fine print, is the fact they're talking about FP16, FP8, or Int8 performance, not the FP64 calculations typically used in most HPC workloads.
"Just taking a look at the relative performance over the last 10 years, as much as we've improved in traditional metrics around SpecInt Rate or flops, the AI flops have improved much faster," the AMD chief said. "They've improved much faster because we've had all these mixed precision capabilities."
One of the first applications of AI/ML for HPC could be for what Su refers to as AI surrogate physics models. The general principle is that practitioners employ traditional HPC in a much more targeted way and use machine learning to help narrow the field and reduce the computational power required overall.
Several DoE labs are already exploring the use of AI/ML to improve everything from climate models and drug discovery to simulated nuclear weapons testing and maintenance.
"It's early. There is a lot of work to be done on the algorithms here, and there's a lot of work to be done in how to partition the problems," Su said."
Source: https://www.theregister.com/2023/02/23/amd_zettaflop_systems_nuclear/