3 ms·
With HGX you can have all chips as one giant 'card' with unified memory. That's something you can't really DIY. Even with Quadro you can hook up two at a time v
by Keyframe 8y ago
With HGX you can have all chips as one giant 'card' with unified memory. That's something you can't really DIY. Even with Quadro you can hook up two at a time via NVLink and that's about it.
- lmeyerov 8y agoYep. This is a huge distinction for developers. Hodgepodge ("NUMA") systems can easily have the problem of "GPU 1 can quickly talk to GPU 2 but slowly to GPU 3", which is a productivity killer on code that is already > 10X harder to write to beginwith. GPU teams like ours (Graphistry) make simplifying assumptions on the hardware based on where things are going... and uniform memory access (within a cache hierarchy level) is one of them. In this case, we assumed saturating a single GPU is enough, and write kernels such that as single-node multi-GPU boxes become mainstream (like this), that it's in reach to use them. For initiatives like the GPU dataframe devs in the GOAI project, same thing. Today's office conversation: What will this look like for cloud multinode (e.g., rack)? :) AND... if you like frontend js or fullstack js, and think this stuff is cool, we're actively hiring :) See https://www.graphistry.com/blog/js-gpus-ml-arrow-goai https://www.graphistry.com/blog/js-gpus-ml-arrow-goai for our thoughts here.
- shaklee3 8y agoNot just that, but they showed at GTC that the access time to memory on a local card is very close to remote cards over nvlink for small transfer sizes, which is great.